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Get data from fedeo_ceda using EODAG

This tutorial will show you how to use EODAG to search and download data from the fedeo_ceda provider.

from eodag import EODataAccessGateway

dag = EODataAccessGateway()

List collections

list all the collections available from the fedeo_ceda provider.

dag.list_collections(provider="fedeo_ceda")

CollectionsList (319)

0  Collection("AATSR_ADV_L2_V2.31") id: 'AATSR_ADV_L2_V2.31', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ADV Algorithm), Version 2.31', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-adv-l2-v2.31,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on ENVISAT, derived using the ADV algorithm, version 2.31. Data is available for the period 2002-2012.For further details about these data products please see the linked documentation.',

1  Collection("AATSR_ADV_L3_V2.31") id: 'AATSR_ADV_L3_V2.31', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-adv-l3-v2.31,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31. Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation.',

2  Collection("AATSR_ENS_L2_V2.6") id: 'AATSR_ENS_L2_V2.6', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ensemble product), Version 2.6', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-ens-l2-v2.6,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. For further details about these data products please see the documentation.',

3  Collection("AATSR_ENS_L3_V2.6") id: 'AATSR_ENS_L3_V2.6', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6', instrument: 'AATSR,ATSR-2', platform: 'Envisat,ERS-2', keywords: 'aatsr,aatsr-ens-l3-v2.6,aerosol,atsr-2,cci,dif10,earth-science>atmosphere>aerosols,envisat,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite. A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.',

4  Collection("AATSR_ORAC_L2_V4.01") id: 'AATSR_ORAC_L2_V4.01', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ORAC Algorithm), Version 4.01', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-orac-l2-v4.01,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. For further details about these data products please see the linked documentation.',

5  Collection("AATSR_ORAC_L3_V4.01") id: 'AATSR_ORAC_L3_V4.01', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-orac-l3-v4.01,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orac,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation.',

6  Collection("AATSR_SU_L2_V4.3") id: 'AATSR_SU_L2_V4.3', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (SU Algorithm), Version 4.3', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-su-l2-v4.3,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the Swansea University (SU) algorithm, version 4.3. It covers the period from 2002 - 2012.For further details about these data products please see the linked documentation.',

7  Collection("AATSR_SU_L3_V4.3") id: 'AATSR_SU_L3_V4.3', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (SU algorithm), Version 4.3', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,aatsr-su-l3-v4.3,aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly aerosol products from the AATSR instrument on the ENVISAT satellite, using the Swansea University (SU) algorithm, version 4.3. Data is available for the period 2002 - 2012.For further details about these data products please see the documentation.',

8  Collection("ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V03.0") id: 'ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V03.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0', instrument: 'MODIS,MERIS,AVHRR-3,AVHRR-3,AVHRR-3,MODIS', platform: 'AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V', keywords: 'active-layer-thickness,active-layer-thickness-l4-area4-pp-v03.0,aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,envisat,meris,modis,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,proba-v,sar-x,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.',

9  Collection("ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V04.0") id: 'ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V04.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0', instrument: 'MODIS,MERIS,MODIS,AVHRR-3,AVHRR-3,AVHRR-3', platform: 'AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V', keywords: 'active-layer-thickness,active-layer-thickness-l4-area4-pp-v04.0,aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,envisat,meris,modis,modis-terra,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,proba-v,sar-x,spot,terra', license: 'other', abstract: 'This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.',

10  Collection("ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_ANTARCTICA") id: 'ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_ANTARCTICA', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for Antarctica, v5.0', instrument: 'MODIS,MODIS', platform: 'AQUA,TERRA', keywords: 'active-layer-thickness,active-layer-thickness-l4-area4-pp-v05.0-antarctica,aqua,cci,dif10,earth-science>agriculture>soils>permafrost,earth-science>land-surface>frozen-ground>permafrost,level-4,modis,orthoimagery,permafrost,terra', license: 'other', abstract: 'This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers Antarctica (south of 60°S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-ALT-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60°S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-ALT-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc',

11  Collection("ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE") id: 'ACTIVE_LAYER_THICKNESS_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v5.0', instrument: 'MODIS,MERIS,C-SAR,MSI,MODIS', platform: 'AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V', keywords: 'active-layer-thickness,active-layer-thickness-l4-area4-pp-v05.0-northern-hemisphere,aqua,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,earth-science>land-surface>frozen-ground>permafrost,envisat,level-4,meris,modis,msi,msi-(sentinel-2),orthoimagery,permafrost,proba-v,sar-c-(sentinel-1),sentinel-1a,sentinel-2,sentinel-2-msi,sentinel-2a,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30°N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-ALT-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30°N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-ALT-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc',

12  Collection("AGB_MAPS_V2.0") id: 'AGB_MAPS_V2.0', title: 'ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2', instrument: 'PALSAR-2,PALSAR,ASAR,C-SAR,C-SAR,P-SAR', platform: 'ALOS-2,ALOS-1,Envisat,Sentinel-1A,Sentinel-1B,Biomass', keywords: 'agb-maps-v2.0,alos,alos-1,alos-2,asar,biomass,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>biosphere>vegetation>biomass,earth-science>spectral/engineering>radar,envisat,esa,orthoimagery,p-sar,palsar,palsar-2,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes.',

13  Collection("AGB_MAPS_V3.0") id: 'AGB_MAPS_V3.0', title: 'ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3', instrument: 'P-SAR', platform: 'Biomass', keywords: 'agb-maps-v3.0,biomass,cci,dif10,earth-science>biosphere>vegetation>biomass,esa,orthoimagery,p-sar', license: 'other', abstract: 'This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.',

14  Collection("AGB_MAPS_V4.0") id: 'AGB_MAPS_V4.0', title: 'ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4', instrument: 'P-SAR', platform: 'Biomass', keywords: 'agb-maps-v4.0,biomass,cci,dif10,earth-science>biosphere>vegetation>biomass,esa,orthoimagery,p-sar', license: 'other', abstract: 'This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.',

15  Collection("AGB_MAPS_V5.01") id: 'AGB_MAPS_V5.01', title: 'ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01', instrument: 'P-SAR', platform: 'Biomass', keywords: 'agb-maps-v5.01,biomass,cci,dif10,earth-science>biosphere>vegetation>biomass,esa,orthoimagery,p-sar', license: 'other', abstract: 'This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude.',

16  Collection("AGB_MAPS_V6.0") id: 'AGB_MAPS_V6.0', title: 'ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022, v6.0', instrument: 'P-SAR', platform: 'Biomass', keywords: 'agb-maps-v6.0,biomass,cci,dif10,earth-science>biosphere>vegetation>biomass,esa,orthoimagery,p-sar', license: 'other', abstract: 'This dataset comprises estimates of forest above-ground biomass (AGB) for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.This release of the data is version 6. Compared to version 5, version 6 consists of an update of the maps of AGB for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps for 2007 and 2022. AGB change maps have been created for consecutive years (e.g., 2020-2019), for a decadal interval (2020-2010) as well as for the interval 2010-2007. The pool of remote sensing data includes multi-temporal observations at L-band for all biomes and for all years and extended ICESat-2 observations to calibrate retrieval models. A cost function that preserves the temporal features as expressed in the remote sensing data has been refined to limit biases between the 2007-2010 and the 2015+ maps.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots per unit area2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2016-2015, 2017-2016, 2018-2017, 2019-2018, 2020-2019, 2021-2020, 2022-2021), over a decade (2020-2010) and over 2010-2007 are provided. Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.',

17  Collection("AQUA_MODIS_L3C_0.01_V3.00_DAILY") id: 'AQUA_MODIS_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00', keywords: 'aqua-modis-l3c-0.01-v3.00-daily,not-defined,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

18  Collection("AQUA_MODIS_L3C_0.01_V3.00_MONTHLY") id: 'AQUA_MODIS_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00', keywords: 'aqua-modis-l3c-0.01-v3.00-monthly,cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

19  Collection("ARCTIC_MSLA_20161024") id: 'ARCTIC_MSLA_20161024', title: 'ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML)', keywords: 'arctic-msla-20161024,cci,earth-science>oceans>sea-surface-topography>sea-surface-height,esa,orthoimagery,sea-level', license: 'other', abstract: 'This dataset contains estimations of Arctic sea level anomalies produced by the ESA Sea Level Climate Change Initiative project (Sea_level_cci), based on satellite altimetry from the ENVISAT and SARAL/Altika satellites. It has been produced by Collecte Localisation Satellites (CLS) and the Plymouth Marine Laboratory (PML).The retrieval of sea level in the Arctic sea ice covered region requires specific processing steps of the satellite altimetry measurements. For this dataset, a specific radar waveform classification method has been applied based on a neural network approach, and the waveform retracking is based on a new adaptive retracking that is able to process both open ocean and peaky echoes measured in leads without introducing any bias between the two types of surfaces. Editing and mapping processing steps have been optimized for this dataset',

20  Collection("ATSR2_ADV_L2_V2.31") id: 'ATSR2_ADV_L2_V2.31', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ADV algorithm), Version 2.31', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aerosol,atsr-2,atsr2-adv-l2-v2.31,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. Data are available for the period 1995-2002.For further details about these data products please see the linked documentation.',

21  Collection("ATSR2_ADV_L3_V2.31") id: 'ATSR2_ADV_L3_V2.31', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ADV algorithm), Version 2.31', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aerosol,atsr-2,atsr2,atsr2-adv-l3-v2.31,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. It covers the period from 1995-2003.For further details about these data products please see the linked documentation.',

22  Collection("ATSR2_ENS_L2_V2.6") id: 'ATSR2_ENS_L2_V2.6', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ensemble product), Version 2.6', instrument: 'AATSR,ATSR-2', platform: 'Envisat,ERS-2', keywords: 'aatsr,aerosol,atsr-2,atsr2-ens-l2-v2.6,cci,dif10,earth-science>atmosphere>aerosols,envisat,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. For further details about these data products please see the documentation.',

23  Collection("ATSR2_ENS_L3_V2.6") id: 'ATSR2_ENS_L3_V2.6', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6', instrument: 'AATSR,ATSR-2', platform: 'Envisat,ERS-2', keywords: 'aatsr,aerosol,atsr-2,atsr2-ens-l3-v2.6,cci,dif10,earth-science>atmosphere>aerosols,envisat,ers-2,esa,orthoimagery', license: 'other', abstract: 'In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002. In 2002, it also contains data from the AATSR instrument on the ENVISAT satellite. A separate AATSR product covering the period 2002-2012 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.',

24  Collection("ATSR2_ORAC_L2_V4.01") id: 'ATSR2_ORAC_L2_V4.01', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aatsr,aerosol,atsr-2,atsr2-orac-l2-v4.01,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ORAC algorithm, version 4.01. It covers the period from 1995-2003For further details about these data products please see the linked documentation.',

25  Collection("ATSR2_ORAC_L3_V4.01") id: 'ATSR2_ORAC_L3_V4.01', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ORAC algorithm), Version 4.01', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aerosol,atsr-2,atsr2-orac-l3-v4.01,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ENVISAT satellite, derived using the ORAC algorithm, version 4.01. The data covers the period from 1995 - 2003.For further details about these data products please see the linked documentation.',

26  Collection("ATSR2_SU_L2_V4.3") id: 'ATSR2_SU_L2_V4.3', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from ATSR-2 (SU algorithm), Version 4.3', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aerosol,atsr-2,atsr2-su-l2-v4.3,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the Swansea University (SU) algorithm, version 4.3. Data are available for the period 1995-2003.For further details about these data products please see the documentation.',

27  Collection("ATSR2_SU_L3_V4.3") id: 'ATSR2_SU_L3_V4.3', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (SU algorithm), Version 4.3', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'aerosol,atsr-2,atsr2-su-l3-v4.3,cci,dif10,earth-science>atmosphere>aerosols,ers-2,esa,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 daily and monthly aerosol products from the ATSR-2 instrument on the ERS-2 satellite, using the Swansea University (SU) algorithm, version 4.3. Data cover the period 1995 - 2003.For further details about these data products please see the documentation.',

28  Collection("BURNED_AREA_AVHRR-LTDR_GRID_V1.1") id: 'BURNED_AREA_AVHRR-LTDR_GRID_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1', instrument: 'AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,burned-area,burned-area-avhrr-ltdr-grid-v1.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,earth-science>spectral/engineering>infrared-wavelengths,esa,fire,fire-disturbance,gcos-essential-climate-variable,noaa-11,noaa-14,noaa-16,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,pixel', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide.',

29  Collection("BURNED_AREA_AVHRR-LTDR_PIXEL_V1.1") id: 'BURNED_AREA_AVHRR-LTDR_PIXEL_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Pixel product, version 1.1', instrument: 'AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,burned-area,burned-area-avhrr-ltdr-pixel-v1.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,earth-science>spectral/engineering>infrared-wavelengths,esa,fire,fire-disturbance,gcos-essential-climate-variable,noaa-11,noaa-14,noaa-16,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,pixel', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Pixel v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.The dataset provides monthly information on global burned area at 0.05-degree spatial resolution (the resolution of the AVHRR-LTDR input data) from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in monthly GeoTIFF files, packed in annual tar.gz files, and it includes 5 files: date of BA detection (labelled JD), confidence label (CL), burned area in each pixel (BA), number of observations in the month (OB) and a metadata file. For further information on the product and its format see the Product User Guide.',

30  Collection("BURNED_AREA_MODIS_GRID_V5.1") id: 'BURNED_AREA_MODIS_GRID_V5.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.1', instrument: 'MODIS', platform: 'TERRA', keywords: 'burned-area,burned-area-modis-grid-v5.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,eos,esa,fire,fire-disturbance,gcos-essential-climate-variable,grid,level-4,moderate-resolution-imaging-spectroradiometer,modis,modis-terra,month,orthoimagery,terra,university-of-alcala', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The MODIS Fire_cci v5.1 grid product described here contains gridded data on global burned area derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2019. This product supercedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022. This gridded dataset has been derived from the MODIS Fire_cci v5.1 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v2.0.7 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.',

31  Collection("BURNED_AREA_MODIS_PIXEL_V5.1") id: 'BURNED_AREA_MODIS_PIXEL_V5.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1', instrument: 'MODIS', platform: 'TERRA', keywords: 'burned-area,burned-area-modis-pixel-v5.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire,fire-disturbance,gcos,modis,modis-terra,orthoimagery,pixel,terra', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022.The Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.Files are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation.',

32  Collection("BURNED_AREA_SENTINEL3_SYN_GRID_V1.1") id: 'BURNED_AREA_SENTINEL3_SYN_GRID_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.1', keywords: 'burned-area,burned-area-sentinel3-syn-grid-v1.1,cci,climate-change,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire-disturbance,gcos,grid,orthoimagery', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2022.This gridded dataset has been derived from the FireCCIS311 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative (C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation.',

33  Collection("BURNED_AREA_SENTINEL3_SYN_PIXEL_V1.1") id: 'BURNED_AREA_SENTINEL3_SYN_PIXEL_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.1', instrument: 'OLCI,OLCI', platform: 'Sentinel-3A,Sentinel-3B', keywords: 'burned-area,burned-area-sentinel3-syn-pixel-v1.1,cci,climate-change,dif10,earth-science>atmosphere,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire,fire-disturbance,gcos,level-3s,olci,orthoimagery,pixel,sentinel-3a,sentinel-3b', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2022.The FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation.',

34  Collection("BURNED_AREA_SFDL_V1.0_PIXEL") id: 'BURNED_AREA_SFDL_V1.0_PIXEL', title: 'ESA Fire Climate Change Initiative (Fire_cci): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia, Africa and Siberia, version 1.0', keywords: 'burned-area,burned-area-sfdl-v1.0-pixel,cci,climate-change,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire-disturbance,orthoimagery,pixel', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project aims to generate burned area developed from satellite observations. The Long-Term Small Fire Dataset (SFDL) pixel products have been obtained using spectral information from Landsat sensors for three study areas located in different parts of the world (Amazon, Sahel and Siberia), and coinciding with the ESA CCI High Resolution Land Cover product.The dataset uses surface reflectance information from the Landsat-4 and Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI sensors, and covers the period 1990 to 2019, with a spatial resolution of 0.00025 degrees (approximately 30 m at the Equator).',

35  Collection("BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V1.1") id: 'BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 1.1', instrument: 'MSI', platform: 'Sentinel-2', keywords: 'burned-area,burned-area-sfd-africa-sentinel2-grid-v1.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire,fire-disturbance,gcos-essential-climate-variable,grid,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v1.1 (also available), which covers Sub-Saharan Africa for the year 2016, by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution.',

36  Collection("BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V2.0") id: 'BURNED_AREA_SFD_AFRICA_SENTINEL2_GRID_V2.0', title: 'ESA Fire Climate Change Initiative (Fire_cci): Small Fire Database (SFD) Burned Area grid product for Sub-Saharan Africa, version 2.0', keywords: 'burned-area,burned-area-sfd-africa-sentinel2-grid-v2.0,cci,climate-change,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire-disturbance,gcos-essential-climate-variable,grid,orthoimagery', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Database (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.This gridded dataset has been derived from the Small Fire Database (SFD) Burned Area pixel product for Sub-Saharan Africa, v2.0 (also available), which covers Sub-Saharan Africa for the year 2019, by summarising its burned area information into a regular grid covering the Earth at 0.05 x 0.05 degrees resolution and at monthly temporal resolution.',

37  Collection("BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V1.1") id: 'BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V1.1', title: 'ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1', instrument: 'MSI', platform: 'Sentinel-2', keywords: 'burned-area,burned-area-sfd-africa-sentinel2-pixel-v1.1,cci,climate-change,dif10,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>environmental-governance/management>fire-management,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire,fire-disturbance,gcos,msi,msi-(sentinel-2),orthoimagery,pixel,sentinel-2,sentinel-2-msi,sentinel-2a', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset.',

38  Collection("BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V2.0") id: 'BURNED_AREA_SFD_AFRICA_SENTINEL2_PIXEL_V2.0', title: 'ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 2.0', keywords: 'burned-area,burned-area-sfd-africa-sentinel2-pixel-v2.0,cci,climate-change,earth-science>biosphere>ecological-dynamics>fire-ecology>fire-disturbance,earth-science>human-dimensions>natural-hazards>wildfires>burned-area,esa,fire-disturbance,gcos-essential-climate-variable,orthoimagery,pixel', license: 'other', abstract: 'The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.This dataset is part of v2.0 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2019. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset.',

39  Collection("CCI_PLUS_CH4_GO2_SRFP_V1.0") id: 'CCI_PLUS_CH4_GO2_SRFP_V1.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm, version 1.0.0', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-ch4-go2-srfp-v1.0,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

40  Collection("CCI_PLUS_CH4_GO2_SRFP_V2.0.2") id: 'CCI_PLUS_CH4_GO2_SRFP_V2.0.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.2', keywords: 'atmosphere,cci,cci-plus-ch4-go2-srfp-v2.0.2,ch4,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

41  Collection("CCI_PLUS_CH4_GO2_SRFP_V2.0.3") id: 'CCI_PLUS_CH4_GO2_SRFP_V2.0.3', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.3', keywords: 'atmosphere,cci,cci-plus-ch4-go2-srfp-v2.0.3,ch4,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

42  Collection("CCI_PLUS_CH4_GO2_SRPR_V1.0") id: 'CCI_PLUS_CH4_GO2_SRPR_V1.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm, version 1.0.0', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-ch4-go2-srpr-v1.0,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2(TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRPR Proxy Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

43  Collection("CCI_PLUS_CH4_GO2_SRPR_V2.0.2") id: 'CCI_PLUS_CH4_GO2_SRPR_V2.0.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.2', keywords: 'atmosphere,cci,cci-plus-ch4-go2-srpr-v2.0.2,ch4,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

44  Collection("CCI_PLUS_CH4_GO2_SRPR_V2.0.3") id: 'CCI_PLUS_CH4_GO2_SRPR_V2.0.3', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.3', keywords: 'atmosphere,cci,cci-plus-ch4-go2-srpr-v2.0.3,ch4,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

45  Collection("CCI_PLUS_CH4_S5P_WFMD_V1.8") id: 'CCI_PLUS_CH4_S5P_WFMD_V1.8', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - October 2023', instrument: 'TROPOMI', platform: 'Sentinel-5P', keywords: 'atmosphere,carbon-monoxide,cci,cci-plus-ch4-s5p-wfmd-v1.8,dif10,earth-science>atmosphere,earth-science>atmosphere>air-quality>carbon-monoxide,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite,sentinel-5-precursor,sentinel-5p,tropomi', license: 'other', abstract: 'This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023.',

46  Collection("CCI_PLUS_CH4_S5P_WFMD_V1.8_EXTENDED_JUNE2024") id: 'CCI_PLUS_CH4_S5P_WFMD_V1.8_EXTENDED_JUNE2024', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - June 2024', instrument: 'TROPOMI', platform: 'Sentinel-5P', keywords: 'atmosphere,carbon-monoxide,cci,cci-plus-ch4-s5p-wfmd-v1.8-extended-june2024,dif10,earth-science>atmosphere,earth-science>atmosphere>air-quality>carbon-monoxide,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,methane,orthoimagery,satellite,sentinel-5-precursor,sentinel-5p,tropomi', license: 'other', abstract: 'This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - June 2024. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023.',

47  Collection("CCI_PLUS_CO2_GO2_SRFP_V1.0") id: 'CCI_PLUS_CO2_GO2_SRFP_V1.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm, version 1.0.0', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-go2-srfp-v1.0,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared(NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the RemoTeC SRFP Full Physics Retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

48  Collection("CCI_PLUS_CO2_GO2_SRFP_V2.0.2") id: 'CCI_PLUS_CO2_GO2_SRFP_V2.0.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.2', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-go2-srfp-v2.0.2,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

49  Collection("CCI_PLUS_CO2_GO2_SRFP_V2.0.3") id: 'CCI_PLUS_CO2_GO2_SRFP_V2.0.3', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.3', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-go2-srfp-v2.0.3,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.',

50  Collection("CCI_PLUS_CO2_OC2_FOCA_V10.1") id: 'CCI_PLUS_CO2_OC2_FOCA_V10.1', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10.1', keywords: 'carbon-dioxide,cci,cci-plus-co2-oc2-foca-v10.1,co2,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.This version of the data (v10.1) was produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)and got co-funding from the University of Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).When citing this data, please also cite the following peer-reviewed publications:M.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017M.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017',

51  Collection("CCI_PLUS_CO2_OC2_FOCA_V11.0") id: 'CCI_PLUS_CO2_OC2_FOCA_V11.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 11.0', keywords: 'carbon-dioxide,cci,cci-plus-co2-oc2-foca-v11.0,co2,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite', license: 'other', abstract: 'This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2) data, generated using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.This version of the data (v11) was produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg).The FOCAL OCO-2 XCO2 retrieval development, data processing and analysis has received co-funding from ESA’s Climate Change Initiative (CCI+) via project GHG-CCI+ (contract 4000126450/19/I-NB, https://climate.esa.int/en/projects/ghgs), EUMETSAT via the FOCAL-CO2M study (contract EUM/CO/19/4600002372/RL), the European Union via the Horizon 2020 (H2020) projects VERIFY (Grant Agreement No. 776810, http://verify.lsce.ipsl.fr) and CHE (Grant Agreement No. 776186, https://www.che-project.eu), and by the State and the University of Bremen.When citing this data, please also cite the following peer-reviewed publications:M.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017M.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017',

52  Collection("CCI_PLUS_CO2_TAN_OCFP_V1.0") id: 'CCI_PLUS_CO2_TAN_OCFP_V1.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-tan-ocfp-v1.0,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,greenhouse-gases,orthoimagery,satellite,tansat', license: 'other', abstract: 'This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only. A full global dataset is in production. For further information on the dataset, please see the linked documentation.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).',

53  Collection("CCI_PLUS_CO2_TAN_OCFP_V1.0_GLOBAL_LAND") id: 'CCI_PLUS_CO2_TAN_OCFP_V1.0_GLOBAL_LAND', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-tan-ocfp-v1.0-global-land,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,greenhouse-gases,orthoimagery,satellite,tansat', license: 'other', abstract: 'This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.Initially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6. Additional time periods will be added in the future.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).',

54  Collection("CCI_PLUS_CO2_TAN_OCFP_V1.2") id: 'CCI_PLUS_CO2_TAN_OCFP_V1.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.2', keywords: 'atmosphere,carbon-dioxide,cci,cci-plus-co2-tan-ocfp-v1.2,co2,earth-science>atmosphere,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,orthoimagery,satellite,tansat', license: 'other', abstract: 'This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.Initially this dataset contains data from the period from March 2017 to May 2018, delivered as part of the GHG_cci Climate Research Data Package 7. Additional time periods may be delivered in the future.This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).',

55  Collection("CDR_V2_ANALYSIS_L4_V2.1") id: 'CDR_V2_ANALYSIS_L4_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1', instrument: 'AATSR,ATSR-1,ATSR-2,AVHRR-3,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'Envisat,ERS-1,ERS-2,Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'aatsr,atsr,atsr-1,atsr-2,avhrr-2,avhrr-3,cdr-v2-analysis-l4-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,envisat,ers-1,ers-2,esa-climate-change-initiative,metop-a,noaa-11,noaa-12,noaa-14,noaa-15,noaa-16,noaa-17,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,sea-surface-temperature,sst', license: 'other', abstract: 'This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.The CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

56  Collection("CDR_V2_ATSR_L2P_V2.1") id: 'CDR_V2_ATSR_L2P_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1', instrument: 'AATSR,ATSR-1,ATSR-2', platform: 'Envisat,ERS-1,ERS-2', keywords: 'aatsr,atsr,atsr-1,atsr-2,cdr-v2-atsr-l2p-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,envisat,ers-1,ers-2,esa-climate-change-initiative,orthoimagery,sea-surface-temperature,sst', license: 'other', abstract: 'This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SST's to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

57  Collection("CDR_V2_ATSR_L3C_V2.1") id: 'CDR_V2_ATSR_L3C_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record, version 2.1', instrument: 'AATSR,ATSR-1,ATSR-2', platform: 'Envisat,ERS-1,ERS-2', keywords: 'aatsr,atsr,atsr-1,atsr-2,cci,cdr-v2-atsr-l3c-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,envisat,ers-1,ers-2,esa-climate-change-initiative,esacci-sst,orthoimagery,sst', license: 'other', abstract: 'This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

58  Collection("CDR_V2_ATSR_L3U_V2.1") id: 'CDR_V2_ATSR_L3U_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1', instrument: 'AATSR,ATSR-1,ATSR-2', platform: 'Envisat,ERS-1,ERS-2', keywords: 'aatsr,atsr,atsr-1,atsr-2,cci,cdr-v2-atsr-l3u-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,envisat,ers-1,ers-2,esa-climate-change-initiative,esacci-sst,orthoimagery,sst', license: 'other', abstract: 'This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. The L3U products provide these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 and the Long Term product v1.1. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

59  Collection("CDR_V2_AVHRR_L2P_V2.1") id: 'CDR_V2_AVHRR_L2P_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1', instrument: 'AVHRR-3,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,cdr-v2-avhrr-l2p-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,esa-climate-change-initiative,metop-a,noaa-11,noaa-12,noaa-14,noaa-15,noaa-16,noaa-17,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,sst', license: 'other', abstract: 'This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

60  Collection("CDR_V2_AVHRR_L3C_V2.1") id: 'CDR_V2_AVHRR_L3C_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1', instrument: 'AVHRR-3,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,cdr-v2-avhrr-l3c-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,esa-climate-change-initiative,metop-a,noaa-11,noaa-12,noaa-14,noaa-15,noaa-16,noaa-17,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,sst', license: 'other', abstract: 'This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

61  Collection("CDR_V2_AVHRR_L3U_V2.1") id: 'CDR_V2_AVHRR_L3U_V2.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1', instrument: 'AVHRR-3,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'Metop-A,NOAA-11,NOAA-12,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,cci,cdr-v2-avhrr-l3u-v2.1,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,esa-climate-change-initiative,metop-a,noaa-11,noaa-12,noaa-14,noaa-15,noaa-16,noaa-17,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,sea-surface-temperature,sst', license: 'other', abstract: 'This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

62  Collection("CDR_V2_CLIMATOLOGY_L4_V2.2") id: 'CDR_V2_CLIMATOLOGY_L4_V2.2', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.2', keywords: 'cci,cdr-v2-climatology-l4-v2.2,earth-science>oceans>ocean-temperature>sea-surface-temperature,esa-climate-change-initiative,orthoimagery,sst', license: 'other', abstract: 'This v2.2 SST_cci Climatology Data Record (CDR) consists of daily climatological mean sea surface temperature on a global 0.05 degree latitude-longitude grid, derived from the SST CCI analysis data for the period 1982 to 2010 (29 years). This climatology includes the post-hoc dust corrections from Merchant and Embury (2020) https://doi.org/10.3390/rs12162554.The changes from climatology v2.1 are:* Inclusion of post-hoc dust corrections from Merchant and Embury (2020) reduces biases in affected regions (tropical Atlantic Ocean and the Mediterranean, Red, and Arabian Seas).* Improved compliance with CF Conventions.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. (2019) Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223. http://doi.org/10.1038/s41597-019-0236-x',

63  Collection("CRDP_4_EMMA_CH4_V1.2") id: 'CRDP_4_EMMA_CH4_V1.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-emma-ch4-v1.2,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,emma,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,institute-of-environmental-physics,level-2,orthoimagery,satellite-orbit-frequency,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer', license: 'other', abstract: 'The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).',

64  Collection("CRDP_4_EMMA_CO2_V2.2") id: 'CRDP_4_EMMA_CO2_V2.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2', instrument: 'SCIAMACHY,TANSO-FTS', platform: 'Envisat,GOSAT-1', keywords: 'cci,co2,column-averaged-dry-air-mole-fraction-of-co2,crdp-4-emma-co2-v2.2,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,emma,environmental-satellite,envisat,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,institute-of-environmental-physics,level-2,merged,orthoimagery,satellite-orbit-frequency,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer', license: 'other', abstract: 'The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4.The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).',

65  Collection("CRDP_4_GOSAT_CH4_GOS_OCFP_V2.1") id: 'CRDP_4_GOSAT_CH4_GOS_OCFP_V2.1', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-gosat-ch4-gos-ocfp-v2.1,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,ocfp,orthoimagery,satellite-orbit-frequency,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer,university-of-leicester', license: 'other', abstract: 'The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4.The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).',

66  Collection("CRDP_4_GOSAT_CH4_GOS_OCPR_V7.0") id: 'CRDP_4_GOSAT_CH4_GOS_OCPR_V7.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-gosat-ch4-gos-ocpr-v7.0,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,ocpr,orthoimagery,proxy,satellite-orbit-frequency,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer,university-of-leicester', license: 'other', abstract: 'This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4.This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.',

67  Collection("CRDP_4_GOSAT_CH4_GOS_SRFP_V2.3.8") id: 'CRDP_4_GOSAT_CH4_GOS_SRFP_V2.3.8', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-gosat-ch4-gos-srfp-v2.3.8,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,fp,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,netherlands-institute-for-space-research,orthoimagery,satellite-orbit-frequency,srfp,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer', license: 'other', abstract: 'The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document.',

68  Collection("CRDP_4_GOSAT_CH4_GOS_SRPR_V2.3.8") id: 'CRDP_4_GOSAT_CH4_GOS_SRPR_V2.3.8', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-gosat-ch4-gos-srpr-v2.3.8,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,netherlands-institute-for-space-research,orthoimagery,proxy,remotec,satellite-orbit-frequency,srpr,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer', license: 'other', abstract: 'The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.',

69  Collection("CRDP_4_GOSAT_CO2_GOS_OCFP_V7.0") id: 'CRDP_4_GOSAT_CO2_GOS_OCFP_V7.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,co2,column-averaged-dry-air-mole-fraction-of-co2,crdp-4-gosat-co2-gos-ocfp-v7.0,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,ocfp,orthoimagery,satellite-orbit-frequency,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer,university-of-leicester', license: 'other', abstract: 'The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).',

70  Collection("CRDP_4_GOSAT_CO2_GOS_SRFP_V2.3.8") id: 'CRDP_4_GOSAT_CO2_GOS_SRFP_V2.3.8', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8', instrument: 'TANSO-FTS', platform: 'GOSAT-1', keywords: 'cci,co2,column-averaged-dry-air-mole-fraction-of-co2,crdp-4-gosat-co2-gos-srfp-v2.3.8,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,esa,ghg,gosat,gosat-1,gosat-programme,greenhouse-gases,level-2,netherlands-institute-for-space-research,orthoimagery,satellite-orbit-frequency,srfp,tanso-fts,thermal-and-near-infrared-sensor-for-carbon-observation---fourier-transform-spectrometer', license: 'other', abstract: 'The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.',

71  Collection("CRDP_4_SCIAMACHY_CH4_SCI_IMAP_V7.2") id: 'CRDP_4_SCIAMACHY_CH4_SCI_IMAP_V7.2', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2', instrument: 'SCIAMACHY', platform: 'Envisat', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-sciamachy-ch4-sci-imap-v7.2,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,environmental-satellite,envisat,esa,ghg,greenhouse-gases,imap,level-2,netherlands-institute-for-space-research,orthoimagery,satellite-orbit-frequency,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy', license: 'other', abstract: 'The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4.The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.',

72  Collection("CRDP_4_SCIAMACHY_CH4_SCI_WFMD_V4.0") id: 'CRDP_4_SCIAMACHY_CH4_SCI_WFMD_V4.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0', instrument: 'SCIAMACHY', platform: 'Envisat', keywords: 'cci,ch4,column-averaged-dry-air-mole-fraction-of-ch4,crdp-4-sciamachy-ch4-sci-wfmd-v4.0,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,environmental-satellite,envisat,esa,ghg,greenhouse-gases,level-2,orthoimagery,satellite-orbit-frequency,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy,university-of-bremen,wfmd', license: 'other', abstract: 'The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.The Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. The data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. For further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.',

73  Collection("CRDP_4_SCIAMACHY_CO2_SCI_BESD_V02.01.02") id: 'CRDP_4_SCIAMACHY_CO2_SCI_BESD_V02.01.02', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02', instrument: 'SCIAMACHY', platform: 'Envisat', keywords: 'besd,cci,co2,column-averaged-dry-air-mole-fraction-of-co2,crdp-4-sciamachy-co2-sci-besd-v02.01.02,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,environmental-satellite,envisat,esa,ghg,greenhouse-gases,institute-of-environmental-physics,level-2,orthoimagery,satellite-orbit-frequency,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy', license: 'other', abstract: 'The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.',

74  Collection("CRDP_4_SCIAMACHY_CO2_SCI_WFMD_V4.0") id: 'CRDP_4_SCIAMACHY_CO2_SCI_WFMD_V4.0', title: 'ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0', instrument: 'SCIAMACHY', platform: 'Envisat', keywords: 'cci,co2,column-averaged-dry-air-mole-fraction-of-co2,crdp-4-sciamachy-co2-sci-wfmd-v4.0,dif10,earth-science>atmosphere>atmospheric-chemistry,earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>atmospheric-carbon-dioxide,environmental-satellite,envisat,esa,ghg,greenhouse-gases,level-2,orthoimagery,satellite-orbit-frequency,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy,university-of-bremen,wfmd', license: 'other', abstract: 'The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.',

75  Collection("DAILY_FILES_ACTIVE_V05.2") id: 'DAILY_FILES_ACTIVE_V05.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2', instrument: 'AMI/Scatterometer,AMI/Scatterometer,ASCAT,ASCAT', platform: 'ERS-1,ERS-2,Metop-A,Metop-B', keywords: 'active,ami-scat,ami/scatterometer,ascat,cci,daily-files-active-v05.2,dif10,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,metop-a,metop-b,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

76  Collection("DAILY_FILES_ACTIVE_V05.3") id: 'DAILY_FILES_ACTIVE_V05.3', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.3', instrument: 'AMI/Scatterometer,AMI/Scatterometer,ASCAT,ASCAT', platform: 'ERS-1,ERS-2,Metop-A,Metop-B', keywords: 'active,ami-scat,ami/scatterometer,ascat,cci,daily-files-active-v05.3,dif10,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,metop-a,metop-b,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.3 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

77  Collection("DAILY_FILES_ACTIVE_V06.1") id: 'DAILY_FILES_ACTIVE_V06.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1', instrument: 'AMI/Scatterometer,AMI/Scatterometer,ASCAT,ASCAT', platform: 'ERS-1,ERS-2,Metop-A,Metop-B', keywords: 'active,ami-scat,ami/scatterometer,ascat,cci,daily-files-active-v06.1,dif10,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,metop-a,metop-b,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

78  Collection("DAILY_FILES_ACTIVE_V06.2") id: 'DAILY_FILES_ACTIVE_V06.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.2', keywords: 'active,cci,daily-files-active-v06.2,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

79  Collection("DAILY_FILES_ACTIVE_V07.1") id: 'DAILY_FILES_ACTIVE_V07.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 07.1', keywords: 'active,cci,daily-files-active-v07.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v07.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

80  Collection("DAILY_FILES_ACTIVE_V08.1") id: 'DAILY_FILES_ACTIVE_V08.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 08.1', keywords: 'active,cci,daily-files-active-v08.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v08.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

81  Collection("DAILY_FILES_ACTIVE_V09.1") id: 'DAILY_FILES_ACTIVE_V09.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.1', keywords: 'active,cci,daily-files-active-v09.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v09.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

82  Collection("DAILY_FILES_BREAK_ADJUSTED_COMBINED_V06.1") id: 'DAILY_FILES_BREAK_ADJUSTED_COMBINED_V06.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,SSM/I,AMI/Scatterometer,AMI/Scatterometer,AMSR2,ASCAT,ASCAT,SMMR,MIRAS,TMI', platform: 'FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP', keywords: 'ami-scat,ami/scatterometer,amsr-e,amsr2,amsre,aqua,ascat,cci,combined,coriolis,daily-files-break-adjusted-combined-v06.1,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,dmsp-f13,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>microwave,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,fy-3b,gcom-w1,gmi,metop-a,metop-b,miras,nimbus-7,orthoimagery,smap,smmr,smos,soil-moisture,ssm/i,tmi,trmm,virr,windsat', license: 'other', abstract: 'An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

83  Collection("DAILY_FILES_BREAK_ADJUSTED_COMBINED_V07.1") id: 'DAILY_FILES_BREAK_ADJUSTED_COMBINED_V07.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 07.1', keywords: 'cci,combined,daily-files-break-adjusted-combined-v07.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for their v07.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v07.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v07.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

84  Collection("DAILY_FILES_COMBINED_V05.2") id: 'DAILY_FILES_COMBINED_V05.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,SSM/I,AMI/Scatterometer,AMI/Scatterometer,AMSR2,ASCAT,ASCAT,SMMR,MIRAS,TMI', platform: 'AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP', keywords: 'ami-scat,ami/scatterometer,amsr-e,amsr2,amsre,aqua,ascat,cci,combined,coriolis,daily-files-combined-v05.2,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,dmsp-f13,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture,earth-science>spectral/engineering>microwave,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,gcom-w1,metop-a,metop-b,miras,nimbus-7,orthoimagery,smap,smmr,smos,soil-moisture,ssm/i,tmi,trmm,windsat', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

85  Collection("DAILY_FILES_COMBINED_V05.3") id: 'DAILY_FILES_COMBINED_V05.3', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3', instrument: 'WINDSAT,SSM/I,SSM/I,AMI/Scatterometer,AMI/Scatterometer,AMSR2,ASCAT,ASCAT,SMMR,TMI', platform: 'FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,TRMM,SMAP', keywords: 'ami-scat,ami/scatterometer,amsr2,aqua,ascat,cci,combined,coriolis,daily-files-combined-v05.3,dif10,dmsp-5d-2/f11,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>microwave,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,fy-3b,gcom-w1,metop-a,metop-b,miras,nimbus-7,orthoimagery,smap,smmr,soil-moisture,ssm/i,tmi,trmm,windsat', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.3 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

86  Collection("DAILY_FILES_COMBINED_V06.1") id: 'DAILY_FILES_COMBINED_V06.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,SSM/I,AMI/Scatterometer,AMI/Scatterometer,AMSR2,ASCAT,ASCAT,SMMR,MIRAS,TMI', platform: 'FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,ERS-1,ERS-2,GCOM-W1,Metop-A,Metop-B,Nimbus-7,SMOS,TRMM,SMAP', keywords: 'ami-scat,ami/scatterometer,amsr-e,amsr2,amsre,aqua,ascat,cci,combined,coriolis,daily-files-combined-v06.1,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,dmsp-f13,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>microwave,earth-science>spectral/engineering>radar,ers-1,ers-2,ers-wind-scatterometer,esa,fy-3b,gcom-w1,gmi,metop-a,metop-b,miras,nimbus-7,orthoimagery,smap,smmr,smos,soil-moisture,ssm/i,tmi,trmm,virr,windsat', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

87  Collection("DAILY_FILES_COMBINED_V06.2") id: 'DAILY_FILES_COMBINED_V06.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.2', keywords: 'cci,combined,daily-files-combined-v06.2,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

88  Collection("DAILY_FILES_COMBINED_V07.1") id: 'DAILY_FILES_COMBINED_V07.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 07.1', keywords: 'cci,combined,daily-files-combined-v07.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v07.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

89  Collection("DAILY_FILES_COMBINED_V08.1") id: 'DAILY_FILES_COMBINED_V08.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 08.1', keywords: 'cci,combined,daily-files-combined-v08.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v08.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

90  Collection("DAILY_FILES_COMBINED_V09.1") id: 'DAILY_FILES_COMBINED_V09.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.1', keywords: 'cci,combined,daily-files-combined-v09.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v09.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

91  Collection("DAILY_FILES_PASSIVE_V05.2") id: 'DAILY_FILES_PASSIVE_V05.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,SSM/I,AMSR2,SMMR,MIRAS,TMI', platform: 'AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,GCOM-W1,Nimbus-7,SMOS,TRMM,SMAP', keywords: 'amsr-e,amsr2,amsre,aqua,cci,coriolis,daily-files-passive-v05.2,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,dmsp-f13,earth-science>climate-indicators>land-surface/agriculture-indicators>soil-moisture,earth-science>spectral/engineering>microwave,esa,gcom-w1,miras,nimbus-7,orthoimagery,passive,smap,smmr,smos,soil-moisture,ssm/i,tmi,trmm,windsat', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

92  Collection("DAILY_FILES_PASSIVE_V05.3") id: 'DAILY_FILES_PASSIVE_V05.3', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,AMSR2,SMMR,TMI', platform: 'FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,GCOM-W1,Nimbus-7,TRMM,SMAP', keywords: 'amsr-e,amsr2,amsre,aqua,cci,coriolis,daily-files-passive-v05.3,dif10,dmsp-5d-2/f11,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,earth-science>spectral/engineering>microwave,esa,fy-3b,gcom-w1,miras,nimbus-7,orthoimagery,passive,smap,smmr,ssm/i,tmi,trmm,windsat', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v05.3 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070',

93  Collection("DAILY_FILES_PASSIVE_V06.1") id: 'DAILY_FILES_PASSIVE_V06.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.1', instrument: 'AMSR-E,WINDSAT,SSM/I,SSM/I,SSM/I,AMSR2,SMMR,MIRAS,TMI', platform: 'FY-3B,AQUA,WindSat,DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,GCOM-W1,Nimbus-7,SMOS,TRMM,SMAP', keywords: 'amsr-e,amsr2,amsre,aqua,cci,coriolis,daily-files-passive-v06.1,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-f08,dmsp-f11,dmsp-f13,earth-science>agriculture>soils>soil-moisture/water-content,earth-science>spectral/engineering>microwave,esa,fy-3b,gcom-w1,gmi,miras,nimbus-7,orthoimagery,passive,smap,smmr,smos,soil-moisture,ssm/i,tmi,trmm,virr,windsat', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

94  Collection("DAILY_FILES_PASSIVE_V06.2") id: 'DAILY_FILES_PASSIVE_V06.2', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.2', keywords: 'cci,daily-files-passive-v06.2,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,passive,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.The v06.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

95  Collection("DAILY_FILES_PASSIVE_V07.1") id: 'DAILY_FILES_PASSIVE_V07.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 07.1', keywords: 'cci,daily-files-passive-v07.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,passive,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.The v07.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001',

96  Collection("DAILY_FILES_PASSIVE_V08.1") id: 'DAILY_FILES_PASSIVE_V08.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 08.1', keywords: 'cci,daily-files-passive-v08.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,passive,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.The v08.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2022-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

97  Collection("DAILY_FILES_PASSIVE_V09.1") id: 'DAILY_FILES_PASSIVE_V09.1', title: 'ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.1', keywords: 'cci,daily-files-passive-v09.1,earth-science>agriculture>soils>soil-moisture/water-content,esa,orthoimagery,passive,soil-moisture', license: 'other', abstract: 'The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.The v09.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.',

98  Collection("DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_CRYOSAT_V1.0_NH") id: 'DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_CRYOSAT_V1.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from CryoSat-2, v1.0', keywords: 'arctic,cci,drift-aware,drift-aware-sea-ice-thickness-l3c-cryosat-v1.0-nh,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice', license: 'other', abstract: 'This dataset provides daily drift-aware sea ice freeboard and thickness maps, using satellite altimetry data from CryoSat-2, covering the entire Arctic sea ice domain. Daily files are provided during boreal winter seasons (October to April).Neglecting sea ice drift when generating monthly sea ice thickness maps from satellite altimetry will cause blurring of the spatial distribution of ice thickness. This dataset synergizes sea ice freeboard and thickness information from satellite altimetry with sea ice drift estimates from passive microwave satellite sensors. Individual parcels of satellite altimeter measurements are advected daily over a time span of one month to obtain drift-aware sea ice freeboard and thickness maps. Because of the drift correction, this allows the determination of sea ice that was overflown by the satellite multiple times, and therefore the estimation of growth rates and changes in the sea ice thickness distribution due to deformation and thermodynamic ice growth between satellite overflights. With the estimation of sea ice growth, measurements can be corrected for the time offset between the acquisition day and the target day, the day to which all measurements within a month are projected.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, as part of the ESA CCI Sea Ice project.',

99  Collection("DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_ENVISAT_V1.0_NH") id: 'DRIFT_AWARE_SEA_ICE_THICKNESS_L3C_ENVISAT_V1.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Drift-aware sea-ice thickness for the Northern Hemisphere from Envisat, v1.0', keywords: 'arctic,cci,drift-aware,drift-aware-sea-ice-thickness-l3c-envisat-v1.0-nh,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice', license: 'other', abstract: 'This dataset provides daily drift-aware sea ice freeboard and thickness maps, using satellite altimetry data from Envisat, covering the entire Arctic sea ice domain. Daily files are provided during boreal winter seasons (October to April).Neglecting sea ice drift when generating monthly sea ice thickness maps from satellite altimetry will cause blurring of the spatial distribution of ice thickness. This dataset synergizes sea ice freeboard and thickness information from satellite altimetry with sea ice drift estimates from passive microwave satellite sensors. Individual parcels of satellite altimeter measurements are advected daily over a time span of one month to obtain drift-aware sea ice freeboard and thickness maps. Because of the drift correction, this allows the determination of sea ice that was overflown by the satellite multiple times, and therefore the estimation of growth rates and changes in the sea ice thickness distribution due to deformation and thermodynamic ice growth between satellite overflights. With the estimation of sea ice growth, measurements can be corrected for the time offset between the acquisition day and the target day, the day to which all measurements within a month are projected.These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, as part of the ESA CCI Sea Ice project.',

100  Collection("DTU_TUM_ARCTIC_ANTARCTIC_MSLA_20170720") id: 'DTU_TUM_ARCTIC_ANTARCTIC_MSLA_20170720', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)', keywords: 'dtu-tum-arctic-antarctic-msla-20170720,esa-cci,orthoimagery,sla', license: 'other', abstract: 'This dataset contains high latitude sea level anomalies produced by DTU (Technical University of Denmark) and TUM (Technical University of Munich) as part of the ESA Sea Level CCI (Climate Change Initiative) project, covering both the Arctic and Antarctic regions.The data comprises weekly means from August 1991 to April 2017 and has been obtained using satellite altimetry data from four satellite missions: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336).Two datasets are available: dataset #1 is based on the ALES+ retracking without correction of the inverse barometer whereas dataset #2 has been corrected for this effect.Dataset #1 is provided both 'masked' and 'unmasked', where the masked data have been masked using sea ice concentrations downloaded from osisaf.met.no/p/ice. Dataset #2 is provided both 'masked' and 'unmasked', where the masked data have had data points retrieved over land removed from the files.',

101  Collection("ENVISAT_ATSR_L3C_0.01_V3.00_DAILY") id: 'ENVISAT_ATSR_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,envisat,envisat-atsr-l3c-0.01-v3.00-daily,esa,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

102  Collection("ENVISAT_ATSR_L3C_0.01_V3.00_MONTHLY") id: 'ENVISAT_ATSR_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,envisat,envisat-atsr-l3c-0.01-v3.00-monthly,esa,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage for the monthly dataset starts from August 2002 and ends March 2012. There is a twelve day gap in the underlying data due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

103  Collection("ERS-2_ATSR_L3C_0.01_V3.00_DAILY") id: 'ERS-2_ATSR_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'atsr-2,cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,ers-2,ers-2-atsr-l3c-0.01-v3.00-daily,esa,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

104  Collection("ERS-2_ATSR_L3C_0.01_V3.00_MONTHLY") id: 'ERS-2_ATSR_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00', instrument: 'ATSR-2', platform: 'ERS-2', keywords: 'atsr-2,cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,ers-2,ers-2-atsr-l3c-0.01-v3.00-monthly,esa,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

105  Collection("FCDR_V2.0") id: 'FCDR_V2.0', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): Fundamental Climate Data Records of sea level anomalies and altimeter standards, Version 2.0', instrument: 'SIRAL,RA-2,RA,RA,POSEIDON-2,SSALT', platform: 'CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON', keywords: 'altika,cryosat-2,dif10,earth-science>oceans>sea-surface-topography>sea-surface-height,earth-science>spectral/engineering>radar,envisat,ers-1,ers-2,esa-cci,fcdr-v2.0,gfo,gfo-ra,jason-1,jason-2,orthoimagery,poseidon-2,poseidon-3,ra,ra-2,saral,sea-level,siral,sla,ssalt,topex/poseidon', license: 'other', abstract: 'As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project, Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project. These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015. This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products. They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km. The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org',

106  Collection("GIEMS_METHANE_CENTRIC_V1.0") id: 'GIEMS_METHANE_CENTRIC_V1.0', title: 'ESA RECCAP-2 Climate Change Initiative (RECCAP2_cci): Methane-Centric Wetland Dataset Based on GIEMS (1992-2020), v1.0', keywords: 'earth-science>atmosphere>atmospheric-chemistry>carbon-and-hydrocarbon-compounds>methane,giems,giems-methane-centric-v1.0,methane,orthoimagery,reccap-2,wetland', license: 'other', abstract: 'To aid methane emission modelling within ESA's Regional Carbon Cycle Assessment and Processes Phase 2 (RECCAP-2) project, a methane-centric wetland dataset based on the Global Inundation Estimate from Multiple Satellites (GIEMS-2) database has been produced.The GIEMS-2 database provides the monthly extent of the continental water surfaces, including lakes, rivers, wetlands, and rice paddies, from 1992 to 2015, as described in Prigent et al. (2020). It is on a 0.25 x 0.25 degree regular grid in latitude and longitude. It has recently been extended to 2020 within the RECCAP-2 project.For methane emission modeling, three water surface types are usually considered separately: the permanent water surfaces (such as lakes, rivers, and reservoirs), the rice paddies, and the wetlands (i.e., the remaining water surfaces). As a consequence, the possibility to separate these contributions within the GIEMS pixels is required. This methane-centric GIEMS dataset isolates wetlands from the other surface waters in order to facilitate the estimation of the wetland methane emissions.',

107  Collection("GMPE_CDR_V2_L4_V2.0") id: 'GMPE_CDR_V2_L4_V2.0', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): GHRSST Multi-Product ensemble (GMPE), v2.0', keywords: 'cci,earth-science>oceans>ocean-temperature>sea-surface-temperature,esa,esacci-sst,gmpe-cdr-v2-l4-v2.0,orthoimagery,sst,unspecified', license: 'other', abstract: 'The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci) has accurately determined the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified sea surface temperatures (SSTs) to a quality suitable for climate research. This GHRSST (Group for High Resolution Sea Surface Temperature) Multi-Product Ensemble (GMPE) dataset was produced by the ESA SST_cci project to facilitate comparison of its own spatially complete analyses with other level 4 SST analysis products. It provides the median and standard deviation of the ensemble of input analyses, differences between the individual analyses and the median, and gradients in the input data and the median. The outputs are provided on a 0.25˚ regular latitude-longitude grid. The product extends from 1 September 1981 to 31 December 2016.The product was generated using the following inputs: ESA SST_cci Analysis version 2.0; ESA SST_cci Analysis version 1.1; E.U. Copernicus Marine Environment Monitoring Service (CMEMS) SST information (the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing); National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis; Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis; Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 (10 realisations); Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis. Full details of the data used to generate this product are provided in the associated documentation.',

108  Collection("GOES_IMAGER_ABI_L3C_V1.00_MONTHLY") id: 'GOES_IMAGER_ABI_L3C_V1.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Geostationary Operational Environmental Satellite (GOES) level 3C (L3C) product (2009-2020), version 1.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,goes,goes-imager-abi-l3c-v1.00-monthly,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16. The original surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16, and in the L3C dataset a monthly average at each time step is provided. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. LSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.',

109  Collection("GOES_IMAGER_ABI_L3U_V1.00") id: 'GOES_IMAGER_ABI_L3U_V1.00', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3U (L3U) product (2009-2020), version 1.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,goes,goes-imager-abi-l3u-v1.00,land-surface-temperature,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LST) and their uncertainty estimates from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16. The surface temperatures are generated every 3 hours for GOES 12 and 13 and every hour for GOES 16. Data are distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the GOES disk, which encompasses North and South America. LSTs are estimated from infrared measurements using a single channel algorithm in the case of GOES 12 and 13, and a split-window algorithm in the case of GOES 16. Observations are only available under clear-sky conditions. Quality of single channel algorithms is generally lower than dual channel ones, users are advised to read the respective Validation Report for more information on expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.',

110  Collection("GOMOS_AERGOM_L3_V3.00") id: 'GOMOS_AERGOM_L3_V3.00', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from GOMOS (AERGOM algorithm), Version 3.00', instrument: 'GOMOS', platform: 'Envisat', keywords: 'aerosol,cci,dif10,earth-science>atmosphere>aerosols,envisat,esa,gomos,gomos-aergom-l3-v3.00,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 gridded stratospheric aerosol properties from the GOMOS instrument on the ENVISAT satellite. This version of the data is version 3.00, and has been derived using the AERGOM algorithm by BIRA (Belgian Institute for Space Aeronomy). For further details about these data products please see the linked documentation.',

111  Collection("GRAVIMETRIC_MASS_BALANCE_BASIN_V3.0") id: 'GRAVIMETRIC_MASS_BALANCE_BASIN_V3.0', title: 'ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance basin product, v3.0, 2002-2020', keywords: 'antarctica,earth-science>solid-earth>gravity/gravitational-field,esa-cci,grace,grace-fo,gravimetric-mass-balance-basin-v3.0,ice-sheet-mass-balance,orthoimagery', license: 'other', abstract: 'This dataset contains the Gravimetric Mass Balance (GMB) basin product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)The GMB basin product provides time series of integrated mass changes for 26 drainage basins and the aggregations of the Antarctic Peninsula, East Antarctica, West Antarctica and the entire AIS. Based on the GMB basin product, ice mass balance estimates, i.e. linear trend in the change in ice mass, were derived for all drainage basins and aggregations. A gridded GMB product is also available as a separate dataset.Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736',

112  Collection("GRAVIMETRIC_MASS_BALANCE_GRIDDED_V3.0") id: 'GRAVIMETRIC_MASS_BALANCE_GRIDDED_V3.0', title: 'ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0, 2002 - 2020', keywords: 'antarctica,earth-science>solid-earth>gravity/gravitational-field,esa-cci,grace,grace-fo,gravimetric-mass-balance-gridded-v3.0,ice-sheet-mass-balance,orthoimagery', license: 'other', abstract: 'This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)The GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km² covering the entire AIS. A GMB basin product is also available as a separate dataset.Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736',

113  Collection("GREENLAND_CALVING_FRONT_LOCATIONS_V3.0") id: 'GREENLAND_CALVING_FRONT_LOCATIONS_V3.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v3.0', instrument: 'AMI/SAR,AMI/SAR,TM,ETM,TIRS,C-SAR,C-SAR', platform: 'ERS-1,ERS-2,Landsat-5,Landsat-7,Landsat-8,Sentinel-1A,Sentinel-1B', keywords: 'ami,ami-sar,ami/sar,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,ers-1,ers-2,esa,etm,etm+,greenland,greenland-calving-front-locations-v3.0,ice-sheet,ice-sheets,landsat-5,landsat-7,landsat-8,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b,tirs,tm', license: 'other', abstract: 'The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet, produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. The calving front location has been derived by manual delineation using SAR (Synthetic Aperture Radar) data from the ERS-1/2, Envisat and Sentinel-1 satellites and satellite imagery from LANDSAT 5,7,8. The digitized calving fronts are stored in ESRI vector shape-file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The product was generated by ENVEO (Environmental Earth Observation Information Technology GmbH)',

114  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.4") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.4', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v1.4', instrument: 'GRACE LRR', platform: 'GRACE', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>solid-earth>gravity/gravitational-field,esa,grace,grace-instrument,grace-lrr,greenland,greenland-gravimetric-mass-balance-dtu-space-v1.4,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017. This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.',

115  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.5") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V1.5', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5', instrument: 'GRACE LRR', platform: 'GRACE', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>solid-earth>gravity/gravitational-field,esa,grace,grace-instrument,grace-lrr,greenland,greenland-gravimetric-mass-balance-dtu-space-v1.5,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.',

116  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V2.2") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V2.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2', keywords: 'cci,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,gravimetric-mass-balance,greenland-gravimetric-mass-balance-dtu-space-v2.2,greenland-ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through August 2021.The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.Two different types of products are available. First, the gridded mass trends product is comprised of ice mass change trends for cells of equal area with 50 km resolution covering the whole GIS. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS.Reference:Barletta, V. R., Sørensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.",',

117  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V3.0") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_DTU_SPACE_V3.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v3.0', keywords: 'cci,esa,gravimetric-mass-balance,greenland-gravimetric-mass-balance-dtu-space-v3.0,greenland-ice-sheet,orthoimagery', license: 'other', abstract: 'This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through May 2024.The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.Two different types of products are available. First, the gridded mass trends product is comprised of ice mass change trends for cells of equal area with 44 km resolution covering the whole GIS and different drainage basins. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS over different 5-year periods between 2002 and 2024. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document and the Product Specification Document which are provided on the project website. Reference:Barletta, V. R., Sørensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.',

118  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.2") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.2', instrument: 'GRACE LRR', platform: 'GRACE', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>solid-earth>gravity/gravitational-field,esa,grace,grace-instrument,grace-lrr,greenland,greenland-gravimetric-mass-balance-tu-dresden-v1.2,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.2) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016)The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065',

119  Collection("GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.3") id: 'GREENLAND_GRAVIMETRIC_MASS_BALANCE_TU_DRESDEN_V1.3', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by TU Dresden, v1.3', instrument: 'GRACE LRR', platform: 'GRACE', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>solid-earth>gravity/gravitational-field,esa,grace,grace-instrument,grace-lrr,greenland,greenland-gravimetric-mass-balance-tu-dresden-v1.3,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.3) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.Please cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065',

120  Collection("GREENLAND_GROUNDING_LINE_LOCATIONS_V1.3") id: 'GREENLAND_GROUNDING_LINE_LOCATIONS_V1.3', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations from SAR Interferometry, v1.3', instrument: 'AMI/SAR,AMI/SAR,C-SAR,C-SAR', platform: 'ERS-1,ERS-2,Sentinel-1A,Sentinel-1B', keywords: 'ami,ami-sar,ami/sar,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,ers-1,ers-2,esa,greenland,greenland-grounding-line-locations-v1.3,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 and Sentinel-1 SAR (Synthetic Aperture Radar) interferometry. This version of the dataset (v1.3) has been extended with grounding lines for 2017. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. The grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.Remote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone).',

121  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2013_2014_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2013_2014_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2013-2014, v1.0', instrument: 'SAR', platform: 'RADARSAT-2', keywords: 'dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>terrestrial-hydrosphere>snow/ice>ice-velocity,greenland,greenland-ice-velocity-greenland-ice-velocity-map-winter-2013-2014-v1.0,ice-sheets,ice-velocity,orthoimagery,radarsat-2,sar,sar-(radarsat-2)', license: 'other', abstract: 'This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2013-2014, derived from RADARSAT-2 data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The ice velocity data were derived from intensity-tracking of RADARSAT-2 data aquired between 21/1/2014 and 02/04/2014. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the Eastings and Northings direction of the grid; the vertical displacement, derived from a digital elevation model, is also provided. Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. This product was generated by DTU Space - Microwaves and Remote Sensing.',

122  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2014_2015_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2014_2015_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2014-2015, v1.0', instrument: 'C-SAR', platform: 'Sentinel-1A', keywords: 'c-sar,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>terrestrial-hydrosphere>snow/ice>ice-velocity,greenland,greenland-ice-velocity-greenland-ice-velocity-map-winter-2014-2015-v1.0,ice-sheets,ice-velocity,orthoimagery,sar-c-(sentinel-1),sentinel-1a', license: 'other', abstract: 'This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2014-2015, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.',

123  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2015_2016_V1.2") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2015_2016_V1.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2015-2016, v1.2', instrument: 'C-SAR', platform: 'Sentinel-1A', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-ice-velocity-map-winter-2015-2016-v1.2,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a', license: 'other', abstract: 'This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data acquired from 01/10/2015 to 31/10/2016, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).',

124  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2016_2017_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2016_2017_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2016-2017, v1.0', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-ice-velocity-map-winter-2016-2017-v1.0,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2016-2017, derived from Sentinel-1 SAR data acquired from 23/12/2016 to 27/02/2017, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. In total approximately 1800 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).',

125  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2017_2018_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_ICE_VELOCITY_MAP_WINTER_2017_2018_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2017-2018, v1.0', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-ice-velocity-map-winter-2017-2018-v1.0,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2017-2018, derived from Sentinel-1 SAR data acquired from 28/12/2017 to 28/02/2018, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. In total approximately 1900 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz),derived from a digital elevation model, is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).',

126  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_CSK_JAKOBSHAVN_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_CSK_JAKOBSHAVN_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0', instrument: 'SAR', platform: 'COSMO-SkyMed', keywords: 'cci,cosmo-skymed,csk-1,csk-2,csk-4,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-csk-jakobshavn-v1.0,ice-sheet,ice-sheets,orthoimagery,sar,sar-2000', license: 'other', abstract: 'This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014. The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space. For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0.',

127  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_79FJORD_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_79FJORD_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the 79-Fjord Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-79fjord-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

128  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HAGEN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HAGEN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-hagen-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Hagen glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

129  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HELHEIM_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_HELHEIM_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-helheim-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

130  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_JAKOBSHAVN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_JAKOBSHAVN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-jakobshavn-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

131  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_KANGERLUSSUAQ_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_KANGERLUSSUAQ_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq Glacier for 2015-2017 from Sentinel-1, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-kangerlussuaq-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

132  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_PETERMANN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_PETERMANN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-petermann-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 22/1/2015-19/3/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

133  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_STORSTROEMMEN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_STORSTROEMMEN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Storstroemmen Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-storstroemmen-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 24/1/2015 and 22/03/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

134  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_UPERNAVIK_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_UPERNAVIK_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-upernavik-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

135  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_ZACHARIAE_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_250M_S1_ZACHARIAE_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1', instrument: 'C-SAR,C-SAR', platform: 'Sentinel-1A,Sentinel-1B', keywords: 'c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-250m-s1-zachariae-v1.1,ice-sheet,ice-sheets,orthoimagery,sar-c-(sentinel-1),sentinel-1a,sentinel-1b', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Zachariae glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.',

136  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_79FJORD_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_79FJORD_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-79fjord-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The data have been produced by S[&]T Norway',

137  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_DOCKER_SMITH_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_DOCKER_SMITH_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Døcker Smith Glacier between 2016-05-08 and 2016-05-18, generated using Sentinel-2 data, v1.0', instrument: 'MSI', platform: 'Sentinel-2', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-docker-smith-v1.0,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a', license: 'other', abstract: 'This dataset contains an optical ice velocity time series for the Døcker Smith Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2016-05-08 and 2016-05-18. It is part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway.',

138  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HAGEN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HAGEN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Hagen Glacier between 2017-06-30 and 2017-08-14, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-hagen-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Hagen Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-30 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.',

139  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HELHEIM_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_HELHEIM_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Helheim Glacier between 2017-05-01 and 2017-08-29, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-helheim-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Helheim Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-08-29. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.',

140  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_JAKOBSHAVN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_JAKOBSHAVN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Jakobshavn Glacier between 2017-06-03 and 2017-09-08, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-jakobshavn-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Jakobshavn Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-03 and 2017-09-08. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.',

141  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_KANGERDLUGSSUAQ_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_KANGERDLUGSSUAQ_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-kangerdlugssuaq-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.',

142  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_PETERMANN_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_PETERMANN_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Petermann Glacier between 2017-05-01 and 2017-09-14, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-petermann-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Petermann Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-09-14. It has been produced as part of the ESA Greenland Ice sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.',

143  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_UPERNAVIK_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_UPERNAVIK_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Upernavik Glacier between 2017-07-15 and 2017-08-14, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-upernavik-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains optical ice velocity time series and seasonal product of the Upernavik Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-15 and 2017-08-14. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The product was generated by S[&]T Norway.',

144  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_ZACHARIAE_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_50M_S2_ZACHARIAE_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1', instrument: 'MSI,MSI', platform: 'Sentinel-2,Sentinel-2B', keywords: 'cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland,greenland-ice-velocity-greenland-iv-50m-s2-zachariae-v1.1,ice-sheet,ice-sheets,msi,msi-(sentinel-2),orthoimagery,sentinel-2,sentinel-2-msi,sentinel-2a,sentinel-2b', license: 'other', abstract: 'This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway.',

145  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_HAGEN_TIMESERIES_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_HAGEN_TIMESERIES_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-hagen-timeseries-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Hagen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 26/08/1991 and 7/5/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

146  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_HELHEIM_TIMESERIES_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_HELHEIM_TIMESERIES_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim glacier from ERS-1, ERS-2 and Envisat data for 1996-2010, v1.1', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-helheim-timeseries-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Helheim glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 29/05/1996 and 26/2/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle of 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

147  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_JAKOBSHAVN_TIMESERIES_V1.2") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_JAKOBSHAVN_TIMESERIES_V1.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-jakobshavn-timeseries-v1.2,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH).',

148  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_KANGERLUSSUAQ_TIMESERIES_V1.0") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_KANGERLUSSUAQ_TIMESERIES_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq glacier from ERS-1, ERS-2, Envisat for 1992-2008, v1.0', instrument: 'ASAR,AMI/SAR', platform: 'Envisat,ERS-1', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,esa,greenland,greenland-ice-velocity-greenland-iv-kangerlussuaq-timeseries-v1.0,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Kangerlussuaq glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data aquired between 02/01/1992 and 17/12/2008. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 3 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

149  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_PETERMANN_TIMESERIES_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_PETERMANN_TIMESERIES_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-petermann-timeseries-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

150  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_STORSTROMMEN_TIMESERIES_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_STORSTROMMEN_TIMESERIES_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series of the Storstrommen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-storstrommen-timeseries-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Storstrommen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 06/10/1991 and 20/03/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

151  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_UPERNAVIK_TIMESERIES_V1.2") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_UPERNAVIK_TIMESERIES_V1.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2', instrument: 'PALSAR,ASAR,AMI/SAR,AMI/SAR', platform: 'ALOS-1,Envisat,ERS-1,ERS-2', keywords: 'alos,alos-1,ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>spectral/engineering>radar,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-upernavik-timeseries-v1.2,ice-sheet,ice-sheets,orthoimagery,palsar', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

152  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_IV_ZACHARIAE_79FJORD_TIMESERIES_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_IV_ZACHARIAE_79FJORD_TIMESERIES_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae and 79Fjord area from ERS-1, ERS-2 and Envisat data for 1991-2011, v1.1', instrument: 'ASAR,AMI/SAR,AMI/SAR', platform: 'Envisat,ERS-1,ERS-2', keywords: 'ami,ami-sar,ami/sar,asar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland,greenland-ice-velocity-greenland-iv-zachariae-79fjord-timeseries-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Zachariae and 79Fjord area in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 01/08/1991 and 07/02/2011. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland).',

153  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_ERS2_1995_1996_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_ERS2_1995_1996_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release)', instrument: 'AMI/SAR', platform: 'ERS-2', keywords: 'ami,ami-sar,ami/sar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,ers-2,esa,greenland,greenland-ice-velocity-greenland-margin-ers2-1995-1996-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing. For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product.',

154  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_PALSAR_TIMESERIES_2006_2011_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_MARGIN_PALSAR_TIMESERIES_2006_2011_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from the PALSAR instrument for 2006-2011, v1.1 (June 2016 version)', instrument: 'PALSAR', platform: 'ALOS-1', keywords: 'alos,alos-1,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,earth-science>spectral/engineering>radar,esa,greenland,greenland-ice-velocity-greenland-margin-palsar-timeseries-2006-2011-v1.1,ice-sheet,ice-sheets,orthoimagery,palsar', license: 'other', abstract: 'This dataset contains a time series of ice velocities for the Greenland margin from the PALSAR instrument on the ALOS satellite. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. This dataset consists of a time series of ice velocity with yearly sampling, derived from intensity tracking of PALSAR data acquired between 20-12-2016 and 17-03-2011. It provides components of the ice velocity and the magnitude of the velocity. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by GEUS. For further details, please consult the Product User Guide (v2.0)Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product.',

155  Collection("GREENLAND_ICE_VELOCITY_GREENLAND_NORTHERN_DRAINAGE_BASINS_ERS1_WINTER_1991_1992_V1.1") id: 'GREENLAND_ICE_VELOCITY_GREENLAND_NORTHERN_DRAINAGE_BASINS_ERS1_WINTER_1991_1992_V1.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Northern Drainage basin from ERS-1 for winter 1991-1992, v1.1 (June 2016 release)', instrument: 'AMI/SAR', platform: 'ERS-1', keywords: 'ami,ami-sar,ami/sar,cci,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,ers-1,esa,greenland,greenland-ice-velocity-greenland-northern-drainage-basins-ers1-winter-1991-1992-v1.1,ice-sheet,ice-sheets,orthoimagery', license: 'other', abstract: 'This dataset contains ice velocities for the Greenland Northern Drainage Basin for winter 1991-1992, which have been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data has been derived from intensity-tracking of ERS-1 Ice phase (3 days repeat) data aquired between 29th December 1991 and 22nd March 1992.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevationmodel, is also provided. (Please note that in earlier versions of this product the horizontal velocities were provided as true East and North velocities). Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use this later v1.1 product.',

156  Collection("GREENLAND_ML_CALVING_FRONT_LOCATIONS_V1.0") id: 'GREENLAND_ML_CALVING_FRONT_LOCATIONS_V1.0', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Machine Learning Generated Greenland Calving Front Locations v1.0', keywords: 'cci,esa,greenland,greenland-ml-calving-front-locations-v1.0,ice-sheet,orthoimagery', license: 'other', abstract: 'Calving Front locations for Upernavik A,E,F, Humboldt and Hagen glaciers in Greenland, generated by a deep learning based model using Sentinel-2 imagery.The calving front location is generated by a deep learning based model using Sentinel-2 imagery acquired from 2019-2020. The digitized calving fronts are stored in geoJSON vector file format and include metadata information on the sensor and processing steps in the corresponding attribute table.The CCI Calving Front Locations (CFL) v1.0 release contains one primary dataset, the calving front locations, and auxiliary files to describe the file product: locations.png and glaciers.geojson for visualizing the glaciers, README and DESCRIPTION text files about the product structure, and a visual example of what a calving front looks like. The Greenland CCI Calving Front Locations (CFL) v1.0 product is an experimental product using deep learning to automatically derive calving front locations for selected glaciers based on Sentinel-2 imagery at the end of the summer season.The product was generated by S[&]T Norway and ENVEO.',

157  Collection("GREENLAND_SURFACE_ELEVATION_CHANGE_CRYOSAT2_V2.2") id: 'GREENLAND_SURFACE_ELEVATION_CHANGE_CRYOSAT2_V2.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.2', instrument: 'SIRAL', platform: 'CryoSat-2', keywords: 'cci,cryosat-2,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland-ice-sheet,greenland-surface-elevation-change-cryosat2-v2.2,ice-sheets,orthoimagery,siral', license: 'other', abstract: 'This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017. The surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.The algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012',

158  Collection("GREENLAND_SURFACE_ELEVATION_CHANGE_SARAL-ALTIKA_V0.1") id: 'GREENLAND_SURFACE_ELEVATION_CHANGE_SARAL-ALTIKA_V0.1', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change grid from SARAL-AltiKa for 2013-2017, v0.1', instrument: 'SIRAL', platform: 'CryoSat-2,SARAL', keywords: 'altika,cci,cryosat-2,dif10,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,esa,greenland-ice-sheet,greenland-surface-elevation-change-saral-altika-v0.1,ice-sheets,orthoimagery,saral,siral', license: 'other', abstract: 'This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from SARAL-AltiKa for 2013-2017. This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite. The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.The corresponding SEC grid from Cryosat-2 is included for comparison. The algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012. The approach used here corresponds to Least Squares Method (LSM) 5 described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 20 km.',

159  Collection("GREENLAND_SURFACE_ELEVATION_CHANGE_V1.2") id: 'GREENLAND_SURFACE_ELEVATION_CHANGE_V1.2', title: 'ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change 1992-2014, v1.2', instrument: 'SIRAL,RA-2,RA,RA', platform: 'CryoSat-2,Envisat,ERS-1,ERS-2', keywords: 'cci,cryosat-2,dif10,dtu-space,earth-science>cryosphere>glaciers/ice-sheets>ice-sheets,envisat,ers-1,ers-2,esa,greenland-ice-sheet,greenland-surface-elevation-change-v1.2,ice-sheets,orthoimagery,ra,ra-2,siral', license: 'other', abstract: 'This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from satellite (ERS‐1, ERS‐2, Envisat and Cryosat) radar altimetry. The ice mask is based on the GEUS/GST land/ice/ocean mask provided as part of national mapping projects, and based on 1980’s aerial photography. The data from ERS and Envisat are based on a 5‐year running average, using combined algorithms of repeat‐track (RT), along‐track (AT) or cross‐over (XO) algorithms, and include propagated error estimates. It is important to note that different processing algorithms were applied to the ERS‐1, ERS‐2, Envisat and CryoSat data; for details see the Product User Guide (PUG), available on the CCI website and in the documentation section here. For ERS‐1, the radar data were processed using a cross‐over algorithm (XO) only. For ERS‐2 data and Envisat data in repeat mode, a combination of RT and XO algorithms was applied, followed by filtering. For across‐mission (i.e. ERS‐2‐Envisat) combinations, and for Envisat operating in a drifting orbit, an AT and XO combination was applied (the difference between RT and AT algorithms is that AT use reference tracks and searches for data in the vicinity of this track). For CryoSat data a binning/gridding and plane fit method has been applied, following by weak filtering (0.05 degree resolution).',

160  Collection("GROUNDING_LINE_LOCATIONS_KEY_GLACIERS_V2.0") id: 'GROUNDING_LINE_LOCATIONS_KEY_GLACIERS_V2.0', title: 'ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding line location for key glaciers, Antarctica, 1994-2020, v2.0', keywords: 'antarctica,cci,esa,ground-line-location,grounding-line-locations-key-glaciers-v2.0,orthoimagery', license: 'other', abstract: 'This dataset contains grounding line locations (GLL) for key glaciers in Antarctica, produced as part of the ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci) project. The data have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 satellites, acquired between 1994 and 2020.',

161  Collection("GROUND_TEMPERATURE_L4_AREA4_PP_V03.0") id: 'GROUND_TEMPERATURE_L4_AREA4_PP_V03.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0', instrument: 'MODIS,MERIS,AVHRR-3,AVHRR-3,AVHRR-3,MODIS', platform: 'AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V', keywords: 'aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,envisat,ground-temperature,ground-temperature-l4-area4-pp-v03.0,meris,modis,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,proba-v,sar-x,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.',

162  Collection("GROUND_TEMPERATURE_L4_AREA4_PP_V04.0") id: 'GROUND_TEMPERATURE_L4_AREA4_PP_V04.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0', instrument: 'MODIS,MERIS,MODIS,AVHRR-3,AVHRR-3,AVHRR-3', platform: 'AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V', keywords: 'aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,envisat,ground-temperature,ground-temperature-l4-area4-pp-v04.0,meris,modis,modis-terra,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,proba-v,sar-x,spot,terra', license: 'other', abstract: 'This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.',

163  Collection("GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_ANTARCTICA") id: 'GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_ANTARCTICA', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for Antarctica, v5.0', instrument: 'MODIS,MODIS', platform: 'AQUA,TERRA', keywords: 'aqua,cci,department-of-geosciences,dif10,earth-science>agriculture>soils>permafrost,earth-science>land-surface>frozen-ground>permafrost,ground-temperature,ground-temperature-l4-area4-pp-v05.0-antarctica,level-4,modis,orthoimagery,permafrost,terra,university-of-oslo,year', license: 'other', abstract: 'This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m). Case A: It covers Antarctica (south of 60°S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60°S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc',

164  Collection("GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE") id: 'GROUND_TEMPERATURE_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v5.0', instrument: 'MODIS,MERIS,C-SAR,MSI,MODIS', platform: 'AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V', keywords: 'aqua,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,earth-science>land-surface>frozen-ground>permafrost,envisat,ground-temperature,ground-temperature-l4-area4-pp-v05.0-northern-hemisphere,level-4,meris,modis,msi,msi-(sentinel-2),orthoimagery,permafrost,proba-v,sar-c-(sentinel-1),sentinel-1a,sentinel-2,sentinel-2-msi,sentinel-2a,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m). Case A: It covers the Northern Hemisphere (north of 30°N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30°N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc',

165  Collection("ICE_VELOCITY_ANTARCTIC_ICE_SHEET_SENTINEL_1_MONTHLY_V1.0") id: 'ICE_VELOCITY_ANTARCTIC_ICE_SHEET_SENTINEL_1_MONTHLY_V1.0', title: 'ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1', keywords: 'antarctic,cci,esa,ice-sheet-velocity,ice-velocity-antarctic-ice-sheet-sentinel-1-monthly-v1.0,orthoimagery', license: 'other', abstract: 'This dataset contains monthly gridded ice velocity maps of the Antarctic Ice Sheet derived from Sentinel-1 data acquired between 2017-01-01 and 2020-08-31. It was generated by ENVEO, as part of the ESA Antarctic Ice Sheet Climate Change Initiative project (Antarctic_Ice_Sheet_cci).The surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 200m grid spacing in Polar Stereographic projection (EPSG: 3031). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty. The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired within a single month in a monthly averaged product.',

166  Collection("IIML_GREENLAND_V1_2017") id: 'IIML_GREENLAND_V1_2017', title: 'ESA Glaciers Climate Change Initiative (Glaciers_cci): 2017 inventory of ice marginal lakes in Greenland (IIML), v1', keywords: 'esa,glaciers-cci,greenland,ice-marginal-lakes,iiml-greenland-v1-2017,orthoimagery', license: 'other', abstract: 'The 2017 inventory of ice marginal lakes in Greenland (IIML) has been produced as part of the European Space Agency (ESA) Climate Change Initiative (CCI) in Option 6 of the Glaciers_cci project, and is a product that addresses the terrestrial essential climate variable (ECV) Lakes.The IIML is a comprehensive record of all identified ice marginal lakes across the terrestrial margin of Greenland, detected using remote sensing techniques. The detected lakes are presented as polygon vector features in shapefile format, with coordinates provided in the WGS 1984 UTM Zone 24N projected coordinate system. Ice marginal lakes were identified using three independent remote sensing methods: 1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery; 2) multi-spectral indices classification from Sentinel-2 optical imagery; and 3) sink detection from the ArcticDEM (v3). All data were compiled and filtered in a semi-automated approach, using a modified version of the MEaSUREs GIMP ice mask (https://nsidc.org/data/NSIDC-0714/versions/1) to clip the dataset to within 1 km of the ice margin. Each detected lake was then verified manually. The IIML was collected to better understand the impact of ice marginal lake change on the future sea level budget and the terrestrial and marine landscapes of Greenland, such as its ecosystems and human activities.The IIML is a complete inventory of Greenland, with no absent data.',

167  Collection("IND_V2.0") id: 'IND_V2.0', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): Oceanic Indicators of Mean Sea Level Changes, Version 2.0', instrument: 'RA-2,RA,RA,POSEIDON-2,SSALT', platform: 'CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON', keywords: 'altika,cci,centre-national-detudes-spatiales,collecte-localisation-satellites,cryosat-2,cryosat-programme,dif10,earth-science>oceans>sea-surface-topography>sea-surface-height,earth-science>spectral/engineering>radar,environmental-satellite,envisat,ers,ers-1,ers-2,esa,european-space-agency,geodetic,geosat-follow-on-radar-altimeter,gfo,gfo-ra,ind-v2.0,indicator,jason,jason-1,jason-2,mean-sea-level-trends,merged,month,orthoimagery,poseidon-2,poseidon-3,ra,ra-2,radar-altimeter,radar-altimeter-2,saral,saral-programme,sea-level,sea-level-indicators,single-frequency-solid-state-altimeter,ssalt,topex/poseidon', license: 'other', abstract: 'As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a number of oceanic indicators of mean sea level changes have been produced from merging satellite altimetry measurements of sea level anomalies. The oceanic indicators dataset consists of static files covering the whole altimeter period, describing the evolution of the project's monthly sea level anomaly gridded product (see separate dataset record).The oceanic indicators that are provided are: 1) the temporal evolution of the global Mean Sea Level (MSL) DOI: 10.5270/esa-sea_level_cci-IND_MSL_MERGED-1993_2015-v_2.0-201612 ;2) the geographic distribution of Mean Sea Level changes (MSLTR) DOI: 10.5270/esa-sea_level_cci-IND_MSLTR_MERGED-1993_2015-v_2.0-201612 ;3) Maps of the amplitude and phase of the annual cycle (MSLAMPH) DOI: 10.5270/esa-sea_level_cci-IND_MSLAMPH_MERGED-1993_2015-v_2.0-201612.The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612.When using or referring to the SL_cci products, please mention the associated DOIs and also use the following citation where a detailed description of the SL_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these products please email: info-sealevel@esa-sealevel-cci.org',

168  Collection("L3S_VP_PRODUCTS_V1.0") id: 'L3S_VP_PRODUCTS_V1.0', title: 'ESA Vegetation Parameters Climate Change Initiative (Vegetation_Parameters_cci): LAI and fAPAR, Version 1.0', keywords: 'cci,climate-change,earth-science>biosphere>vegetation,earth-science>biosphere>vegetation>photosynthetically-active-radiation>fraction-of-absorbed-photosynthetically-active-radiation-(fapar),earth-science>biosphere>vegetation>vegetation-index>leaf-area-index-(lai),esa,fapar,gcos,l3s-vp-products-v1.0,lai,orthoimagery,vegetation', license: 'other', abstract: 'Climate Research Data Package 1 from the ESA Climate Change Initiative Vegetation Parameters Project (Vegetation_parameters_cci). The dataset consists of Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) gridded at 1 km resolution for the period 2000-2020. The dataset is based on data from SPOT4/5-VEGETATION1/2 and PROBA-V as input data.LAI and fAPAR are retrieved using OptiSAIL (see Blessing and Giering, 2021 doi:10.20944/preprints202109.0147.v1). The dataset is processed for a north-south transect from Finland to South-Africa, as well as for a set of globally distributed sites that is representative for all biomes and for those sites where in-situ reference data is available.The temporal resolution of both datasets is 5 days, but is computed using data selected from a symmetric 10-days window. The data are not smoothed in time. The transect is ordered in tiles following the PROBA-V tiling definition. These files contain the fully validated layers of (effective) LAI, fAPAR, their uncertainties and the correlation between both. The sites additionally include the variables Chlorophyll a+b leaf pigment concentration (Cab), the fraction of Chlorophyll Absorbed Photosynthetically Active Radiation (fAPAR_Cab) and Surface Albedo calculated as bi-hemispheric reflectance (BHR) for diffuse illumination with a reference spectrum for spectral broadband intervals visible wavelengths (VIS, 400-700 nm), near-infrared wavelengths (NIR, 700-2500 nm), and for the combined shortwave range (SW, 400-2500 nm), as well as directional-hemispherical reflectance (DHR) for the same spectral broadbands, computed for local solar noon. These additional variables are not validated.Further details about the data, including validation and intercomparison with similar datasets, can be found in the PDF documentation.',

169  Collection("L4_MSLA_V2.0") id: 'L4_MSLA_V2.0', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0', instrument: 'RA-2,RA,RA,POSEIDON-2,SSALT', platform: 'CryoSat-2,Envisat,ERS-1,ERS-2,Jason-1,SARAL,TOPEX/POSEIDON', keywords: 'altika,cryosat-2,dif10,earth-science>oceans>sea-surface-topography>sea-surface-height,earth-science>spectral/engineering>radar,envisat,ers-1,ers-2,esa-cci,gfo,gfo-ra,jason-1,jason-2,l4-msla-v2.0,orthoimagery,poseidon-2,poseidon-3,ra,ra-2,saral,sea-level,sla,ssalt,topex/poseidon', license: 'other', abstract: 'As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a multi-satellite merged time series of monthly gridded Sea Level Anomalies (SLA) has been produced from satellite altimeter measurements. The Sea Level Anomaly grids have been calculated after merging the altimetry mission measurements together into monthly grids, with a spatial resolution of 0.25 degrees. This version of the product is Version 2.0. The following DOI can be used to reference the monthly Sea Level Anomaly product: DOI: 10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org',

170  Collection("LAKE_PRODUCTS_L3S_V1.0") id: 'LAKE_PRODUCTS_L3S_V1.0', title: 'ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.0', instrument: 'RA-2,POSEIDON-2,AVHRR-3,OLCI,OLCI,SSALT', platform: 'Envisat,Jason-1,JASON-3,Metop-A,OrbView-2,SARAL,Sentinel-3A,Sentinel-3B,TOPEX/POSEIDON', keywords: 'aatsr,altika,atsr-2,avhrr-3,cci,dif10,earth-science>atmosphere,earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake,earth-science>spectral/engineering>infrared-wavelengths,earth-science>spectral/engineering>radar,ecv,envisat,esa,gfo,jason-1,jason-2,jason-3,lake-products-l3s-v1.0,lakes,meris,metop-a,modis,mss,olci,oli,orbview-2,orthoimagery,poseidon-2,poseidon-3,ra,ra-2,saral,sentinel-3a,sentinel-3b,sral,ssalt,tm,topex/poseidon,viirs', license: 'other', abstract: 'This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:• Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.• Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.• Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.',

171  Collection("LAKE_PRODUCTS_L3S_V1.1") id: 'LAKE_PRODUCTS_L3S_V1.1', title: 'ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.1', instrument: 'MODIS,RA-2,RA,POSEIDON-2,TM,TM,ETM,OLI,AVHRR-3,AVHRR-3,SeaWiFS,C-SAR,OLCI,OLCI,MODIS,SSALT', platform: 'AQUA,Envisat,ERS-2,Jason-1,JASON-3,Landsat-4,Landsat-5,Landsat-7,Landsat-8,Metop-A,Metop-B,OrbView-2,SARAL,Sentinel-1A,Sentinel-3A,Sentinel-3B,TERRA,TOPEX/POSEIDON', keywords: 'aatsr,altika,aqua,atsr-2,avhrr-3,c-sar,cci,collecte-localisation-satellites,day,dif10,earth-science>agriculture>soils,earth-science>atmosphere,earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake,earth-science>spectral/engineering>infrared-wavelengths,earth-science>spectral/engineering>radar,ecv,envisat,ers-2,esa,etm,etm+,gfo,h2o-geomatics,jason-1,jason-2,jason-3,laboratoire-detudes-en-geodesie-et-oceanographie-spatiales,lake-products-l3s-v1.1,lakes,landsat-4,landsat-5,landsat-7,landsat-8,level-3,level-3s,merged,meris,metop-a,metop-b,modis,mss,multiple-lake-products,olci,oli,orbview-2,orthoimagery,plymouth-marine-laboratory,poseidon-2,poseidon-3,ra,ra-2,saral,seawifs,sentinel-1a,sentinel-3a,sentinel-3b,snpp,sral,ssalt,terra,tm,topex/poseidon,university-of-reading,viirs', license: 'other', abstract: 'This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:• Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.• Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.• Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.',

172  Collection("LAKE_PRODUCTS_L3S_V2.0.2") id: 'LAKE_PRODUCTS_L3S_V2.0.2', title: 'ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.2', keywords: 'cci,earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake,ecv,esa,lake-products-l3s-v2.0.2,lakes,orthoimagery', license: 'other', abstract: 'This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset. The five thematic climate variables included in this dataset are:• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z',

173  Collection("LAKE_PRODUCTS_L3S_V2.1") id: 'LAKE_PRODUCTS_L3S_V2.1', title: 'ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.1', instrument: 'ETM,C-SAR,MSI', platform: 'Envisat,Jason-1,TOPEX/POSEIDON,TERRA,Landsat-8,Landsat-5,JASON-3,Landsat-7,Metop-A,Metop-B,Landsat-4,SARAL,Sentinel-3A,Sentinel-3B,Sentinel-1,Sentinel-2', keywords: 'c-sar,cci,dif10,earth-science>atmosphere,earth-science>biosphere>ecosystems>freshwater-ecosystems>lake/pond>montane-lake,earth-science>spectral/engineering>radar,earth-science>spectral/engineering>visible-wavelengths,ecv,envisat,ers,esa,etm,etm+,jason-1,jason-2,jason-3,lake-products-l3s-v2.1,lakes,landsat-4,landsat-5,landsat-7,landsat-8,metop,metop-a,metop-b,modis-aqua,modis-terra,msi,orthoimagery,saral,sentinel-1,sentinel-2,sentinel-2-msi,sentinel-3a,sentinel-3b,terra,topex/poseidon', license: 'other', abstract: 'This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.1.0 of the dataset.The six thematic climate variables included in this dataset are:• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).• Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.Satellite sensors associated with the thematic climate variables are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z',

174  Collection("LAND_COVER_MAPS_A01_AFRICA_HISTORICAL_V1.2_GEOTIFF") id: 'LAND_COVER_MAPS_A01_AFRICA_HISTORICAL_V1.2_GEOTIFF', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Africa (Eastern Sahel region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a01-africa-historical-v1.2-geotiff,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Africa, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty products.They cover the geographic range (3.5°N – 16.3°N; 27.0°E – 43.3°E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.',

175  Collection("LAND_COVER_MAPS_A01_AFRICA_STATIC_V1.2_GEOTIFF_HRLC10") id: 'LAND_COVER_MAPS_A01_AFRICA_STATIC_V1.2_GEOTIFF_HRLC10', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Africa (Eastern Sahel region) at 10m spatial resolution for 2019 in Geotiff format, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a01-africa-static-v1.2-geotiff-hrlc10,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Africa, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. This consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (0.1°S – 18.1°N; 9.9°E – 43.3°E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.',

176  Collection("LAND_COVER_MAPS_A02_AMAZONIA_HISTORICAL_V1.2_GEOTIFF") id: 'LAND_COVER_MAPS_A02_AMAZONIA_HISTORICAL_V1.2_GEOTIFF', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Amazonia (Eastern Amazonas region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a02-amazonia-historical-v1.2-geotiff,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Amazonia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty products.They cover the geographic range (23.6°S – 11.7°S; 46.7°W – 62.1°W).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.',

177  Collection("LAND_COVER_MAPS_A02_AMAZONIA_STATIC_V1.2_GEOTIFF_HRLC10") id: 'LAND_COVER_MAPS_A02_AMAZONIA_STATIC_V1.2_GEOTIFF_HRLC10', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Amazonia (Eastern Amazonas region) at 10m spatial resolution for 2019 in Geotiff format, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a02-amazonia-static-v1.2-geotiff-hrlc10,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Amazonia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (23.6°S – 0°S; 42.9°W – 62.1°W).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.',

178  Collection("LAND_COVER_MAPS_A03_SIBERIA_HISTORICAL_V1.2_GEOTIFF") id: 'LAND_COVER_MAPS_A03_SIBERIA_HISTORICAL_V1.2_GEOTIFF', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps in Siberia (Norther Siberia region) at 30m spatial resolution in GeoTiff format, 1990-2019, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a03-siberia-historical-v1.2-geotiff,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Siberia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.3) Associated uncertainty productsThey cover the geographic range (59.4°N – 73.9°N, 64.8°E – 87.4°E).The data are provided as both GeoTIFF tiles following the Sentinel 2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.',

179  Collection("LAND_COVER_MAPS_A03_SIBERIA_STATIC_V1.2_GEOTIFF_HRLC10") id: 'LAND_COVER_MAPS_A03_SIBERIA_STATIC_V1.2_GEOTIFF_HRLC10', title: 'ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Maps in Siberia (Northern Siberia region) at 10m spatial resolution for 2019 in Geotiff format, v1.2', keywords: 'cci,earth-science>land-surface>land-use/land-cover,high-resolution,land-cover,land-cover-maps-a03-siberia-static-v1.2-geotiff-hrlc10,orthoimagery', license: 'other', abstract: 'This dataset contains high resolution (HR) land cover (LC) maps of a subregion of Siberia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:1) HRLC10: High Resolution Land Cover Maps at 10m spatial resolution for year 2019 (also referred to as static maps).2) Associated uncertainty products.They cover the geographic range (51.3°N – 75.7°N; 64.4°E – 93.4°E).The data are provided as both GeoTIFF tiles following the Sentinel-2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as static maps.',

180  Collection("LAND_COVER_MAPS_V2.0.7") id: 'LAND_COVER_MAPS_V2.0.7', title: 'ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7', instrument: 'MERIS,VG1,VG2', platform: 'Envisat,SPOT-4,SPOT-5,PROBA-V', keywords: 'cci,dif10,earth-science>biosphere>vegetation,earth-science>land-surface>land-use/land-cover,envisat,land-cover,land-cover-maps-v2.0.7,meris,orthoimagery,proba-v,spot-4,spot-5,vegetation,vegetation-1,vegetation-2,vg1,vg2,vã©gã©tation-p', license: 'other', abstract: 'As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).',

181  Collection("LIMB_PROFILES_L3_ACE_FTS_SCISAT_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_ACE_FTS_SCISAT_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'ACE-FTS', platform: 'SCISAT-1', keywords: 'ace,ace-fts,ace-fts-scisat,atmospheric-chemistry-experiment---fourier-transform-spectrometer,cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-ace-fts-scisat-monthly-zonal-mean-v0001,orthoimagery,ozone,ozone-limb-profile,scisat,scisat-1,scisat-1/ace', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for ACE in 2008.',

182  Collection("LIMB_PROFILES_L3_GOMOS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_GOMOS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'GOMOS', platform: 'Envisat', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,environmental-satellite,envisat,esa,global-ozone-monitoring-by-occultation-of-stars,gomos,gomos-envisat,level-3,limb-profiles-l3-gomos-envisat-monthly-zonal-mean-v0001,orthoimagery,ozone,ozone-limb-profile', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc” contains monthly zonal mean data for GOMOS in 2008.',

183  Collection("LIMB_PROFILES_L3_MEGRIDOP_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_MEGRIDOP_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone_cci): MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001', keywords: 'cci,climate-change-initiative,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,limb-profiles-l3-megridop-monthly-zonal-mean-v0001,orthoimagery,ozone,profiles', license: 'other', abstract: 'This dataset comprises the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura. The merged dataset was generated as a contribution to the European Space Agency Climate Change Initiative Ozone project (Ozone_cci). The period of this merged time series of ozone profiles is from late 2001 until the end of 2022.The monthly mean gridded ozone profiles and deseasonalised anomalies are provided in the altitude range from 10 to 50 km in bins of 10 degree latitude x 20 degree longitude. For more details please see the associated readme file and Sofieva, V. F., Szeląg, M., Tamminen, J., Kyrölä, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G. P., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M. and Retscher, C.: Measurement report: regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys., 21(9), 6707–6720, doi:10.5194/acp-21-6707-2021, 2021',

184  Collection("LIMB_PROFILES_L3_MERGED_MERGED_MONTHLY_ZONAL_MEAN_V0002") id: 'LIMB_PROFILES_L3_MERGED_MERGED_MONTHLY_ZONAL_MEAN_V0002', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 2', keywords: 'cci,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-merged-merged-monthly-zonal-mean-v0002,merged,orthoimagery,ozone,ozone-limb-profile', license: 'other', abstract: 'This dataset consists of a gridded composite of limb ozone profile data, combining data from a range of instruments. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean. The merged monthly zonal mean data (MMZM) include merged ozone profiles in 10° latitude zones for each month, on the ozone-CCI pressure grid from 250 hPa to 1 hPa, and the parameters which characterize the uncertainty of the merged profiles. In Phase I of the ESA CCI Programme, the dataset has been created for 2 years, 2007 and 2008.The merged monthly zonal mean data are structured into monthly netcdf files with self-explanatory names. For example, the file “ESACCI-OZONE-L3-LP-MERGED-MZM-200801-fv0002.nc” contains merged monthly zonal mean data for January 2008. In addition to the variables of the merged data, the profiles from individual instruments with their uncertainty parameters are also included (for the altitude range 250-1 hPa used in data merging).',

185  Collection("LIMB_PROFILES_L3_MERGED_MERGED_SEMI_MONTHLY_MEAN_V0002") id: 'LIMB_PROFILES_L3_MERGED_MERGED_SEMI_MONTHLY_MEAN_V0002', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): Merged Level 3 Limb Ozone Semi-Monthly Mean Profiles, Version 2', keywords: 'cci,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-merged-merged-semi-monthly-mean-v0002,orthoimagery,ozone,ozone-limb-profile,smm', license: 'other', abstract: 'This dataset consists of a gridded composite of limb ozone profile data, combining data from a range of instruments. The Merged Semi-Monthly Mean (MSMM) dataset is created using measurements from limb sensors participating in Ozone_cci project, for years 2007-2008.First, the ozone profiles from individual instruments are averaged in 10° x 20° latitude-longitude zones over half-month time intervals, and then merged.The merged semi-monthly mean ozone profiles are structured into yearly netcdf files with self-explanatory names. For example, the file “ESACCI-OZONE-L3-LP-SMM-2008-fv0002.nc” contains the semi-monthly mean ozone profiles for January 2008. In addition to the variables of the merged data, the profiles from individual instruments with their uncertainty parameters are also included (for the altitude range 250-1 hPa used in data merging).',

186  Collection("LIMB_PROFILES_L3_MERGED_SAGE_CCI_OMPS_MONTHLY_ZONAL_MEAN_V0002") id: 'LIMB_PROFILES_L3_MERGED_SAGE_CCI_OMPS_MONTHLY_ZONAL_MEAN_V0002', title: 'ESA Ozone Climate Change Initiative (Ozone_cci): Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles, v0002', keywords: 'cci,climate-change-initiative,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,limb-profiles-l3-merged-sage-cci-omps-monthly-zonal-mean-v0002,orthoimagery,ozone,profiles', license: 'other', abstract: 'The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP; POAM III on SPOT 4 and SAGE III on ISS. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The long-term SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023).The merged SAGE-CCI-OMPS+ dataset consists of deseasonalized anomalies of ozone and ozone concentrations in 10 degree latitude bands from 90S to 90N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to December 2021.',

187  Collection("LIMB_PROFILES_L3_MIPAS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_MIPAS_ENVISAT_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): MIPAS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'MIPAS', platform: 'Envisat', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,environmental-satellite,envisat,esa,level-3,limb-profiles-l3-mipas-envisat-monthly-zonal-mean-v0001,michelson-interferometer-for-passive-atmospheric-sounding,mipas,mipas-envisat,orthoimagery,ozone,ozone-limb-profile', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the MIPAS instrument on the ENVISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file "ESACCI-OZONE-L3-LP-MIPAS_ENVISAT-MZM-2008-fv0001.nc“ contains monthly zonal mean data for MIPAS in 2008.',

188  Collection("LIMB_PROFILES_L3_OSIRIS_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_OSIRIS_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'OSIRIS', platform: 'ODIN', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-osiris-monthly-zonal-mean-v0001,odin,opticalâ spectrograph-and-infraredâ imagerâ system,orthoimagery,osiris,osiris-odin,ozone,ozone-limb-profile', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for OSIRIS in 2008.',

189  Collection("LIMB_PROFILES_L3_SCIAMACHY_ENVISAT_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_SCIAMACHY_ENVISAT_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'SCIAMACHY', platform: 'Envisat', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,environmental-satellite,envisat,esa,level-3,limb-profiles-l3-sciamachy-envisat-monthly-zonal-mean-v0001,orthoimagery,ozone,ozone-limb-profile,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy,sciamachy-envisat', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for SCIAMACHY in 2008.',

190  Collection("LIMB_PROFILES_L3_SMR_ODIN_544_6_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_SMR_ODIN_544_6_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR (544.6 GHz) Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'SMR', platform: 'ODIN', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-smr-odin-544-6-monthly-zonal-mean-v0001,mzm,odin,orthoimagery,ozone,ozone-limb-profile,smr,sub-millimetre-radiometer', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR (544.6 GHz) instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-MZM-SMR_ODIN-544_6_GHz-2008-fv0001.nc” contains monthly zonal mean data for ODIN/SMR at 544.6GHz in 2008.',

191  Collection("LIMB_PROFILES_L3_SMR_ODIN_MONTHLY_ZONAL_MEAN_V0001") id: 'LIMB_PROFILES_L3_SMR_ODIN_MONTHLY_ZONAL_MEAN_V0001', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1', instrument: 'SMR', platform: 'ODIN', keywords: 'cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,esa,level-3,limb-profiles-l3-smr-odin-monthly-zonal-mean-v0001,odin,orthoimagery,ozone,ozone-limb-profile,smr,smr-odin,sub-millimetre-radiometer', license: 'other', abstract: 'This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SMR_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for ODIN/SMR in 2008.',

192  Collection("LT_ANALYSIS_L4_V01.1") id: 'LT_ANALYSIS_L4_V01.1', title: 'ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.1', instrument: 'AVHRR-3,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AATSR,ATSR-2,ATSR-1,AVHRR-2,AVHRR-3,AVHRR-3', platform: 'NOAA-15,NOAA-12,Metop-A,Metop-B,NOAA-17,Envisat,ERS-2,ERS-1,NOAA-14,NOAA-18,NOAA-16', keywords: 'aatsr,advanced-along-track-scanning-radiometer,advanced-very-high-resolution-radiometer---1,along-track-scanning-radiometer---1,along-track-scanning-radiometer---2,atsr,atsr-1,atsr-2,avhrr,avhrr-2,avhrr-3,cci,day,dif10,earth-science>oceans>ocean-temperature>sea-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,environmental-satellite,envisat,ers,ers-1,ers-2,esacci-sst,level-4,lt-analysis-l4-v01.1,metop,metop-a,metop-b,noaa-12,noaa-14,noaa-15,noaa-16,noaa-17,noaa-18,noaa-4th,noaa-5th,orthoimagery,ostia,sea-surface-temperature,sea-water-temperature,sst', license: 'other', abstract: 'The ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI) dataset accurately maps the surface temperature of the global oceans over the period 1991 to 2010, using observations from many satellites. The data provides an independently quantified SST to a quality suitable for climate research.The ESA SST CCI Analysis Long Term Product consists of daily, spatially complete fields of sea surface temperature (SST), obtained by combining the orbit data from the AVHRR and ATSR ESA SST CCI Long Term Products, using optimal interpolation to provide SSTs where there were no measurements. These data cover the period between 09/1991 and 12/2010.The Version 1.1 data is an update of the Version 1.0 dataset.Version 1.0 of this dataset is cited in: Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S. and Donlon, C. (2014), Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geoscience Data Journal. doi: 10.1002/gdj3.20',

193  Collection("MERIS_ALAMO_L2_V2.2") id: 'MERIS_ALAMO_L2_V2.2', title: 'ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 2 aerosol products from MERIS (ALAMO algorithm), Version 2.2', instrument: 'MERIS', platform: 'Envisat', keywords: 'aerosol,aerosol-optical-depth,cci,dif10,earth-science>atmosphere>aerosols,environmental-satellite,envisat,esa,hygeos,icare,imaging-spectrometer,level-2,level-2-pre-processing,medium-spectral-resolution,meris,meris-alamo-l2-v2.2,meris-envisat,orthoimagery,satellite-orbit-frequency', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 2 aerosol products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation.',

194  Collection("MERIS_ALAMO_L3_V2.2") id: 'MERIS_ALAMO_L3_V2.2', title: 'ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2', instrument: 'MERIS', platform: 'Envisat', keywords: 'aerosol,aerosol-optical-depth,cci,day,dif10,earth-science>atmosphere>aerosols,environmental-satellite,envisat,esa,hygeos,icare,imaging-spectrometer,level-3,level-3c,medium-spectral-resolution,meris,meris-alamo-l3-v2.2,meris-envisat,month,orthoimagery', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation.',

195  Collection("METOPA_AVHRR_L3C_0.01_V1.10_DAILY") id: 'METOPA_AVHRR_L3C_0.01_V1.10_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10', keywords: 'avhrr-metop-a,cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,metopa-avhrr-l3c-0.01-v1.10-daily,orthoimagery', license: 'other', abstract: 'This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st March 2007 and ends on 15th November 2021. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

196  Collection("MS_UVAI_L3_V1.7") id: 'MS_UVAI_L3_V1.7', title: 'ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7', instrument: 'OMI,SCIAMACHY,GOME,GOME-2,GOME-2,TOMS', platform: 'Aura,Envisat,ERS-2,Metop-A,Metop-B,Nimbus-7', keywords: 'aerosol,aura,cci,dif10,earth-science>atmosphere>aerosols,envisat,ers-2,esa,gome,gome-2,metop-a,metop-b,ms-uvai-l3-v1.7,nimbus-7,omi,orthoimagery,sciamachy,toms', license: 'other', abstract: 'The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7. L3 products are provided as daily and monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation.',

197  Collection("MTSAT_JAMI_L3C_V1.00_MONTHLY") id: 'MTSAT_JAMI_L3C_V1.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multi-Functional Transport Satellite (MTSAT) level 3C (L3C) product (2009-2015), version 1.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,land-surface-temperature,mtsat,mtsat-jami-l3c-v1.00-monthly,orthoimagery', license: 'other', abstract: 'This dataset contains monthly averaged land surface temperatures (LST) and their uncertainty estimates from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite series (MTSAT1 and 2, also known as Himiwari-6 and 7). The original surface temperatures are generated every 3 hours and in this L3C product are monthly averaged at each time step and distributed on a regular latitude-longitude grid with a resolution of 0.05ºx0.05º. The coverage is limited to land surfaces within the MTSAT disk, which encompasses Australia and part of Asia.The LSTs in this dataset are estimated from infrared measurements using a single channel algorithm, and, therefore, are only available under clear-sky conditions. The quality of single channel algorithms is generally lower than dual channel ones, and users are advised to read the respective Validation Report for more information on the expected quality of these LST estimates.The dataset was produced by the Portuguese Institute for Sea and Atmosphere (IPMA) as part of the ESA Land Surface Temperature Climate Change Initiative. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.',

198  Collection("MULTISENSOR_IRCDR_L3S_0.01_V2.00_DAILY") id: 'MULTISENSOR_IRCDR_L3S_0.01_V2.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,infra-red,land-surface-temperature,multisensor-ircdr-l3s-0.01-v2.00-daily,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

199  Collection("MULTISENSOR_IRCDR_L3S_0.01_V2.00_MONTHLY") id: 'MULTISENSOR_IRCDR_L3S_0.01_V2.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,infra-red,land-surface-temperature,multisensor-ircdr-l3s-0.01-v2.00-monthly,orthoimagery', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

200  Collection("MULTISENSOR_IRCDR_L3S_0.01_V3.00_DAILY") id: 'MULTISENSOR_IRCDR_L3S_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,infra-red,land-surface-temperature,multisensor-ircdr-l3s-0.01-v3.00-daily,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra), to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.This version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024. This dataset provides a daily product, and a separate monthly averaged product also exists. The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include: SLSTR on Sentinel 3A is no longer used, instead data from SLSTR on Sentinel 3B is used from November 2018; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

201  Collection("MULTISENSOR_IRCDR_L3S_0.01_V3.00_MONTHLY") id: 'MULTISENSOR_IRCDR_L3S_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,infra-red,land-surface-temperature,multisensor-ircdr-l3s-0.01-v3.00-monthly,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR); the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. In this dataset, data has been averaged to a monthly grid. A separate daily product is also available.Dataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.This version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024. The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include a change from SLSTR on Sentinel 3A to SLSTR on Sentinel 3B; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

202  Collection("MULTISENSOR_IRMGP_L3S_0.05_V1.00_DAILY") id: 'MULTISENSOR_IRMGP_L3S_0.05_V1.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,multisensor-irmgp-l3s-0.05-v1.00-daily,orthoimagery', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

203  Collection("MULTISENSOR_IRMGP_L3S_0.05_V1.00_MONTHLY") id: 'MULTISENSOR_IRMGP_L3S_0.05_V1.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,multisensor-irmgp-l3s-0.05-v1.00-monthly,orthoimagery', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

204  Collection("NADIR_PROFILES_L3_MERGED_V0002") id: 'NADIR_PROFILES_L3_MERGED_V0002', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Nadir Ozone Profile Merged Data Product, version 2', instrument: 'GOME-2,GOME-2,SCIAMACHY,GOME,OMI', platform: 'Metop-A,Metop-B,Envisat,ERS-2,Aura', keywords: 'aura,cci,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,environmental-satellite,envisat,eos,ers,ers-2,esa,global-monitoring-of-atmospheric-ozone,global-monitoring-of-atmospheric-ozone---2,gome,gome-2,level-3,merged,metop,metop-a,metop-b,month,nadir-profiles-l3-merged-v0002,omi,orthoimagery,ozone,ozone-monitoring-instrument,ozone-nadir-profile,royal-netherlands-meteorological-institute,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy', license: 'other', abstract: 'This dataset contains Level 3 nadir profile ozone data from the ESA Ozone Climate Change Initiative (CCI) project. The Level 3 data are monthly averages on a regular 3D grid derived from level 2 ozone profiles. In this version 2 of the dataset, data are available for 1997 and 2007 and 2008 only, and use data from the GOME instrument on ERS (1997) and the GOME-2 instrument on METOP-A (2007, 2008).',

205  Collection("NOAA20_VIIRS") id: 'NOAA20_VIIRS', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on NOAA-20 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product (2018-2024), version 1.00', keywords: 'canopy,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,land-surface-temperature,noaa20-viirs,orthoimagery,soil,viirs,visible-infrared-imaging-radiometer-suite', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA-20. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the daytime and nighttime NOAA-20 equator crossing times which are 13:25 and 01:25 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. VIIRS achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 5th January 2018 and continues to 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The European Space Agency (ESA) funded the research and development of software to generate these data (ESA grant reference 4000123553/18/I-NB) in addition to funding the production of the data for 2012 to 2023. The data for 2024 and development of software for the production of the ICDR is funded by the UK Natural Environment Research Council (NERC grant reference number NE/X019071/1 Earth Observation Climate Information Service).',

206  Collection("NOT_AVAILABLE") id: 'NOT_AVAILABLE', title: 'ESA Land Cover Climate Change Initiative (Land_Cover_cci): MERIS Surface Reflectance', instrument: 'MERIS', platform: 'Envisat', keywords: 'atmospheric-conditions,cci,dif10,earth-science>land-surface>land-use/land-cover,earth-science>land-surface>surface-radiative-properties>reflectance,envisat,land-cover,meris,not-available,surface-reflectance', license: 'other', abstract: 'This dataset consists of time series of surface reflectance from the MERIS instrument on the ENVISAT satellite, produced as part of the ESA Land Cover Climate Change Initiative (CCI) project. The time series are a temporal syntheses obtained over a 7-day compositing period, and encompass 13 of the 15 MERIS spectral channels (not including bands 11 and 15). The spatial resolution is 300m for the Full Resolution (FR) data and 1000m for the Reduced Resolution (RR) data.Given the amount and size of the MERIS surface reflectance archive (10 To), the Land Cover CCI team make the data available on request, through your own disks. Please contact contact@esa-landcover-cci.org',

207  Collection("OBS4MIPS_DWD_ESACCI-CLOUD-ATSR2-AATSR-3-0_MON") id: 'OBS4MIPS_DWD_ESACCI-CLOUD-ATSR2-AATSR-3-0_MON', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from ATSR2 and AATSR, version 3', keywords: 'cci,cloud,earth-science>atmosphere>clouds,obs4mips,obs4mips-dwd-esacci-cloud-atsr2-aatsr-3-0-mon,orthoimagery', license: 'other', abstract: 'This dataset provides a version of the Cloud_cci ATSR2-AATSRv3 monthly gridded dataset in Obs4MIPs format. The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on measurements taken by the Along-Track Scanning Radiometer (ATSR-2) on-board the European Remote Sensing Satellite -2 (ERS-2), and by the Advanced Along-Track Scanning Radiometer (AATSR) on-board the Environmental Satellite (Envisat). It contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)',

208  Collection("OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-AM-3-0_MON") id: 'OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-AM-3-0_MON', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-AM), version 3', keywords: 'cci,cloud,earth-science>atmosphere>clouds,obs4mips,obs4mips-dwd-esacci-cloud-avhrr-am-3-0-mon,orthoimagery', license: 'other', abstract: 'This dataset provides a version of the Cloud_cci AVHRR-AMv3 monthly gridded dataset in Obs4MIPs format. The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime morning (AM) satellite NOAA-12,-15,-17, and the EUMETSAT Metop-A satellite. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)',

209  Collection("OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-PM-3-0_MON") id: 'OBS4MIPS_DWD_ESACCI-CLOUD-AVHRR-PM-3-0_MON', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-PM), version 3', keywords: 'cci,cloud,earth-science>atmosphere>clouds,obs4mips,obs4mips-dwd-esacci-cloud-avhrr-pm-3-0-mon,orthoimagery', license: 'other', abstract: 'This dataset provides a version of the Cloud_cci AVHRR-PMv3 monthly gridded dataset in Obs4MIPs format. The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime afternoon (PM) satellite NOAA-7,-9,11,-14,-16,-18,-19 satellites. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. This particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. Individual files are provided covering seven cloud variables:Cloud area fraction in atmospheric layer (clCCI);Atmospheric cloud ice content (clivi);Cloud area fraction (cltCCI);Liquid water cloud area fraction in atmospheric layer(clwCCI);Liquid water cloud area fraction (clwtCCI);Atmosphere mass content of cloud condensed water (clwvi);Air pressure at cloud top (pctCCI)',

210  Collection("OBS4MIPS_UREADING_ESA-CCI-SST-V2-1_MON_TOS_GN_V20201130") id: 'OBS4MIPS_UREADING_ESA-CCI-SST-V2-1_MON_TOS_GN_V20201130', title: 'ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Obs4MIPS monthly-averaged sea surface temperature data, v2.1', keywords: 'earth-science>oceans>ocean-temperature>sea-surface-temperature,esa-climate-change-initiative,obs4mips-ureading-esa-cci-sst-v2-1-mon-tos-gn-v20201130,orthoimagery,sst', license: 'other', abstract: 'This dataset contains monthly 1 degree averages of sea surface temperature data in Obs4MIPS format, from the European Space Agency (ESA)'s Climate Change Initiatve (CCI) Sea Surface Temperature (SST) v2.1 analysis.The data covers the period from 1981-2017, with the data from 1981 to 2016 coming from the Sea Surface Temperature (SST) project of the ESA CCI project. The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).This particular product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x',

211  Collection("PERMAFROST_EXTENT_L4_AREA4_PP_V03.0") id: 'PERMAFROST_EXTENT_L4_AREA4_PP_V03.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0', instrument: 'MODIS,MERIS,AVHRR-3,AVHRR-3,AVHRR-3,MODIS', platform: 'AQUA,Envisat,NOAA-15,NOAA-16,NOAA-17,TERRA,PROBA-V', keywords: 'aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,envisat,meris,modis,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,permafrost-extent,permafrost-extent-l4-area4-pp-v03.0,proba-v,sar-x,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).Case A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year.',

212  Collection("PERMAFROST_EXTENT_L4_AREA4_PP_V04.0") id: 'PERMAFROST_EXTENT_L4_AREA4_PP_V04.0', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0', instrument: 'MODIS,MERIS,MODIS,AVHRR-3,AVHRR-3,AVHRR-3', platform: 'AQUA,Envisat,TERRA,NOAA-16,NOAA-15,NOAA-17,PROBA-V', keywords: 'aqua,asar,avhrr-3,cci,dif10,earth-science>agriculture>soils>permafrost,envisat,meris,modis,modis-terra,noaa-15,noaa-16,noaa-17,orthoimagery,permafrost,permafrost-extent,permafrost-extent-l4-area4-pp-v04.0,proba-v,sar-x,spot,terra', license: 'other', abstract: 'This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.',

213  Collection("PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_ANTARCTICA") id: 'PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_ANTARCTICA', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for Antarctica, v5.0', instrument: 'MODIS,MODIS', platform: 'AQUA,TERRA', keywords: 'aqua,cci,dif10,earth-science>agriculture>soils>permafrost,earth-science>land-surface>frozen-ground>permafrost,level-4,modis,orthoimagery,permafrost,permafrost-extent,permafrost-extent-l4-area4-pp-v05.0-antarctica,terra', license: 'other', abstract: 'This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers Antarctica (south of 60°S) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.e.g. ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA27_PP-****-fv05.0.ncCase B: It covers Antarctica (south of 60°S) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the yeare.g. ESACCI-PERMAFROST-L4-PFR-ERA5_MODISLST_BIASCORRECTED-AREA27_PP-****-fv05.0.nc',

214  Collection("PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE") id: 'PERMAFROST_EXTENT_L4_AREA4_PP_V05.0_NORTHERN_HEMISPHERE', title: 'ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v5.0', instrument: 'MODIS,MERIS,C-SAR,MSI,MODIS', platform: 'AQUA,Envisat,Sentinel-1A,Sentinel-2,TERRA,PROBA-V', keywords: 'aqua,c-sar,cci,dif10,earth-science>agriculture>soils,earth-science>agriculture>soils>permafrost,earth-science>biosphere>vegetation,earth-science>land-surface>frozen-ground>permafrost,envisat,level-4,meris,modis,msi,msi-(sentinel-2),orthoimagery,permafrost,permafrost-extent,permafrost-extent-l4-area4-pp-v05.0-northern-hemisphere,proba-v,sar-c-(sentinel-1),sentinel-1a,sentinel-2,sentinel-2-msi,sentinel-2a,terra,vegetation', license: 'other', abstract: 'This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). Case A: It covers the Northern Hemisphere (north of 30°N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. e.g. ESACCI-PERMAFROST-L4-PFR-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.ncCase B: It covers the Northern Hemisphere (north of 30°N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.e.g. ESACCI-PERMAFROST-L4-PFR-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc',

215  Collection("PFT_V2.0.8") id: 'PFT_V2.0.8', title: 'ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Functional Types (PFT) Dataset, v2.0.8', keywords: 'cci,earth-science>land-surface>land-use/land-cover,land-cover,orthoimagery,pft,pft-v2.0.8', license: 'other', abstract: 'This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project. The data provides yearly data, and initially covers the time period from 1992 to 2020. It is anticipated that the dataset will be updated annually going forward.The PFT v2.0.8 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice."Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).All terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.The plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. This dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.This study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci.',

216  Collection("PHASE-2_L3C_MERIS-AATSR_V2.0") id: 'PHASE-2_L3C_MERIS-AATSR_V2.0', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): MERIS+AATSR monthly gridded cloud properties, Version 2.0', instrument: 'AATSR', platform: 'Envisat', keywords: 'aatsr,advanced-along-track-scanning-radiometer,cci,cloud,clouds,dif10,earth-science>atmosphere>clouds,environmental-satellite,envisat,esa,freie-universitaet-berlin,imaging-spectrometer,level-3,level-3c,medium-spectral-resolution,meris,month,multiple-cloud-products,orthoimagery,phase-2-l3c-meris-aatsr-v2.0', license: 'other', abstract: 'The Cloud_cci MERIS+AATSR dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MERIS and AATSR (onboard ENVISAT) measurements and contains a variety of cloud properties which were derived employing the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) retrieval system. The core cloud properties contained in the Cloud_cci MERIS+AATSR dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.',

217  Collection("PHASE-2_L3C_MODIS-AQUA_V2.0") id: 'PHASE-2_L3C_MODIS-AQUA_V2.0', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-AQUA monthly gridded cloud properties, version 2.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,cloud,clouds,deutscher-wetterdienst,dif10,earth-science>atmosphere>clouds,eos,esa,level-3,level-3c,moderate-resolution-imaging-spectroradiometer,modis,modis-aqua,month,multiple-cloud-products,orthoimagery,phase-2-l3c-modis-aqua-v2.0', license: 'other', abstract: 'The Cloud_cci MODIS-Aqua dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Aqua) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Aqua dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.',

218  Collection("PHASE-2_L3C_MODIS-TERRA_V2.0") id: 'PHASE-2_L3C_MODIS-TERRA_V2.0', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0', instrument: 'MODIS', platform: 'TERRA', keywords: 'cci,cloud,clouds,deutscher-wetterdienst,dif10,earth-science>atmosphere>clouds,eos,esa,level-3,level-3c,moderate-resolution-imaging-spectroradiometer,modis,modis-terra,month,multiple-cloud-products,orthoimagery,phase-2-l3c-modis-terra-v2.0,terra', license: 'other', abstract: 'The Cloud_cci MODIS-Terra dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Terra) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Terra dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures.',

219  Collection("RANDOLPH_GLACIER_INVENTORY_GRIDDED_V5.0") id: 'RANDOLPH_GLACIER_INVENTORY_GRIDDED_V5.0', title: 'ESA Glaciers Climate Change Initiative (Glaciers CCI): Randolph Glacier Inventory gridded data product, v5.0', keywords: 'cci,earth-science>terrestrial-hydrosphere>glaciers/ice-sheets>glaciers,esa,glaciers,orthoimagery,randolph-glacier-inventory-gridded-v5.0,rgi', license: 'other', abstract: 'The Randolph Glacier Inventory (RGI 5.0) is a global inventory of glacier outlines. It is supplemental to the Global Land Ice Measurements from Space initiative (GLIMS). Production of the RGI was motivated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Future updates will be made to the RGI and the GLIMS Glacier Database in parallel during a transition period. As all these data are incorporated into the GLIMS Glacier Database and as download tools are developed to obtain GLIMS data in the RGI data format, the RGI will evolve into a downloadable subset of GLIMS, offering complete one-time coverage, version control, and a standard set of attributes.The product provided here is a converted raster version of the Randolph Glacier Inventory (RGI 5.0) data, provided by the ESA Climate Change Initiative (CCI) Glaciers project. The CCI Glaciers project is one of a number of contributors to the RGI 5.0 dataset. For more details, and for a complete list of contributors, please see the RGI 5.0 Technical Report in the Documentation section below.The following reference is recommended when citing RGI version 5.0:Arendt, A., A. Bliss, T. Bolch, J.G. Cogley, A.S. Gardner, J.-O. Hagen, R. Hock, M. Huss, G. Kaser, C. Kienholz, W.T. Pfeffer, G. Moholdt, F. Paul, V. Radić, L. Andreassen, S. Bajracharya, N.E. Barrand, M. Beedle, E. Berthier, R. Bhambri, I. Brown, E. Burgess, D. Burgess, F. Cawkwell, T. Chinn, L. Copland, B. Davies, H. De Angelis, E. Dolgova, L. Earl, K. Filbert, R. Forester, A.G. Fountain, H. Frey, B. Giffen, N. Glasser, W.Q. Guo, S. Gurney, W. Hagg, D. Hall, U.K. Haritashya, G. Hartmann, C. Helm, S. Herreid, I. Howat, G. Kapustin, T. Khromova, M. König, J. Kohler, D. Kriegel, S. Kutuzov, I. Lavrentiev, R. LeBris, S.Y. Liu, J. Lund, W. Manley, R. Marti, C. Mayer, E.S. Miles, X. Li, B. Menounos, A. Mercer, N. Mölg, P. Mool, G. Nosenko, A. Negrete, T. Nuimura, C. Nuth, R. Pettersson, A. Racoviteanu, R. Ranzi, P. Rastner, F. Rau, B. Raup, J. Rich, H. Rott, A. Sakai, C. Schneider, Y. Seliverstov, M. Sharp, O. Sigurðsson, C. Stokes, R.G. Way, R. Wheate, S. Winsvold, G. Wolken, F. Wyatt, N. Zheltyhina, 2015, Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 5.0. Global Land Ice Measurements from Space, Boulder Colorado, USA. Digital Media.',

220  Collection("RD_RD-ALTI_V1.0") id: 'RD_RD-ALTI_V1.0', title: 'ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0', keywords: 'altimeter,cci,orthoimagery,rd-rd-alti-v1.0,river-discharge', license: 'other', abstract: 'This dataset comprises the altimetry-based river discharge (RD-ALTI) Climate Research Data Package (CRDP), derived from nadir radar altimeter missions by the ESA CCI River Discharge precursor project (RD_cci). It provides long-term satellite river discharge (RD) time series at specified locations (defined in the "Selection of river basins" document, available at https://climate.esa.int/documents/2189/D2_CCI-Discharge-0004-RP_WP2_v1-1.pdf). River discharge (in m3/s) corresponds to the water volume passing through the river cross-section per unit of time. In this dataset, it is computed from a rating curve applied to long-term satellite altimeter water surface elevation (WSE) from https://catalogue.ceda.ac.uk/uuid/c5f0aa806ec444b4a4209b49efc4bb65. The rating curve is obtained by fitting the relationship between in-situ discharge and altimeter WSE with a power law following a Bayesian approach.',

221  Collection("RD_RD-COMBINED_V1.0") id: 'RD_RD-COMBINED_V1.0', title: 'ESA River Discharge Climate Change Initiative (RD_cci): Combined river discharge product, v1.0', keywords: 'cci,orthoimagery,rd-rd-combined-v1.0,river-discharge', license: 'other', abstract: 'This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci).These river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series (called CM) and the water level product derived by radar altimeters (called Altimetry), and Level-3, in which the river discharge products obtained from altimeters and multispectral sensors are used. The river discharges are derived following several approaches:1) L2 Merged river discharge:a) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution. b) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry2) L3 Merged river discharge:a) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approachb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approachc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach',

222  Collection("RD_RD-MULTI_V1.2") id: 'RD_RD-MULTI_V1.2', title: 'ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2', keywords: 'cci,multispectral,orthoimagery,rd-rd-multi-v1.2,river-discharge', license: 'other', abstract: 'This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci). These river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series. These multi-mission, multispectral time series are also referred to as CM. The river discharges are derived following several approaches:Calibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;Calibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;Uncalibrated CM approach – CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.',

223  Collection("SCFG_AATSR_V1.0") id: 'SCFG_AATSR_V1.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from AATSR (2002 – 2012), version 1.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-aatsr-v1.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. The global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2002 – 2012. The SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.',

224  Collection("SCFG_ATSR-2_V1.0") id: 'SCFG_ATSR-2_V1.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from ATSR-2 (1995 – 2003), version 1.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-atsr-2-v1.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. The global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 1995 – 2003. The SCFG product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. The retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.',

225  Collection("SCFG_AVHRR_MERGED_V2.0") id: 'SCFG_AVHRR_MERGED_V2.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-avhrr-merged-v2.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.',

226  Collection("SCFG_AVHRR_SINGLE_V3.0") id: 'SCFG_AVHRR_SINGLE_V3.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-avhrr-single-v3.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1979-2022. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.',

227  Collection("SCFG_AVHRR_SINGLE_V4.0") id: 'SCFG_AVHRR_SINGLE_V4.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0', instrument: 'AVHRR-3,AVHRR-3,AVHRR,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR,AVHRR-2,AVHRR,AVHRR-2,TIROS-N', platform: 'Metop-A,Metop-B,Metop-C,NOAA-10,NOAA-11,NOAA-12,NOAA-14,NOAA-16,NOAA-17,NOAA-18,NOAA-6,NOAA-7,NOAA-8,NOAA-9,TIROS-N', keywords: 'avhrr,avhrr-2,avhrr-3,cci,dif10,earth-science>climate-indicators>cryospheric-indicators>snow-cover,earth-science>spectral/engineering>infrared-wavelengths,esa,level-3c,metop-a,metop-b,metop-c,noaa-10,noaa-11,noaa-12,noaa-14,noaa-16,noaa-17,noaa-18,noaa-6,noaa-7,noaa-8,noaa-9,orthoimagery,scfg-avhrr-single-v4.0,snow,snow-cover-fraction,tiros-n', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information. The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.',

228  Collection("SCFG_CRYOCLIM_V1.0") id: 'SCFG_CRYOCLIM_V1.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0', instrument: 'AVHRR,AVHRR,SMMR', platform: 'Metop-A,Metop-B,Nimbus-7', keywords: 'avhrr,cci,dif10,earth-science>climate-indicators>cryospheric-indicators>snow-cover,earth-science>cryosphere>snow/ice>snow-cover,earth-science>spectral/engineering>microwave,esa,metop-a,metop-b,nimbus-7,orthoimagery,scfg-cryoclim-v1.0,sensor-fusion,smmr,snow,snow-cover,snow-cover-fraction,ssm/i,ssmis', license: 'other', abstract: 'This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 – 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).',

229  Collection("SCFG_MODIS_V2.0") id: 'SCFG_MODIS_V2.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-modis-v2.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2020. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.Improvements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.',

230  Collection("SCFG_MODIS_V3.0") id: 'SCFG_MODIS_V3.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfg-modis-v3.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2022. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.Compared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/), the following improvements were applied for the generation of the SCFG CRDP v3.0: 1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; 2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;5) the time series, starting in February 2000, was extended from December 2020 to December 2022;6) two additional layers are provided for each daily product: • the sensor zenith angle in degree per pixel; the image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel. The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.',

231  Collection("SCFV_AATSR_V1.0") id: 'SCFV_AATSR_V1.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from AATSR (2002 – 2012), version 1.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-aatsr-v1.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2002 – 2012. The SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.',

232  Collection("SCFV_ATSR-2_V1.0") id: 'SCFV_ATSR-2_V1.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from ATSR-2 (1995 – 2003), version 1.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-atsr-2-v1.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel.The global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1995 – 2003. The SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.',

233  Collection("SCFV_AVHRR_MERGED_V2.0") id: 'SCFV_AVHRR_MERGED_V2.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-avhrr-merged-v2.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.',

234  Collection("SCFV_AVHRR_SINGLE_V3.0") id: 'SCFV_AVHRR_SINGLE_V3.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-avhrr-single-v3.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1979-2022. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.',

235  Collection("SCFV_AVHRR_SINGLE_V4.0") id: 'SCFV_AVHRR_SINGLE_V4.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2023), version 4.0', instrument: 'AVHRR-3,AVHRR-3,AVHRR,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR,AVHRR-2,AVHRR,AVHRR-2,TIROS-N', platform: 'Metop-A,Metop-B,Metop-C,NOAA-10,NOAA-11,NOAA-12,NOAA-14,NOAA-16,NOAA-17,NOAA-18,NOAA-19,NOAA-6,NOAA-7,NOAA-8,NOAA-9,TIROS-N', keywords: 'avhrr,avhrr-2,avhrr-3,cci,dif10,earth-science>climate-indicators>cryospheric-indicators>snow-cover,earth-science>spectral/engineering>infrared-wavelengths,esa,level-3c,metop-a,metop-b,metop-c,noaa-10,noaa-11,noaa-12,noaa-14,noaa-16,noaa-17,noaa-18,noaa-19,noaa-6,noaa-7,noaa-8,noaa-9,orthoimagery,scfv-avhrr-single-v4.0,snow,snow-cover-fraction,tiros-n', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. RMSE is retrieved from a statistical model and added as pixel-wise information.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.',

236  Collection("SCFV_MODIS_V2.0") id: 'SCFV_MODIS_V2.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2020), version 2.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-modis-v2.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 – 2020. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.Improvements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.',

237  Collection("SCFV_MODIS_V3.0") id: 'SCFV_MODIS_V3.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,scfv-modis-v3.0,snow,snow-cover-fraction', license: 'other', abstract: 'This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 – 2022. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.Compared to the SCFV CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b/), the following improvements were applied for the generation of the SCFV CRDP v3.0: 1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; 2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;5) the time series, starting in February 2000, was extended from December 2020 to December 2022;6) two additional layers are provided for each daily product: • the sensor zenith angle in degree per pixel;• the image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.',

238  Collection("SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.0") id: 'SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.0', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.0', keywords: 'antarctic,arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-concentration-l3c-esmr-25km-v1.0', license: 'other', abstract: 'This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the first version of the product, v1.0.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.',

239  Collection("SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.1") id: 'SEA_ICE_CONCENTRATION_L3C_ESMR_25KM_V1.1', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Nimbus-5 ESMR Sea Ice Concentration, version 1.1', keywords: 'antarctic,arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-concentration-l3c-esmr-25km-v1.1', license: 'other', abstract: 'This dataset provides Sea Ice Concentration (SIC) for the polar regions, derived from the Nimbus-5 Electrical Scanning Microwave Radiometer (ESMR), which operated between 1972 and 1977. It is processed with an algorithm using the single channel ESMR data (19.35 GHz), and has been gridded at 25 km grid spacing. This is the second version of the product, v1.1.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.',

240  Collection("SEA_ICE_CONCENTRATION_L4_AMSR_25KM_V2.1") id: 'SEA_ICE_CONCENTRATION_L4_AMSR_25KM_V2.1', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1', instrument: 'AMSR-E,AMSR2', platform: 'AQUA,GCOM-W1', keywords: 'advanced-microwave-scanning-radiometer-2,advanced-microwave-scanning-radiometer-for-earth-observation-from-space-(amsr-e),amsr-25kmease2,amsr-e,amsr2,amsre,antarctic,aqua,arctic,cci,day,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,earth-science>oceans>sea-ice>sea-ice-concentration,earth-science>spectral/engineering>microwave,eos,esa,gcom,gcom-w1,level-4,norwegian-meteorological-institute,orthoimagery,sea-ice,sea-ice-concentration,sea-ice-concentration-l4-amsr-25km-v2.1', license: 'other', abstract: 'The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 50 km grid spacing is also available.',

241  Collection("SEA_ICE_CONCENTRATION_L4_AMSR_50KM_V2.1") id: 'SEA_ICE_CONCENTRATION_L4_AMSR_50KM_V2.1', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50km grid spacing, version 2.1', instrument: 'AMSR-E,AMSR2', platform: 'AQUA,GCOM-W1', keywords: 'advanced-microwave-scanning-radiometer-2,advanced-microwave-scanning-radiometer-for-earth-observation-from-space-(amsr-e),amsr-50kmease2,amsr-e,amsr2,amsre,antarctic,aqua,arctic,cci,day,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,earth-science>oceans>sea-ice>sea-ice-concentration,earth-science>spectral/engineering>microwave,eos,esa,gcom,gcom-w1,level-4,norwegian-meteorological-institute,orthoimagery,sea-ice,sea-ice-concentration,sea-ice-concentration-l4-amsr-50km-v2.1', license: 'other', abstract: 'The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 25km grid spacing is also available.',

242  Collection("SEA_ICE_CONCENTRATION_L4_SSMI_SSMIS_12.5KM_V3.0") id: 'SEA_ICE_CONCENTRATION_L4_SSMI_SSMIS_12.5KM_V3.0', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): High(er) Resolution Sea Ice Concentration Climate Data Record Version 3 (SSM/I and SSMIS)', keywords: 'antarctic,arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-concentration-l4-ssmi-ssmis-12.5km-v3.0', license: 'other', abstract: 'This climate data record of sea ice concentration (SIC) is obtained using passive microwave satellite data from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) over the polar regions (Arctic and Antarctic). The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, 4) an optimal hybrid sea ice concentration algorithm, and 5) pan-sharpening of the SIC fields using the near-90 GHz imagery channels. This dataset was generated by the ESA Climate Change Initiative (CCI+) Sea Ice Phase 1 project. This dataset is an enhanced-resolution version of the EUMETSAT Ocean and Sea Ice Satellite Application Facility Global Sea Ice Concentration Climate Data Record (OSI SAF OSI-450-a CDR) over the period 1991-2020.',

243  Collection("SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_NH") id: 'SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0', instrument: 'SIRAL', platform: 'CryoSat-2', keywords: 'arctic,cci,cryosat-2,cryosat-programme,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,esa,level-2,level-2-pre-processing,orthoimagery,satellite-orbit-frequency,sea-ice,sea-ice-thickness,sea-ice-thickness-l2p-cryosat2-v2.0-nh,siral', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.',

244  Collection("SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_SH") id: 'SEA_ICE_THICKNESS_L2P_CRYOSAT2_V2.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0', instrument: 'SIRAL', platform: 'CryoSat-2', keywords: 'antarctic,cci,cryosat-2,cryosat-programme,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,esa,level-2,level-2-pre-processing,orthoimagery,satellite-orbit-frequency,sea-ice,sea-ice-thickness,sea-ice-thickness-l2p-cryosat2-v2.0-sh,siral', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

245  Collection("SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_NH") id: 'SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0', keywords: 'arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l2p-cryosat2-v3.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020.',

246  Collection("SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_SH") id: 'SEA_ICE_THICKNESS_L2P_CRYOSAT2_V3.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0', keywords: 'antarctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l2p-cryosat2-v3.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

247  Collection("SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_NH") id: 'SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0', instrument: 'RA-2', platform: 'Envisat', keywords: 'arctic,cci,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,environmental-satellite,envisat,esa,level-2,level-2-pre-processing,orthoimagery,ra-2,radar-altimeter-2,satellite-orbit-frequency,sea-ice,sea-ice-thickness,sea-ice-thickness-l2p-envisat-v2.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.',

248  Collection("SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_SH") id: 'SEA_ICE_THICKNESS_L2P_ENVISAT_V2.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v2.0', instrument: 'RA-2', platform: 'Envisat', keywords: 'antarctic,cci,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,environmental-satellite,envisat,esa,level-2,level-2-pre-processing,orthoimagery,ra-2,radar-altimeter-2,satellite-orbit-frequency,sea-ice,sea-ice-thickness,sea-ice-thickness-l2p-envisat-v2.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

249  Collection("SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_NH") id: 'SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0', keywords: 'arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l2p-envisat-v3.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.',

250  Collection("SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_SH") id: 'SEA_ICE_THICKNESS_L2P_ENVISAT_V3.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0', keywords: 'antarctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l2p-envisat-v3.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

251  Collection("SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_NH") id: 'SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0', instrument: 'SIRAL', platform: 'CryoSat-2', keywords: 'arctic,cci,cryosat-2,cryosat-programme,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,esa,level-3,level-3c,month,orthoimagery,sea-ice,sea-ice-thickness,sea-ice-thickness-l3c-cryosat2-v2.0-nh,siral', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April.',

252  Collection("SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_SH") id: 'SEA_ICE_THICKNESS_L3C_CRYOSAT2_V2.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0', instrument: 'SIRAL', platform: 'CryoSat-2', keywords: 'antarctic,cci,cryosat-2,cryosat-programme,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,esa,level-3,level-3c,month,orthoimagery,sea-ice,sea-ice-thickness,sea-ice-thickness-l3c-cryosat2-v2.0-sh,siral', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

253  Collection("SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_NH") id: 'SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0', keywords: 'arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l3c-cryosat2-v3.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Data are only available for the NH winter months, October - April.',

254  Collection("SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_SH") id: 'SEA_ICE_THICKNESS_L3C_CRYOSAT2_V3.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0', keywords: 'antarctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l3c-cryosat2-v3.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

255  Collection("SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_NH") id: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0', instrument: 'RA-2', platform: 'Envisat', keywords: 'arctic,cci,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,environmental-satellite,envisat,esa,level-3,level-3c,month,orthoimagery,ra-2,radar-altimeter-2,sea-ice,sea-ice-thickness,sea-ice-thickness-l3c-envisat-v2.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April.',

256  Collection("SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_SH") id: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V2.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0', instrument: 'RA-2', platform: 'Envisat', keywords: 'antarctic,cci,dif10,earth-science>cryosphere>sea-ice,earth-science>oceans>sea-ice,environmental-satellite,envisat,esa,level-3,level-3c,month,orthoimagery,ra-2,radar-altimeter-2,sea-ice,sea-ice-thickness,sea-ice-thickness-l3c-envisat-v2.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

257  Collection("SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_NH") id: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_NH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0', keywords: 'arctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l3c-envisat-v3.0-nh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April.',

258  Collection("SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH") id: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', title: 'ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0', keywords: 'antarctic,cci,earth-science>cryosphere>sea-ice,esa,orthoimagery,sea-ice,sea-ice-thickness-l3c-envisat-v3.0-sh', license: 'other', abstract: 'This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.',

259  Collection("SENTINEL3A_SLSTR_L3C_0.01_V3.00_DAILY") id: 'SENTINEL3A_SLSTR_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00', instrument: 'SLSTR', platform: 'Sentinel-3', keywords: 'cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery,sentinel-3,sentinel3a-slstr-l3c-0.01-v3.00-daily,slstr', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

260  Collection("SENTINEL3A_SLSTR_L3C_0.01_V3.00_MONTHLY") id: 'SENTINEL3A_SLSTR_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00', instrument: 'SLSTR', platform: 'Sentinel-3', keywords: 'cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery,sentinel-3,sentinel3a-slstr-l3c-0.01-v3.00-monthly,slstr', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

261  Collection("SENTINEL3B_SLSTR_L3C_0.01_V3.00_DAILY") id: 'SENTINEL3B_SLSTR_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00', instrument: 'SLSTR', platform: 'Sentinel-3', keywords: 'cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery,sentinel-3,sentinel3b-slstr-l3c-0.01-v3.00-daily,slstr', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

262  Collection("SENTINEL3B_SLSTR_L3C_0.01_V3.00_MONTHLY") id: 'SENTINEL3B_SLSTR_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00', instrument: 'SLSTR', platform: 'Sentinel-3', keywords: 'cci,dif10,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery,sentinel-3,sentinel3b-slstr-l3c-0.01-v3.00-monthly,slstr', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage runs from December 2018 to December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

263  Collection("SNPP_VIIRS") id: 'SNPP_VIIRS', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from VIIRS (Visible Infrared Imaging Radiometer Suite) on Suomi National Polar-orbiting Partnership (SNPP), level 3 collated (L3C) global product (2012-2024), version 1.00', keywords: 'canopy,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,earth-science>spectral/engineering>infrared-wavelengths,land-surface-temperature,orthoimagery,snpp-viirs,soil,viirs,visible-infrared-imaging-radiometer-suite', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening SNPP equator crossing times which are 13:25 and 01:25 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. VIIRS achieves full Earth coverage twice per day. LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 19th January 2012 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.The European Space Agency (ESA) funded the research and development of software to generate these data (ESA grant reference 4000123553/18/I-NB) in addition to funding the production of the data for 2012 to 2023. The data for 2024 and development of software for the production of the ICDR is funded by the UK Natural Environment Research Council (NERC grant reference number NE/X019071/1 Earth Observation Climate Information Service).',

264  Collection("SSMI_SSMIS_L3C_V2.33") id: 'SSMI_SSMIS_L3C_V2.33', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020), v2.33', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,land-surface-temperature,orthoimagery,ssmi-ssmis-l3c-v2.33', license: 'other', abstract: 'MW-LST is a data record of land surface temperature (LST) derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020). The data record has been produced by the company Estellus working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci). Compared with the remaining infrared LST data records of the LST_cci, the spatial resolution of the MW-LST is coarser, and the associated retrieval errors are larger. However, it offers LST estimates for clear-sky and cloudy conditions, therefore complementing the IR LST data records, which can only provide LST for clear skies. The data record is temporally and spatially complete, although in rare occasions some data can be missing due to missing observations, e.g., due to satellite maintenance operations or anomalous behavior. The data record is provided on a regular grid of 0.25x0.25 degrees, saved as daily, monthly, and yearly netcdf files. The reader is referred to the LST_cci website for more information about how the data record was derived, and how to use the data and associated quality flags and estimated uncertainty.This version of the data is v2.33. It fixes an issue that was found with the variable 'lst_unc_time_correction' in the previous v2.23, but is otherwise identical.',

265  Collection("SWE_MERGED_V2.0") id: 'SWE_MERGED_V2.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2020), version 2.0', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,snow,swe,swe-merged-v2.0', license: 'other', abstract: 'This dataset contains v2.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2020/05. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2020, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.This version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/), the grid spacing is reduced from 25 km to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added.The ESA CCI phased product development framework allowed for a systematic analysis of these changes to the input data and snow density parameterization that occurred between v1 and v2 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v2 improved 18% (0.1) and 12% (5mm), respectively, relative to v1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is removed in v2.',

266  Collection("SWE_MERGED_V3.1") id: 'SWE_MERGED_V3.1', title: 'ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.1', keywords: 'cci,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,orthoimagery,snow,swe,swe-merged-v3.1', license: 'other', abstract: 'This dataset contains v3.1 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2022/05 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October – May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland. The product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/), resampled to the 12.5km EASE-Grid 2.0. A background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC). Changes from v2.0 and v3.0v3.1 applies spatially and temporally varying snow densities within the SWE retrieval instead of during post-processing. The dry snow detection algorithm as well as the snow masking in post-production have also been updated. The time series has been extended from snow_cci version 2 by two years from 2020 to 2022. In comparison with in situ snow courses, the correlation and RMSE of v3.1 improved by 0.014 and 0.6 mm, respectively, relative to v2.0. The timing of peak snow mass is shifted two weeks later compared to v1.0 and reduction in peak snow mass presented in v2.0 is removed in v3.1. Differences between v3.0 and v.3.1 are minor, the resampling from 12.5km EASE-Grid 2.0 to the final 0.1 resolution grid has been changed for v.3.1 resulting in improved peak snow mass estimation.',

267  Collection("SWE_MERGED_V4.0") id: 'SWE_MERGED_V4.0', title: 'ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2023), version 4.0', instrument: 'SSM/I,SSM/I,SSM/I,SMMR', platform: 'DMSP 5D-2/F8,DMSP 5D-2/F11,DMSP 5D-2/F13,DMSP 5D-3/F17,DMSP 5D-3/F18,Nimbus-7', keywords: 'cci,dif10,dmsp-5d-2/f11,dmsp-5d-2/f13,dmsp-5d-2/f8,dmsp-5d-3/f17,dmsp-5d-3/f18,dmsp-f08,dmsp-f11,dmsp-f13,dmsp-f17,dmsp-f18,earth-science>climate-indicators>cryospheric-indicators>snow-cover,esa,level-3c,merged,nimbus-7,orthoimagery,smmr,snow,ssm/i,ssmi/s,swe,swe-merged-v4.0', license: 'other', abstract: 'This dataset contains v4.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.Snow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2023/12 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October – May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland.The product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/), resampled to the 12.5km EASE-Grid 2.0.A background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.The dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute (FMI) is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC).Changes from v3.1 The time series has been extended from version 3.1 by one year, to 2023. The retrieval algorithm has been modified to prioritize morning overpass (descending) data over evening (ascending) data. This change affects the SWE retrieval for all years except 1988–1991. Data from those years is from the F08 satellite, which has a reversed orbit, and evening (descending) data is prioritized, as in earlier versions of the SWE retrieval. Snow masking in post-production now uses CryoClim SCE data for 35–40° latitude and −30–3° longitude. Elsewhere, the baseline snow mask and CryoClim are combined so that any pixel flagged by either is marked snow-covered, as in v3.1. The pixel-wise uncertainty model has been updated for North America using extensive snow course data.',

268  Collection("TCWV-LAND_L3_V3.2_0.05DEG_DAILY") id: 'TCWV-LAND_L3_V3.2_0.05DEG_DAILY', title: 'ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.05 degree resolution, version 3.2', keywords: 'cci,earth-science>atmosphere>atmospheric-water-vapor,orthoimagery,tcwv,tcwv-land-l3-v3.2-0.05deg-daily,water-vapour', license: 'other', abstract: 'This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.',

269  Collection("TCWV-LAND_L3_V3.2_0.05DEG_MONTHLY") id: 'TCWV-LAND_L3_V3.2_0.05DEG_MONTHLY', title: 'ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.05 degree resolution, version 3.2', keywords: 'cci,earth-science>atmosphere>atmospheric-water-vapor,orthoimagery,tcwv,tcwv-land-l3-v3.2-0.05deg-monthly,water-vapour', license: 'other', abstract: 'This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.',

270  Collection("TCWV-LAND_L3_V3.2_0.5DEG_DAILY") id: 'TCWV-LAND_L3_V3.2_0.5DEG_DAILY', title: 'ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.5 degree resolution, version 3.2', keywords: 'cci,earth-science>atmosphere>atmospheric-water-vapor,orthoimagery,tcwv,tcwv-land-l3-v3.2-0.5deg-daily,water-vapour', license: 'other', abstract: 'This dataset consists of daily total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.',

271  Collection("TCWV-LAND_L3_V3.2_0.5DEG_MONTHLY") id: 'TCWV-LAND_L3_V3.2_0.5DEG_MONTHLY', title: 'ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.5 degree resolution, version 3.2', keywords: 'cci,earth-science>atmosphere>atmospheric-water-vapor,orthoimagery,tcwv,tcwv-land-l3-v3.2-0.5deg-monthly,water-vapour', license: 'other', abstract: 'This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.',

272  Collection("TERRA_MODIS_L3C_0.01_V3.00_DAILY") id: 'TERRA_MODIS_L3C_0.01_V3.00_DAILY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00', keywords: 'not-defined,orthoimagery,terra-modis-l3c-0.01-v3.00-daily', license: 'other', abstract: 'This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 24th February 2000 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

273  Collection("TERRA_MODIS_L3C_0.01_V3.00_MONTHLY") id: 'TERRA_MODIS_L3C_0.01_V3.00_MONTHLY', title: 'ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00', keywords: 'cci,earth-science>land-surface>surface-thermal-properties>land-surface-temperature,esa,land-surface-temperature,orthoimagery,terra-modis-l3c-0.01-v3.00-monthly', license: 'other', abstract: 'This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.The monthly dataset starts from March 2000 and ends December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.',

274  Collection("TIMESERIES_SLB_ELEMENTS_V2.2") id: 'TIMESERIES_SLB_ELEMENTS_V2.2', title: 'ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.2', keywords: 'cci,esa,orthoimagery,sea-level-budget-closure,timeseries-slb-elements-v2.2', license: 'other', abstract: 'This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget:(a) global mean sea level(b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion(c) the mass contribution to global mean sea level(d) the global glaciers contribution (excluding Greenland and Antarctica)(e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution(f) the Antarctic Ice Sheet contribution(g) the contribution from changes in land water storage (including snow cover).The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.For some elements, more than one time series are given, as a result of different assessments from different data sources and methods.Data and methods underlying the time series are as follows:(a) satellite altimetry analysis by the Sea Level CCI project.(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.(d) results from a global glacier model.(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.(g) results from the WaterGAP global hydrological model.Version 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE.',

275  Collection("TOTAL_COLUMNS_L3_MERGED_V0100") id: 'TOTAL_COLUMNS_L3_MERGED_V0100', title: 'ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01', instrument: 'GOME-2,GOME-2,SCIAMACHY,GOME', platform: 'Metop-A,Metop-B,Envisat,ERS-2', keywords: 'cci,day,deutsches-zentrum-fuer-luft--und-raumfahrt,dif10,earth-science>atmosphere>atmospheric-chemistry>oxygen-compounds>atmospheric-ozone,environmental-satellite,envisat,ers,ers-2,esa,global-monitoring-of-atmospheric-ozone,global-monitoring-of-atmospheric-ozone---2,gome,gome-2,level-3,level-3s,merged,metop,metop-a,metop-b,orthoimagery,ozone,ozone-total-column,scanningâ imagingâ absorption-spectrometer-forâ atmospheric-chartography,sciamachy,total-columns-l3-merged-v0100', license: 'other', abstract: 'This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011.',

276  Collection("V02.31_30DAYS") id: 'V02.31_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v2.31, for 2010 to 2019', instrument: 'MIRAS', platform: 'SMOS,SAC-D,SMAP', keywords: 'aquarius,cci,dif10,earth-science>spectral/engineering>microwave,esa-climate-change-initiative,miras,orthoimagery,sac-d,sea-surface-salinity,smap,smos,v02.31-30days', license: 'other', abstract: 'The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.',

277  Collection("V02.31_7DAYS") id: 'V02.31_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019', instrument: 'MIRAS', platform: 'SMOS,SAC-D,SMAP', keywords: 'aquarius,cci,dif10,earth-science>spectral/engineering>microwave,esa-climate-change-initiative,miras,orthoimagery,sac-d,sea-surface-salinity,smap,smos,v02.31-7days', license: 'other', abstract: 'The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.This dataset contains Sea Surface Salinity (SSS) v2.31 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).An overview paper about CCI SSS is now published:Boutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.An updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.',

278  Collection("V03.21_30DAYS") id: 'V03.21_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v03.21-30days', license: 'other', abstract: 'The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.This dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided. For more information, see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.',

279  Collection("V03.21_7DAYS") id: 'V03.21_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v03.21, for 2010 to 2020', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v03.21-7days', license: 'other', abstract: 'The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.This dataset contains Sea Surface Salinity (SSS) v03.21 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page).Compared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag.',

280  Collection("V04.41_GLOBALV4.41_30DAYS") id: 'V04.41_GLOBALV4.41_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a global grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-globalv4.41-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).',

281  Collection("V04.41_GLOBALV4.41_7DAYS") id: 'V04.41_GLOBALV4.41_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a global grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-globalv4.41-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a 0.25 degree grid and 1 day of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset also covers a longer period (Jan 2010-Oct 2022).',

282  Collection("V04.41_NHV4.41_30DAYS") id: 'V04.41_NHV4.41_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-nhv4.41-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a NH polar 25km EASE (Equal Area Scaleable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).',

283  Collection("V04.41_NHV4.41_7DAYS") id: 'V04.41_NHV4.41_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-nhv4.41-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a NH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).',

284  Collection("V04.41_SHV4.41_30DAYS") id: 'V04.41_SHV4.41_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-shv4.41-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).',

285  Collection("V04.41_SHV4.41_7DAYS") id: 'V04.41_SHV4.41_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v04.41-shv4.41-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.Compared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).',

286  Collection("V05.5_GLOBALV5.5_30DAYS") id: 'V05.5_GLOBALV5.5_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-globalv5.5-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

287  Collection("V05.5_GLOBALV5.5_7DAYS") id: 'V05.5_GLOBALV5.5_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-globalv5.5-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a 0.25 degree grid and 1 day of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

288  Collection("V05.5_NHV5.5_30DAYS") id: 'V05.5_NHV5.5_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-nhv5.5-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a NH polar 25km EASE (Equal Area Scaleable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

289  Collection("V05.5_NHV5.5_7DAYS") id: 'V05.5_NHV5.5_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-nhv5.5-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a NH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

290  Collection("V05.5_SHV5.5_30DAYS") id: 'V05.5_SHV5.5_30DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-shv5.5-30days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 15 days of time sampling. This product is also available separately on a regular lat/lon grid. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

291  Collection("V05.5_SHV5.5_7DAYS") id: 'V05.5_SHV5.5_7DAYS', title: 'ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Southern Hemisphere on a 25km EASE grid, v5.5, for 2010 to 2023', keywords: 'cci,esa-climate-change-initiative,orthoimagery,sea-surface-salinity,v05.5-shv5.5-7days', license: 'other', abstract: 'This dataset contains Sea Surface Salinity (SSS) v5.5 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a SH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.',

292  Collection("V3_RELEASE_ALTIMETER_L2P") id: 'V3_RELEASE_ALTIMETER_L2P', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetry, L2P product, version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,earth-science>oceans>ocean-waves>significant-wave-height,orthoimagery,sea-state,significant-wave-height,v3-release-altimeter-l2p', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2022021 (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).',

293  Collection("V3_RELEASE_ALTIMETER_L3_V3.0") id: 'V3_RELEASE_ALTIMETER_L3_V3.0', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing daily merged multi-mission along-track significant wave height from altimetry, L3 product, version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,earth-science>oceans>ocean-waves>significant-wave-height,orthoimagery,sea-state,significant-wave-height,v3-release-altimeter-l3-v3.0', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021.The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).',

294  Collection("V3_RELEASE_ALTIMETER_L4_V3.0") id: 'V3_RELEASE_ALTIMETER_L4_V3.0', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission monthly gridded significant wave height from altimetry, L4 product, version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,earth-science>oceans>ocean-waves>significant-wave-height,orthoimagery,sea-state,significant-wave-height,v3-release-altimeter-l4-v3.0', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies.This dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization.The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).',

295  Collection("V3_RELEASE_INSITU_MICROSEISM_V1.0") id: 'V3_RELEASE_INSITU_MICROSEISM_V1.0', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci): Seismic spectrograms from broadband seismometers, release version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,integrated-sea-state-parameters,issp,orthoimagery,sar,sea-state,v3-release-insitu-microseism-v1.0', license: 'other', abstract: 'This dataset provides microseism data, produced as part of the ESA Sea State Climate Change Initiative (Sea_State_cci). This microseism dataset is version 1.0, and forms part of the v3 release from the ESA Sea State Climate Change Initiative.Microseism data is a new addition to this CCI Sea State version 3 Dataset. Microseisms are well known to contain rich spectral information about sea states and have been used as early as 1898 to locate typhoons . As such they are unique sources of information on sea states before the advent of surface-following buoys and satellites, with the potential to define long-term trends alongside Voluntary Observing Ship data . Microseisms can be a useful resource when looking for the occurrence of wave events or investigating trends, in particular in regions where no in situ data is available. The data here are spectra of vertical ground displacement at all the available land-based stations from 3 global seismic networks (Geoscope, IRIS USGS and IRIS IDA) from 1988 to 2019. So far microseism data was either handled by seismologists with specific data formats or processed by oceanographers with limited knowledge of the measurement (such as instrument corrections); in the frame of the CCI Sea State project, they have been transformed into yearly NetCDF files containing the seismic spectrograms (Power Spectral Density of displacement) for each station using the “LHZ” channel (vertical displacement sampled at 1 Hz). Note that some of these stations have recordings (with different instruments and stored on different media) that go back to the early part of the 20th century, hence their possible importance for extending climate time series to the pre-satellite era in a future release.',

296  Collection("V3_RELEASE_SAR_L2P_ENVISAT_ISSP_V1.1") id: 'V3_RELEASE_SAR_L2P_ENVISAT_ISSP_V1.1', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, release version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,integrated-sea-state-parameters,issp,orthoimagery,sar,sea-state,v3-release-sar-l2p-envisat-issp-v1.1', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from ENVISAT (referred to as SAR Wave Mode onboard ENVISAT Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the ENVISAT Remote Sensing Integrated Sea State Parameter product (version 1.1), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track significant wave height (SWH) measurements at 5km resolution every 100km, processed using the Li et al., 2020 empirical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard ENVISAT Level 2P (L2P) ISSP v3 dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.',

297  Collection("V3_RELEASE_SAR_L2P_SENTINEL1_ISSP_V1.0") id: 'V3_RELEASE_SAR_L2P_SENTINEL1_ISSP_V1.0', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,integrated-sea-state-parameters,issp,orthoimagery,sar,sea-state,v3-release-sar-l2p-sentinel1-issp-v1.0', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from Sentinel-1 (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the Sentinel-1 SAR Remote Sensing Integrated Sea State Parameter product (v1.0), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track primary significant wave height measurements and secondary sea state parameters, calibrated with CMEMS model data and reference in situ measurements at 20km resolution every 100km, processed using the Pleskachevsky et. al., 2021 emprical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard Sentinel-1 Level 2P (L2P) ISSP v3 dataset come from the Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).',

298  Collection("V3_RELEASE_SAR_L2P_SENTINEL1_SWH_V1.0") id: 'V3_RELEASE_SAR_L2P_SENTINEL1_SWH_V1.0', title: 'ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, release version 3.', keywords: 'cci,earth-science>oceans>ocean-waves>sea-state,earth-science>oceans>ocean-waves>significant-wave-height,orthoimagery,sar,sea-state,significant-wave-height,v3-release-sar-l2p-sentinel1-swh-v1.0', license: 'other', abstract: 'The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) significant wave height (SWH) data (referred to as SAR WV onboard Sentinel-1 Level 2P (L2P) SWH data) with a particular focus for use in climate studies. This dataset contains the Sentinel-1 SAR Remote Sensing Significant Wave Height product (version 1.0), which is part of the ESA Sea State CCI Version 3.0 release. This product provides along-track SWH measurements at 20km resolution every 100km, processed using the Quach et al statistical model , separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI dataset v3 come from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B)',

299  Collection("V6.0-RELEASE_CLIMATOLOGY_NETCDF_MONTHLY_V6.0") id: 'V6.0-RELEASE_CLIMATOLOGY_NETCDF_MONTHLY_V6.0', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-climatology-netcdf-monthly-v6.0', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2022.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.',

300  Collection("V6.0-RELEASE_GEOGRAPHIC_NETCDF_ALL_PRODUCTS") id: 'V6.0-RELEASE_GEOGRAPHIC_NETCDF_ALL_PRODUCTS', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-geographic-netcdf-all-products', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)',

301  Collection("V6.0-RELEASE_GEOGRAPHIC_NETCDF_CHLOR_A") id: 'V6.0-RELEASE_GEOGRAPHIC_NETCDF_CHLOR_A', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-geographic-netcdf-chlor-a', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day, monthly and yearly composites) covering the period 1997 - 2022. Note, this chlor_a data is also included in the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)',

302  Collection("V6.0-RELEASE_GEOGRAPHIC_NETCDF_IOP") id: 'V6.0-RELEASE_GEOGRAPHIC_NETCDF_IOP', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-geographic-netcdf-iop', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)',

303  Collection("V6.0-RELEASE_GEOGRAPHIC_NETCDF_KD") id: 'V6.0-RELEASE_GEOGRAPHIC_NETCDF_KD', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-geographic-netcdf-kd', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).',

304  Collection("V6.0-RELEASE_GEOGRAPHIC_NETCDF_RRS") id: 'V6.0-RELEASE_GEOGRAPHIC_NETCDF_RRS', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>cryosphere>snow/ice>reflectance,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,reflectance,v6.0-release-geographic-netcdf-rrs', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).',

305  Collection("V6.0-RELEASE_SINUSOIDAL_NETCDF_ALL_PRODUCTS") id: 'V6.0-RELEASE_SINUSOIDAL_NETCDF_ALL_PRODUCTS', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products) at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,geographic,modis,ocean-colour,orthoimagery,v6.0-release-sinusoidal-netcdf-all-products', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)',

306  Collection("V6.0-RELEASE_SINUSOIDAL_NETCDF_CHLOR_A") id: 'V6.0-RELEASE_SINUSOIDAL_NETCDF_CHLOR_A', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,chlorophyll-a,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,modis,ocean-colour,orthoimagery,sinusoidal,v6.0-release-sinusoidal-netcdf-chlor-a', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the chlorophyll-a data are also included in the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)',

307  Collection("V6.0-RELEASE_SINUSOIDAL_NETCDF_IOP") id: 'V6.0-RELEASE_SINUSOIDAL_NETCDF_IOP', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,modis,ocean-colour,orthoimagery,sinusoidal,v6.0-release-sinusoidal-netcdf-iop', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 6.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Note, the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)',

308  Collection("V6.0-RELEASE_SINUSOIDAL_NETCDF_KD") id: 'V6.0-RELEASE_SINUSOIDAL_NETCDF_KD', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>oceans>ocean-optics>ocean-color,esa,modis,ocean-colour,orthoimagery,sinusoidal,v6.0-release-sinusoidal-netcdf-kd', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).',

309  Collection("V6.0-RELEASE_SINUSOIDAL_NETCDF_RRS") id: 'V6.0-RELEASE_SINUSOIDAL_NETCDF_RRS', title: 'ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection at 4km resolution, Version 6.0', instrument: 'MODIS', platform: 'AQUA', keywords: 'aqua,cci,dif10,earth-science>cryosphere>snow/ice>reflectance,earth-science>oceans>ocean-optics>ocean-color,esa,modis,ocean-colour,orthoimagery,reflectance,sinusoidal,v6.0-release-sinusoidal-netcdf-rrs', license: 'other', abstract: 'The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).',

310  Collection("VERSION3_L3C_ATSR2-AATSR_V3.0") id: 'VERSION3_L3C_ATSR2-AATSR_V3.0', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR monthly gridded cloud properties, version 3.0', instrument: 'AATSR,ATSR-2', platform: 'Envisat,ERS-2', keywords: 'aatsr,atsr-2,cci,cloud,clouds,dif10,earth-science>atmosphere>clouds,envisat,ers-2,esa,orthoimagery,version3-l3c-atsr2-aatsr-v3.0', license: 'other', abstract: 'The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the ATSR2-AATSR L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003. To cite the full dataset, please use the following citation: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003',

311  Collection("VERSION3_L3C_AVHRR-AM_V3.0") id: 'VERSION3_L3C_AVHRR-AM_V3.0', title: 'ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0', instrument: 'AVHRR-3,AVHRR-2,AVHRR-3,AVHRR-3', platform: 'Metop-A,NOAA-12,NOAA-15,NOAA-17', keywords: 'avhrr-2,avhrr-3,cci,cloud,dif10,earth-science>atmosphere>clouds,earth-science>spectral/engineering>infrared-wavelengths,esa,metop-a,noaa-12,noaa-15,noaa-17,orthoimagery,version3-l3c-avhrr-am-v3.0', license: 'other', abstract: 'The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.',

312  Collection("VERSION3_L3C_AVHRR-PM_V3.0") id: 'VERSION3_L3C_AVHRR-PM_V3.0', title: 'ESA Cloud Climate Change Initiative (Cloud_cci): AVHRR-PM monthly gridded cloud properties, version 3.0', instrument: 'AVHRR-2,AVHRR-2,AVHRR-3,AVHRR-3,AVHRR-3,AVHRR-2,AVHRR-2', platform: 'NOAA-11,NOAA-14,NOAA-16,NOAA-18,NOAA-19,NOAA-7,NOAA-9', keywords: 'avhrr-2,avhrr-3,cci,cloud,dif10,earth-science>atmosphere>clouds,earth-science>spectral/engineering>infrared-wavelengths,esa,noaa-11,noaa-14,noaa-16,noaa-18,noaa-19,noaa-7,noaa-9,orthoimagery,version3-l3c-avhrr-pm-v3.0', license: 'other', abstract: 'The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.',

313  Collection("VERTICAL_LAND_MOTIONS_TUM_MAPS_V1") id: 'VERTICAL_LAND_MOTIONS_TUM_MAPS_V1', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): Regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania, v1', keywords: 'earth-science>oceans>sea-surface-topography>sea-surface-height,esa-cci,orthoimagery,sea-level,sla,vertical-land-motions-tum-maps-v1', license: 'other', abstract: 'This dataset contains a regional coastline profile of Vertical Land Motions in Europe and SE Asia/Oceania produced as part of the ESA Climate Change Initiative Sea Level project.Vertical Land Motions have been estimated as the difference between the altimeter coastal sea level v1.1 dataset (available from https://catalogue.ceda.ac.uk/uuid/222cf11f49a94d2da8a6da239df2efc4 ) and tide gauge measurements from the Permanent Service for Mean Sea Level (PMSML) network. Spatial interpolation has allowed the production of a regularly spaced coastline profile of vertical land movements together with their uncertainties.The altimeter input data are from the Jason-1, Jason-2 and Jason-3 missions during the period Jan. 2002 - May 2018.',

314  Collection("WATER_BODIES_V4.0") id: 'WATER_BODIES_V4.0', title: 'ESA Land Cover Climate Change Initiative (Land_Cover_cci): Water Bodies Map, v4.0', instrument: 'ASAR', platform: 'Envisat', keywords: '13-years,advanced-synthetic-aperture-radar,asar,cci,condition-water-(water-bodies),dif10,earth-science>land-surface>land-use/land-cover,envisat,land-cover,level-4,map,orthoimagery,universite-catholique-de-louvain,water-bodies,water-bodies-v4.0', license: 'other', abstract: 'As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced. The CCI WB v4.0 is composed of two layers:1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.This product is delivered at 150 m as a stand-alone product but it is consistent with class "Water Bodies" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water2. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D’Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036',

315  Collection("WL_V1.1") id: 'WL_V1.1', title: 'ESA River Discharge Climate Change Initiative (RD_cci): Nadir radar altimeters Water Level product, v1.1', keywords: 'cci,orthoimagery,river-discharge,water-level,wl-v1.1', license: 'other', abstract: 'This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci). Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications. These river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series have also been produced.',

316  Collection("WV-STRATO_L3S_V3.3") id: 'WV-STRATO_L3S_V3.3', title: 'ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3', keywords: 'cci,esa-climate-change-initiative,orthoimagery,stratospheric-water-vapour,wv-strato-l3s-v3.3', license: 'other', abstract: 'This water vapour (WV) climate data record (CCI WV-strato or WV_cci CDR-3) has been generated within the European Space Agency (ESA) Water Vapour Climate Change Initiative (Water Vapour_cci). CCI WV-strato is a merged product based on a number of WV datasets obtained from limb and solar occultation satellite instruments. CCI WV-strato features zonal monthly mean vertically resolved water vapour in the stratosphere, covers a pressure range from 300 to 0.1 hPa at 5 degree latitudinal resolution, and spans the time period between 1985 and 2019.This version of the data is v3.3.',

317  Collection("XTRACK_ALES_SLA_ENVISAT_SARAL_SLA_V1.1") id: 'XTRACK_ALES_SLA_ENVISAT_SARAL_SLA_V1.1', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): Altimeter along-track high resolution sea level anomalies in some coastal regions from ENVISAT (2002-2010) and SARAL (2013-2016) satellite altimetry, v1.1', keywords: 'esa-cci,orthoimagery,sla,xtrack-ales-sla-envisat-saral-sla-v1.1', license: 'other', abstract: 'This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). The product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. The main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.The Envisat and SARAL/AltiKa missions have the same ground track but the temporal gap between both missions prevents from computing reliable trends during the total period between both missions.This dataset has been produced by the Climate Change Initiative Coastal Sea Level team, within the extension phase of the European Sapce Agency (ESA) Climate Change Initiative.',

318  Collection("XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V2.2") id: 'XTRACK_ALES_SLA_TRENDS_SELECTEDSITES_V2.2', title: 'ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2', instrument: 'POSEIDON-2', platform: 'Jason-1,JASON-3', keywords: 'centre-national-de-la-recherche-scientific,centre-national-detudes-spatiales,dif10,earth-science>oceans>sea-surface-topography>sea-surface-height,earth-science>spectral/engineering>radar,esa-cci,european-space-agency,indicator,jason-1,jason-2,jason-3,laboratoire-detudes-en-geodesie-et-oceanographie-spatiales,mean-sea-level-trends,merged,month,orthoimagery,poseidon-2,poseidon-3,poseidon-3b,sea-level,sla,xtrack-ales-sla-trends-selectedsites-v2.2', license: 'other', abstract: 'This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856). The dataset should be cited as: Cazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z - Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w',

Search data

With one of the collections listed above, you can search for data.

products = dag.search(
    provider="fedeo_ceda",
    start="2008-01-01",
    end="2009-12-31",
    collection="SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH",
)
products

SearchResult (20)
0  EOProduct(id=f4e5c7465abb04639aefd4b6d19af7b47273480f, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'f4e5c7465abb04639aefd4b6d19af7b47273480f', properties["start_datetime"]: '2008-01-01T00:00:00', properties["end_datetime"]: '2008-01-31T23:59:59.999999', properties: (8) { datetime: '2008-01-01T00:00:00', end_datetime: '2008-01-31T23:59:59.999999', id: 'f4e5c7465abb04639aefd4b6d19af7b47273480f', start_datetime: '2008-01-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f4e5c7465abb04639aefd4b6d19af7b47273480f?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:25', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0.nc', } geometry
1  EOProduct(id=6b8dbf4b92760b8825e9c9e2ea1a20dd890be527, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '6b8dbf4b92760b8825e9c9e2ea1a20dd890be527', properties["start_datetime"]: '2008-02-01T00:00:00', properties["end_datetime"]: '2008-02-29T23:59:59.999999', properties: (8) { datetime: '2008-02-01T00:00:00', end_datetime: '2008-02-29T23:59:59.999999', id: '6b8dbf4b92760b8825e9c9e2ea1a20dd890be527', start_datetime: '2008-02-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200802-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/6b8dbf4b92760b8825e9c9e2ea1a20dd890be527?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:25', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200802-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200802-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200802-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200802-fv3p0.nc', } geometry
2  EOProduct(id=ed443e8091eaf3b93c8b3ee7c23fc1db011e95f5, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'ed443e8091eaf3b93c8b3ee7c23fc1db011e95f5', properties["start_datetime"]: '2008-03-01T00:00:00', properties["end_datetime"]: '2008-03-31T23:59:59.999999', properties: (8) { datetime: '2008-03-01T00:00:00', end_datetime: '2008-03-31T23:59:59.999999', id: 'ed443e8091eaf3b93c8b3ee7c23fc1db011e95f5', start_datetime: '2008-03-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200803-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/ed443e8091eaf3b93c8b3ee7c23fc1db011e95f5?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:26', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200803-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200803-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200803-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200803-fv3p0.nc', } geometry
3  EOProduct(id=0259d1d7cd752b2a752a1abd533ce2cbf60be5de, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '0259d1d7cd752b2a752a1abd533ce2cbf60be5de', properties["start_datetime"]: '2008-04-01T00:00:00', properties["end_datetime"]: '2008-04-30T23:59:59.999999', properties: (8) { datetime: '2008-04-01T00:00:00', end_datetime: '2008-04-30T23:59:59.999999', id: '0259d1d7cd752b2a752a1abd533ce2cbf60be5de', start_datetime: '2008-04-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200804-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/0259d1d7cd752b2a752a1abd533ce2cbf60be5de?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:26', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200804-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200804-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200804-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200804-fv3p0.nc', } geometry
4  EOProduct(id=f95eb41dd18104adda2d5c5d71504f8c45fe419b, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'f95eb41dd18104adda2d5c5d71504f8c45fe419b', properties["start_datetime"]: '2008-05-01T00:00:00', properties["end_datetime"]: '2008-05-31T23:59:59.999999', properties: (8) { datetime: '2008-05-01T00:00:00', end_datetime: '2008-05-31T23:59:59.999999', id: 'f95eb41dd18104adda2d5c5d71504f8c45fe419b', start_datetime: '2008-05-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200805-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f95eb41dd18104adda2d5c5d71504f8c45fe419b?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:26', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200805-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200805-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200805-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200805-fv3p0.nc', } geometry
5  EOProduct(id=10a1f8170b4c79408f2b6a68c828a90972934b01, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '10a1f8170b4c79408f2b6a68c828a90972934b01', properties["start_datetime"]: '2008-06-01T00:00:00', properties["end_datetime"]: '2008-06-30T23:59:59.999999', properties: (8) { datetime: '2008-06-01T00:00:00', end_datetime: '2008-06-30T23:59:59.999999', id: '10a1f8170b4c79408f2b6a68c828a90972934b01', start_datetime: '2008-06-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200806-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/10a1f8170b4c79408f2b6a68c828a90972934b01?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:26', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200806-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200806-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200806-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200806-fv3p0.nc', } geometry
6  EOProduct(id=94d764c032a73052a990c5851721accc75f07f89, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '94d764c032a73052a990c5851721accc75f07f89', properties["start_datetime"]: '2008-07-01T00:00:00', properties["end_datetime"]: '2008-07-31T23:59:59.999999', properties: (8) { datetime: '2008-07-01T00:00:00', end_datetime: '2008-07-31T23:59:59.999999', id: '94d764c032a73052a990c5851721accc75f07f89', start_datetime: '2008-07-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200807-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/94d764c032a73052a990c5851721accc75f07f89?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:27', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200807-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200807-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200807-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200807-fv3p0.nc', } geometry
7  EOProduct(id=f2a967c724029057c68b2056b33c5241b9545a9f, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'f2a967c724029057c68b2056b33c5241b9545a9f', properties["start_datetime"]: '2008-08-01T00:00:00', properties["end_datetime"]: '2008-08-31T23:59:59.999999', properties: (8) { datetime: '2008-08-01T00:00:00', end_datetime: '2008-08-31T23:59:59.999999', id: 'f2a967c724029057c68b2056b33c5241b9545a9f', start_datetime: '2008-08-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200808-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f2a967c724029057c68b2056b33c5241b9545a9f?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:27', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200808-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200808-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200808-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200808-fv3p0.nc', } geometry
8  EOProduct(id=f618f05129c41d2ad3fa0e15b3dcfa063252c7b8, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'f618f05129c41d2ad3fa0e15b3dcfa063252c7b8', properties["start_datetime"]: '2008-09-01T00:00:00', properties["end_datetime"]: '2008-09-30T23:59:59.999999', properties: (8) { datetime: '2008-09-01T00:00:00', end_datetime: '2008-09-30T23:59:59.999999', id: 'f618f05129c41d2ad3fa0e15b3dcfa063252c7b8', start_datetime: '2008-09-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200809-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f618f05129c41d2ad3fa0e15b3dcfa063252c7b8?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:27', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200809-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200809-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200809-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200809-fv3p0.nc', } geometry
9  EOProduct(id=80640ea796b0ada538e60a3721e8ddc59e2bc7d4, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '80640ea796b0ada538e60a3721e8ddc59e2bc7d4', properties["start_datetime"]: '2008-10-01T00:00:00', properties["end_datetime"]: '2008-10-31T23:59:59.999999', properties: (8) { datetime: '2008-10-01T00:00:00', end_datetime: '2008-10-31T23:59:59.999999', id: '80640ea796b0ada538e60a3721e8ddc59e2bc7d4', start_datetime: '2008-10-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200810-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/80640ea796b0ada538e60a3721e8ddc59e2bc7d4?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:28', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200810-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200810-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200810-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200810-fv3p0.nc', } geometry
10  EOProduct(id=e1ad18d318caddb5eb97e981c0a9193e3200a270, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'e1ad18d318caddb5eb97e981c0a9193e3200a270', properties["start_datetime"]: '2008-11-01T00:00:00', properties["end_datetime"]: '2008-11-30T23:59:59.999999', properties: (8) { datetime: '2008-11-01T00:00:00', end_datetime: '2008-11-30T23:59:59.999999', id: 'e1ad18d318caddb5eb97e981c0a9193e3200a270', start_datetime: '2008-11-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200811-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/e1ad18d318caddb5eb97e981c0a9193e3200a270?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:28', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200811-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200811-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200811-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200811-fv3p0.nc', } geometry
11  EOProduct(id=362ca8f16bb5f4f750152d5370e6f570257ef2d4, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '362ca8f16bb5f4f750152d5370e6f570257ef2d4', properties["start_datetime"]: '2008-12-01T00:00:00', properties["end_datetime"]: '2008-12-31T23:59:59.999999', properties: (8) { datetime: '2008-12-01T00:00:00', end_datetime: '2008-12-31T23:59:59.999999', id: '362ca8f16bb5f4f750152d5370e6f570257ef2d4', start_datetime: '2008-12-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200812-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/362ca8f16bb5f4f750152d5370e6f570257ef2d4?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:29', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200812-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200812-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2008/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200812-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200812-fv3p0.nc', } geometry
12  EOProduct(id=a8119f3549e446ffec6aaff226dc97106a003433, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'a8119f3549e446ffec6aaff226dc97106a003433', properties["start_datetime"]: '2009-01-01T00:00:00', properties["end_datetime"]: '2009-01-31T23:59:59.999999', properties: (8) { datetime: '2009-01-01T00:00:00', end_datetime: '2009-01-31T23:59:59.999999', id: 'a8119f3549e446ffec6aaff226dc97106a003433', start_datetime: '2009-01-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200901-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/a8119f3549e446ffec6aaff226dc97106a003433?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:29', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200901-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200901-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200901-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200901-fv3p0.nc', } geometry
13  EOProduct(id=9b712d5883624d7de7a755ae144d0125511e845b, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '9b712d5883624d7de7a755ae144d0125511e845b', properties["start_datetime"]: '2009-02-01T00:00:00', properties["end_datetime"]: '2009-02-28T23:59:59.999999', properties: (8) { datetime: '2009-02-01T00:00:00', end_datetime: '2009-02-28T23:59:59.999999', id: '9b712d5883624d7de7a755ae144d0125511e845b', start_datetime: '2009-02-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200902-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/9b712d5883624d7de7a755ae144d0125511e845b?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:29', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200902-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200902-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200902-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200902-fv3p0.nc', } geometry
14  EOProduct(id=4a9cbfbd1aa3d5f8c484254a04d7d6c1fdfd4b46, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '4a9cbfbd1aa3d5f8c484254a04d7d6c1fdfd4b46', properties["start_datetime"]: '2009-03-01T00:00:00', properties["end_datetime"]: '2009-03-31T23:59:59.999999', properties: (8) { datetime: '2009-03-01T00:00:00', end_datetime: '2009-03-31T23:59:59.999999', id: '4a9cbfbd1aa3d5f8c484254a04d7d6c1fdfd4b46', start_datetime: '2009-03-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200903-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/4a9cbfbd1aa3d5f8c484254a04d7d6c1fdfd4b46?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:30', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200903-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200903-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200903-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200903-fv3p0.nc', } geometry
15  EOProduct(id=ec9a7903007edd66d1b258c048b388ea790a024e, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'ec9a7903007edd66d1b258c048b388ea790a024e', properties["start_datetime"]: '2009-04-01T00:00:00', properties["end_datetime"]: '2009-04-30T23:59:59.999999', properties: (8) { datetime: '2009-04-01T00:00:00', end_datetime: '2009-04-30T23:59:59.999999', id: 'ec9a7903007edd66d1b258c048b388ea790a024e', start_datetime: '2009-04-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200904-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/ec9a7903007edd66d1b258c048b388ea790a024e?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:30', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200904-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200904-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200904-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200904-fv3p0.nc', } geometry
16  EOProduct(id=af234427f2f8f714cea86a52ddcdf3dbbaf4b3b5, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'af234427f2f8f714cea86a52ddcdf3dbbaf4b3b5', properties["start_datetime"]: '2009-05-01T00:00:00', properties["end_datetime"]: '2009-05-31T23:59:59.999999', properties: (8) { datetime: '2009-05-01T00:00:00', end_datetime: '2009-05-31T23:59:59.999999', id: 'af234427f2f8f714cea86a52ddcdf3dbbaf4b3b5', start_datetime: '2009-05-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200905-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/af234427f2f8f714cea86a52ddcdf3dbbaf4b3b5?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:30', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200905-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200905-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200905-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200905-fv3p0.nc', } geometry
17  EOProduct(id=79cbe04d73e7a9d524518329fd545a984b54a75a, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '79cbe04d73e7a9d524518329fd545a984b54a75a', properties["start_datetime"]: '2009-06-01T00:00:00', properties["end_datetime"]: '2009-06-30T23:59:59.999999', properties: (8) { datetime: '2009-06-01T00:00:00', end_datetime: '2009-06-30T23:59:59.999999', id: '79cbe04d73e7a9d524518329fd545a984b54a75a', start_datetime: '2009-06-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200906-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/79cbe04d73e7a9d524518329fd545a984b54a75a?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:30', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200906-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200906-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200906-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200906-fv3p0.nc', } geometry
18  EOProduct(id=9318b213fb183bd81877b919123e747bd8942a57, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: '9318b213fb183bd81877b919123e747bd8942a57', properties["start_datetime"]: '2009-07-01T00:00:00', properties["end_datetime"]: '2009-07-31T23:59:59.999999', properties: (8) { datetime: '2009-07-01T00:00:00', end_datetime: '2009-07-31T23:59:59.999999', id: '9318b213fb183bd81877b919123e747bd8942a57', start_datetime: '2009-07-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200907-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/9318b213fb183bd81877b919123e747bd8942a57?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:31', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200907-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200907-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200907-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200907-fv3p0.nc', } geometry
19  EOProduct(id=da6e8b158b906f632c101e6bef3e9f6c2e41d96a, provider=fedeo_ceda) EOProduct provider: 'fedeo_ceda', collection: 'SEA_ICE_THICKNESS_L3C_ENVISAT_V3.0_SH', properties["id"]: 'da6e8b158b906f632c101e6bef3e9f6c2e41d96a', properties["start_datetime"]: '2009-08-01T00:00:00', properties["end_datetime"]: '2009-08-31T23:59:59.999999', properties: (8) { datetime: '2009-08-01T00:00:00', end_datetime: '2009-08-31T23:59:59.999999', id: 'da6e8b158b906f632c101e6bef3e9f6c2e41d96a', start_datetime: '2009-08-01T00:00:00', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200908-fv3p0', eodag:download_link: 'https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/da6e8b158b906f632c101e6bef3e9f6c2e41d96a?httpAccept=application/geo%2Bjson;profile=https://stacspec.org', fedeo_ceda:updated: '2024-03-14T12:00:31', order:status: 'succeeded', } assets: (2) 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json':  { 'roles': '['metadata']',  'type': 'application/octet-stream',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json',  ... } { roles: ['metadata' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/metadata/kerchunk/sea_ice_thickness/L3C/envisat/v3.0/SH/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', type: 'application/octet-stream', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-2002-2012-fv3p0-kr1.0.json', } 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200908-fv3p0.nc':  { 'roles': '['data']',  'type': 'application/x-netcdf',  'title': 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200908-fv3p0.nc',  ... } { roles: ['data' ], href: 'https://data.cci.ceda.ac.uk/thredds/fileServer/esacci/sea_ice/data/sea_ice_thickness/L3C/envisat/v3.0/SH/2009/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200908-fv3p0.nc', type: 'application/x-netcdf', title: 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200908-fv3p0.nc', } geometry

Open dataset with to_xarray from eodag-cube and plot over a map using cartopy

# Get XarrayDict
xd = products[0].to_xarray()
xd

XarrayDict (1)

'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0.nc':  xarray.Dataset (yc: 216, xc: 216, time: 1, nv: 2)  Size: 3MB <xarray.Dataset> Size: 3MB Dimensions: (yc: 216, xc: 216, time: 1, nv: 2) Coordinates: lat (yc, xc) float64 373kB ... lon (yc, xc) float64 373kB ... * time (time) datetime64[ns] 8B 2008-01-01 * xc (xc) float64 2kB -5.375e+03 ... 5.375e+03 * yc (yc) float64 2kB 5.375e+03 ... -5.375e+03 Dimensions without coordinates: nv Data variables: (12/14) quality_flag (time, yc, xc) int8 47kB ... radar_freeboard (time, yc, xc) float32 187kB ... radar_freeboard_uncertainty (time, yc, xc) float32 187kB ... region_code (time, yc, xc) int8 47kB ... sea_ice_concentration (time, yc, xc) float32 187kB ... sea_ice_freeboard (time, yc, xc) float32 187kB ... ... ... sea_ice_thickness_uncertainty (time, yc, xc) float32 187kB ... snow_depth (time, yc, xc) float32 187kB ... snow_depth_uncertainty (time, yc, xc) float32 187kB ... status_flag (time, yc, xc) int8 47kB ... time_bnds (time, nv) datetime64[ns] 16B ... Lambert_Azimuthal_Grid int8 1B ... Attributes: (12/47) title: ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_... institution: Alfred-Wegener-Institut Helmholtz Zentrum für ... source: Altimetry: envisat, Snow depth: ESA-SICCI AMSR... platform: Envisat sensor: RA-2 history: 20240220T162330Z - Product generated with pysi... ... ... datetime: 2008-01-01T00:00:00 end_datetime: 2008-01-31T23:59:59.999999 start_datetime: 2008-01-01T00:00:00 eodag:download_link: https://fedeo.ceos.org/collections/ab6a05baacc... fedeo_ceda:updated: 2024-03-14T12:00:25 order:status: succeeded xarray.Dataset Dimensions: yc: 216 xc: 216 time: 1 nv: 2 Coordinates: (5) lat (yc, xc) float64 ... coverage_content_type : coordinate long_name : latitude coordinate standard_name : latitude units : degrees_north [46656 values with dtype=float64] lon (yc, xc) float64 ... coverage_content_type : coordinate long_name : longitude coordinate standard_name : longitude units : degrees_east [46656 values with dtype=float64] time (time) datetime64[ns] 2008-01-01 standard_name : time long_name : Time axis : T bounds : time_bnds coverage_content_type : coordinate array(['2008-01-01T00:00:00.000000000'], dtype='datetime64[ns]') xc (xc) float64 -5.375e+03 -5.325e+03 ... 5.375e+03 standard_name : projection_x_coordinate units : km long_name : x coordinate of projection (eastings) coverage_content_type : coordinate array([-5375., -5325., -5275., ..., 5275., 5325., 5375.]) yc (yc) float64 5.375e+03 5.325e+03 ... -5.375e+03 standard_name : projection_y_coordinate units : km long_name : y coordinate of projection (northing) coverage_content_type : coordinate array([ 5375., 5325., 5275., ..., -5275., -5325., -5375.]) Data variables: (14) quality_flag (time, yc, xc) int8 ... comment : The expert assessment on retrieval quality is only provided for grid cess with valid thickness retrieval coverage_content_type : qualityInformation flag_meanings : nominal_quality intermediate_quality low_quality no_data flag_values : [0 1 2 3] grid_mapping : Lambert_Azimuthal_Grid long_name : Sea Ice Thickness Quality Flag standard_name : quality_flag units : 1 valid_max : 3 valid_min : 0 [46656 values with dtype=int8] radar_freeboard (time, yc, xc) float32 ... ancillary_variables : radar_freeboard_uncertainty, region_code comment : radar freeboard is defined as the elevation based on the assumption of vacuum light speed without a snow propagation or range penetration correction coverage_content_type : physicalMeasurement grid_mapping : Lambert_Azimuthal_Grid long_name : radar freeboard units : m [46656 values with dtype=float32] radar_freeboard_uncertainty (time, yc, xc) float32 ... comment : algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding coverage_content_type : qualityInformation grid_mapping : Lambert_Azimuthal_Grid long_name : uncertainty associated with radar freeboard units : m [46656 values with dtype=float32] region_code (time, yc, xc) int8 ... comment : Region code from Antarctic seas based on their longitude range with additional latitude cut-off at 40S (see Parkinson, C. L. and Cavalieri, D. J. (2012), Antarctic sea ice variability and trends, 1979 - 2010, The Cryosphere) coverage_content_type : referenceInformation flag_meanings : land_ice ice_free_ocean indian_ocean western_pacific_ocean ross_sea bellingshausen_and_amundsen_seas weddell_sea land flag_values : [ 0 1 2 3 4 5 6 20] grid_mapping : Lambert_Azimuthal_Grid long_name : region code units : 1 valid_max : 20 valid_min : 0 [46656 values with dtype=int8] sea_ice_concentration (time, yc, xc) float32 ... coverage_content_type : auxiliaryInformation grid_mapping : Lambert_Azimuthal_Grid long_name : sea ice concentration standard_name : sea_ice_area_fraction units : percent valid_max : 100.0 valid_min : 0.0 [46656 values with dtype=float32] sea_ice_freeboard (time, yc, xc) float32 ... ancillary_variables : sea_ice_freeboard_uncertainty, region_code coverage_content_type : physicalMeasurement grid_mapping : Lambert_Azimuthal_Grid long_name : sea ice freeboard standard_name : sea_ice_freeboard units : m [46656 values with dtype=float32] sea_ice_freeboard_uncertainty (time, yc, xc) float32 ... comment : algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding coverage_content_type : qualityInformation grid_mapping : Lambert_Azimuthal_Grid long_name : uncertainty associated with radar freeboard standard_name : sea_ice_freeboard standard_error units : m [46656 values with dtype=float32] sea_ice_thickness (time, yc, xc) float32 ... ancillary_variables : sea_ice_thickness_uncertainty, status_flag, quality_flag region_code comment : this field is the primary sea ice thickness estimate for this data record coverage_content_type : physicalMeasurement grid_mapping : Lambert_Azimuthal_Grid long_name : sea ice thickness standard_name : sea_ice_thickness units : m [46656 values with dtype=float32] sea_ice_thickness_uncertainty (time, yc, xc) float32 ... comment : algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding coverage_content_type : qualityInformation grid_mapping : Lambert_Azimuthal_Grid long_name : uncertainty associated with sea ice thickness standard_name : sea_ice_thickness standard_error units : m [46656 values with dtype=float32] snow_depth (time, yc, xc) float32 ... ancillary_variables : snow_depth_uncertainty, region_code coverage_content_type : auxiliaryInformation grid_mapping : Lambert_Azimuthal_Grid long_name : thickness of the snow layer standard_name : surface_snow_thickness_where_sea_ice units : m [46656 values with dtype=float32] snow_depth_uncertainty (time, yc, xc) float32 ... coverage_content_type : qualityInformation grid_mapping : Lambert_Azimuthal_Grid long_name : uncertainty associated to the thickness of the snow layer standard_name : surface_snow_thickness_where_sea_ice standard_error units : m [46656 values with dtype=float32] status_flag (time, yc, xc) int8 ... comment : Describes the status of the sea-ice thickness retrieval coverage_content_type : qualityInformation flag_meanings : nominal_retrieval no_data open_ocean satellite_pole_hole land_lake_landice retrieval_failed flag_values : [0 1 2 3 4 5] grid_mapping : Lambert_Azimuthal_Grid long_name : Sea Ice Thickness Status Flag standard_name : status_flag units : 1 valid_max : 5 valid_min : 0 [46656 values with dtype=int8] time_bnds (time, nv) datetime64[ns] ... long_name : Time Bounds coverage_content_type : coordinate [2 values with dtype=datetime64[ns]] Lambert_Azimuthal_Grid () int8 ... false_easting : 0.0 false_northing : 0.0 grid_mapping_name : lambert_azimuthal_equal_area inverse_flattening : 298.257223563 latitude_of_projection_origin : -90.0 longitude_of_projection_origin : 0.0 proj4_string : +proj=laea +lon_0=0 +datum=WGS84 +ellps=WGS84 +lat_0=-90.0 semi_major_axis : 6378137.0 [1 values with dtype=int8] Indexes: (3) time PandasIndex PandasIndex(DatetimeIndex(['2008-01-01'], dtype='datetime64[ns]', name='time', freq=None)) xc PandasIndex PandasIndex(Index([-5375.0, -5325.0, -5275.0, -5225.0, -5175.0, -5125.0, -5075.0, -5025.0, -4975.0, -4925.0, ... 4925.0, 4975.0, 5025.0, 5075.0, 5125.0, 5175.0, 5225.0, 5275.0, 5325.0, 5375.0], dtype='float64', name='xc', length=216)) yc PandasIndex PandasIndex(Index([ 5375.0, 5325.0, 5275.0, 5225.0, 5175.0, 5125.0, 5075.0, 5025.0, 4975.0, 4925.0, ... -4925.0, -4975.0, -5025.0, -5075.0, -5125.0, -5175.0, -5225.0, -5275.0, -5325.0, -5375.0], dtype='float64', name='yc', length=216)) Attributes: (47) title : ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0 institution : Alfred-Wegener-Institut Helmholtz Zentrum für Polar und Meeresforschung source : Altimetry: envisat, Snow depth: ESA-SICCI AMSR-E/AMSR2 snow depth on sea ice climatology, Mean Sea Surface: DTU21 global mean sea surface, Sea ice Concentration: C3S Sea Ice Concentration CDR/ICDR v3.0, Sea ice type: First-year sea ice only, Region code: Longitude-based sectors from Parkinson, C. L. and Cavalieri, D. J. (2012) platform : Envisat sensor : RA-2 history : 20240220T162330Z - Product generated with pysiral version 0.9.6 references : CCI+ Sea Ice ECV - Sea Ice Thickness Algorithm Theoretical Basis Document (ATBD)https://doi.org/10.5281/zenodo.10605840; CCI+ Sea Ice ECV - Sea Ice Thickness Product User Guide (PUG) https://doi.org/10.5281/zenodo.10605998 tracking_id : e4017d5a-b213-4a24-a2a7-5e0045fcf00c Conventions : CF-1.8 product_version : 3.0 format_version : CCI Data Standards v2.3 processing_level : Level-3 Collated (l3c) summary : Gridded Southern Hemisphere Sea Ice Thickness Climate Data Record from Envisat and CryoSat-2 satellite radar altimetry for the period June 2002 - April 2020. id : f4e5c7465abb04639aefd4b6d19af7b47273480f naming_authority : de.awi keywords : Earth Science > Cryosphere > Sea Ice > Ice Depth/Thickness, Earth Science > Cryosphere > Sea Ice > Freeboard, Earth Science > Cryosphere > Sea Ice > Snow Depth, Earth Science > Oceans > Sea Ice > Ice Depth/Thickness, Earth Science > Climate Indicators > Cryospheric Indicators > Ice Depth/Thickness, Earth Remote Sensing Instruments > Active Remote Sensing > Altimeters > Radar Altimeters > KU-BAND RADAR, Space-based Platforms > Earth Observation Satellites > envisat, Multinational Organizations > ESA/CCI, Institutions > AWI > Alfred Wegener Institute for Polar and Marine Research, Geographic Region > Southern Hemisphere, Vertical Location > Sea Surface keywords_vocabulary : GCMD Science Keywords doi : 10.5285/ab6a05baacce4c848d137a0bc9921e6e cdm_data_type : Grid comment : Southern hemisphere sea ice thickness is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all month but needs to be considered biased high in areas with high snow depth and during the southern summer month. Please consult the Product User Guide (PUG) for more information. date_created : 20240220T162330Z creator_name : Alfred-Wegener-Institut Helmholtz Zentrum für Polar und Meeresforschung creator_type : institution creator_url : http://www.awi.de contributor_name : Stefan Hendricks, Stephan Paul, Eero Rinne, heidi.sallila@fmi.fi contributor_role : PrincipalInvestigator, Author, Author, Author project : Climate Change Initiative - European Space Agency geospatial_lat_min : -89.6835 geospatial_lat_max : -16.8229 geospatial_lon_min : -179.7335 geospatial_lon_max : 179.7335 geospatial_vertical_min : 0.0 geospatial_vertical_max : 0.0 spatial_resolution : 50km grid spacing geospatial_bounds_crs : EPSG:6932 time_coverage_start : 2008-01-01T00:00:00 time_coverage_end : 2008-01-31T23:59:59.999999 time_coverage_duration : P1M time_coverage_resolution : P1M standard_name_vocabulary : CF Standard Name Table v77 license : ESA CCI Data Policy: free and open access datetime : 2008-01-01T00:00:00 end_datetime : 2008-01-31T23:59:59.999999 start_datetime : 2008-01-01T00:00:00 eodag:download_link : https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f4e5c7465abb04639aefd4b6d19af7b47273480f?httpAccept=application/geo%2Bjson;profile=https://stacspec.org fedeo_ceda:updated : 2024-03-14T12:00:25 order:status : succeeded

# Dataset from XarrayDict first value
ds = next(iter(xd.values()))
ds

<xarray.Dataset> Size: 3MB
Dimensions:                        (yc: 216, xc: 216, time: 1, nv: 2)
Coordinates:
    lat                            (yc, xc) float64 373kB ...
    lon                            (yc, xc) float64 373kB ...
  * time                           (time) datetime64[ns] 8B 2008-01-01
  * xc                             (xc) float64 2kB -5.375e+03 ... 5.375e+03
  * yc                             (yc) float64 2kB 5.375e+03 ... -5.375e+03
Dimensions without coordinates: nv
Data variables: (12/14)
    quality_flag                   (time, yc, xc) int8 47kB ...
    radar_freeboard                (time, yc, xc) float32 187kB ...
    radar_freeboard_uncertainty    (time, yc, xc) float32 187kB ...
    region_code                    (time, yc, xc) int8 47kB ...
    sea_ice_concentration          (time, yc, xc) float32 187kB ...
    sea_ice_freeboard              (time, yc, xc) float32 187kB ...
    ...                             ...
    sea_ice_thickness_uncertainty  (time, yc, xc) float32 187kB ...
    snow_depth                     (time, yc, xc) float32 187kB ...
    snow_depth_uncertainty         (time, yc, xc) float32 187kB ...
    status_flag                    (time, yc, xc) int8 47kB ...
    time_bnds                      (time, nv) datetime64[ns] 16B ...
    Lambert_Azimuthal_Grid         int8 1B ...
Attributes: (12/47)
    title:                     ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_...
    institution:               Alfred-Wegener-Institut Helmholtz Zentrum für ...
    source:                    Altimetry: envisat, Snow depth: ESA-SICCI AMSR...
    platform:                  Envisat
    sensor:                    RA-2
    history:                   20240220T162330Z - Product generated with pysi...
    ...                        ...
    datetime:                  2008-01-01T00:00:00
    end_datetime:              2008-01-31T23:59:59.999999
    start_datetime:            2008-01-01T00:00:00
    eodag:download_link:       https://fedeo.ceos.org/collections/ab6a05baacc...
    fedeo_ceda:updated:        2024-03-14T12:00:25
    order:status:              succeeded

xarray.Dataset

• Dimensions:
  • yc: 216
  • xc: 216
  • time: 1
  • nv: 2
• Coordinates: (5)
  • lat
    (yc, xc)
    float64
    ...

    coverage_content_type :

    coordinate

    long_name :

    latitude coordinate

    standard_name :

    latitude

    units :

    degrees_north

    [46656 values with dtype=float64]

  • lon
    (yc, xc)
    float64
    ...

    coverage_content_type :

    coordinate

    long_name :

    longitude coordinate

    standard_name :

    longitude

    units :

    degrees_east

    [46656 values with dtype=float64]

  • time
    (time)
    datetime64[ns]
    2008-01-01

    standard_name :

    time

    long_name :

    Time

    axis :

    T

    bounds :

    time_bnds

    coverage_content_type :

    coordinate

    array(['2008-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • xc
    (xc)
    float64
    -5.375e+03 -5.325e+03 ... 5.375e+03

    standard_name :

    projection_x_coordinate

    units :

    km

    long_name :

    x coordinate of projection (eastings)

    coverage_content_type :

    coordinate

    array([-5375., -5325., -5275., ...,  5275.,  5325.,  5375.])

  • yc
    (yc)
    float64
    5.375e+03 5.325e+03 ... -5.375e+03

    standard_name :

    projection_y_coordinate

    units :

    km

    long_name :

    y coordinate of projection (northing)

    coverage_content_type :

    coordinate

    array([ 5375.,  5325.,  5275., ..., -5275., -5325., -5375.])

• Data variables: (14)
  • quality_flag
    (time, yc, xc)
    int8
    ...

    comment :

    The expert assessment on retrieval quality is only provided for grid cess with valid thickness retrieval

    coverage_content_type :

    qualityInformation

    flag_meanings :

    nominal_quality intermediate_quality low_quality no_data

    flag_values :

    [0 1 2 3]

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    Sea Ice Thickness Quality Flag

    standard_name :

    quality_flag

    units :

    1

    valid_max :

    3

    valid_min :

    0

    [46656 values with dtype=int8]

  • radar_freeboard
    (time, yc, xc)
    float32
    ...

    ancillary_variables :

    radar_freeboard_uncertainty, region_code

    comment :

    radar freeboard is defined as the elevation based on the assumption of vacuum light speed without a snow propagation or range penetration correction

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    radar freeboard

    units :

    m

    [46656 values with dtype=float32]

  • radar_freeboard_uncertainty
    (time, yc, xc)
    float32
    ...

    comment :

    algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding

    coverage_content_type :

    qualityInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    uncertainty associated with radar freeboard

    units :

    m

    [46656 values with dtype=float32]

  • region_code
    (time, yc, xc)
    int8
    ...

    comment :

    Region code from Antarctic seas based on their longitude range with additional latitude cut-off at 40S (see Parkinson, C. L. and Cavalieri, D. J. (2012), Antarctic sea ice variability and trends, 1979 - 2010, The Cryosphere)

    coverage_content_type :

    referenceInformation

    flag_meanings :

    land_ice ice_free_ocean indian_ocean western_pacific_ocean ross_sea bellingshausen_and_amundsen_seas weddell_sea land

    flag_values :

    [ 0 1 2 3 4 5 6 20]

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    region code

    units :

    1

    valid_max :

    20

    valid_min :

    0

    [46656 values with dtype=int8]

  • sea_ice_concentration
    (time, yc, xc)
    float32
    ...

    coverage_content_type :

    auxiliaryInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    sea ice concentration

    standard_name :

    sea_ice_area_fraction

    units :

    percent

    valid_max :

    100.0

    valid_min :

    0.0

    [46656 values with dtype=float32]

  • sea_ice_freeboard
    (time, yc, xc)
    float32
    ...

    ancillary_variables :

    sea_ice_freeboard_uncertainty, region_code

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    sea ice freeboard

    standard_name :

    sea_ice_freeboard

    units :

    m

    [46656 values with dtype=float32]

  • sea_ice_freeboard_uncertainty
    (time, yc, xc)
    float32
    ...

    comment :

    algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding

    coverage_content_type :

    qualityInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    uncertainty associated with radar freeboard

    standard_name :

    sea_ice_freeboard standard_error

    units :

    m

    [46656 values with dtype=float32]

  • sea_ice_thickness
    (time, yc, xc)
    float32
    ...

    ancillary_variables :

    sea_ice_thickness_uncertainty, status_flag, quality_flag region_code

    comment :

    this field is the primary sea ice thickness estimate for this data record

    coverage_content_type :

    physicalMeasurement

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    sea ice thickness

    standard_name :

    sea_ice_thickness

    units :

    m

    [46656 values with dtype=float32]

  • sea_ice_thickness_uncertainty
    (time, yc, xc)
    float32
    ...

    comment :

    algorithm uncertainty (error propagation) of sea ice thickness including uncertainty reduction of random components by gridding

    coverage_content_type :

    qualityInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    uncertainty associated with sea ice thickness

    standard_name :

    sea_ice_thickness standard_error

    units :

    m

    [46656 values with dtype=float32]

  • snow_depth
    (time, yc, xc)
    float32
    ...

    ancillary_variables :

    snow_depth_uncertainty, region_code

    coverage_content_type :

    auxiliaryInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    thickness of the snow layer

    standard_name :

    surface_snow_thickness_where_sea_ice

    units :

    m

    [46656 values with dtype=float32]

  • snow_depth_uncertainty
    (time, yc, xc)
    float32
    ...

    coverage_content_type :

    qualityInformation

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    uncertainty associated to the thickness of the snow layer

    standard_name :

    surface_snow_thickness_where_sea_ice standard_error

    units :

    m

    [46656 values with dtype=float32]

  • status_flag
    (time, yc, xc)
    int8
    ...

    comment :

    Describes the status of the sea-ice thickness retrieval

    coverage_content_type :

    qualityInformation

    flag_meanings :

    nominal_retrieval no_data open_ocean satellite_pole_hole land_lake_landice retrieval_failed

    flag_values :

    [0 1 2 3 4 5]

    grid_mapping :

    Lambert_Azimuthal_Grid

    long_name :

    Sea Ice Thickness Status Flag

    standard_name :

    status_flag

    units :

    1

    valid_max :

    5

    valid_min :

    0

    [46656 values with dtype=int8]

  • time_bnds
    (time, nv)
    datetime64[ns]
    ...

    long_name :

    Time Bounds

    coverage_content_type :

    coordinate

    [2 values with dtype=datetime64[ns]]

  • Lambert_Azimuthal_Grid
    ()
    int8
    ...

    false_easting :

    0.0

    false_northing :

    0.0

    grid_mapping_name :

    lambert_azimuthal_equal_area

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    -90.0

    longitude_of_projection_origin :

    0.0

    proj4_string :

    +proj=laea +lon_0=0 +datum=WGS84 +ellps=WGS84 +lat_0=-90.0

    semi_major_axis :

    6378137.0

    [1 values with dtype=int8]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2008-01-01'], dtype='datetime64[ns]', name='time', freq=None))

  • xc
    PandasIndex

    PandasIndex(Index([-5375.0, -5325.0, -5275.0, -5225.0, -5175.0, -5125.0, -5075.0, -5025.0,
           -4975.0, -4925.0,
           ...
            4925.0,  4975.0,  5025.0,  5075.0,  5125.0,  5175.0,  5225.0,  5275.0,
            5325.0,  5375.0],
          dtype='float64', name='xc', length=216))

  • yc
    PandasIndex

    PandasIndex(Index([ 5375.0,  5325.0,  5275.0,  5225.0,  5175.0,  5125.0,  5075.0,  5025.0,
            4975.0,  4925.0,
           ...
           -4925.0, -4975.0, -5025.0, -5075.0, -5125.0, -5175.0, -5225.0, -5275.0,
           -5325.0, -5375.0],
          dtype='float64', name='yc', length=216))

• Attributes: (47)

  title :

  ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0

  institution :

  Alfred-Wegener-Institut Helmholtz Zentrum für Polar und Meeresforschung

  source :

  Altimetry: envisat, Snow depth: ESA-SICCI AMSR-E/AMSR2 snow depth on sea ice climatology, Mean Sea Surface: DTU21 global mean sea surface, Sea ice Concentration: C3S Sea Ice Concentration CDR/ICDR v3.0, Sea ice type: First-year sea ice only, Region code: Longitude-based sectors from Parkinson, C. L. and Cavalieri, D. J. (2012)

  platform :

  Envisat

  sensor :

  RA-2

  history :

  20240220T162330Z - Product generated with pysiral version 0.9.6

  references :

  CCI+ Sea Ice ECV - Sea Ice Thickness Algorithm Theoretical Basis Document (ATBD)https://doi.org/10.5281/zenodo.10605840; CCI+ Sea Ice ECV - Sea Ice Thickness Product User Guide (PUG) https://doi.org/10.5281/zenodo.10605998

  tracking_id :

  e4017d5a-b213-4a24-a2a7-5e0045fcf00c

  Conventions :

  CF-1.8

  product_version :

  3.0

  format_version :

  CCI Data Standards v2.3

  processing_level :

  Level-3 Collated (l3c)

  summary :

  Gridded Southern Hemisphere Sea Ice Thickness Climate Data Record from Envisat and CryoSat-2 satellite radar altimetry for the period June 2002 - April 2020.

  id :

  f4e5c7465abb04639aefd4b6d19af7b47273480f

  naming_authority :

  de.awi

  keywords :

  Earth Science > Cryosphere > Sea Ice > Ice Depth/Thickness, Earth Science > Cryosphere > Sea Ice > Freeboard, Earth Science > Cryosphere > Sea Ice > Snow Depth, Earth Science > Oceans > Sea Ice > Ice Depth/Thickness, Earth Science > Climate Indicators > Cryospheric Indicators > Ice Depth/Thickness, Earth Remote Sensing Instruments > Active Remote Sensing > Altimeters > Radar Altimeters > KU-BAND RADAR, Space-based Platforms > Earth Observation Satellites > envisat, Multinational Organizations > ESA/CCI, Institutions > AWI > Alfred Wegener Institute for Polar and Marine Research, Geographic Region > Southern Hemisphere, Vertical Location > Sea Surface

  keywords_vocabulary :

  GCMD Science Keywords

  doi :

  10.5285/ab6a05baacce4c848d137a0bc9921e6e

  cdm_data_type :

  Grid

  comment :

  Southern hemisphere sea ice thickness is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all month but needs to be considered biased high in areas with high snow depth and during the southern summer month. Please consult the Product User Guide (PUG) for more information.

  date_created :

  20240220T162330Z

  creator_name :

  Alfred-Wegener-Institut Helmholtz Zentrum für Polar und Meeresforschung

  creator_type :

  institution

  creator_url :

  http://www.awi.de

  contributor_name :

  Stefan Hendricks, Stephan Paul, Eero Rinne, heidi.sallila@fmi.fi

  contributor_role :

  PrincipalInvestigator, Author, Author, Author

  project :

  Climate Change Initiative - European Space Agency

  geospatial_lat_min :

  -89.6835

  geospatial_lat_max :

  -16.8229

  geospatial_lon_min :

  -179.7335

  geospatial_lon_max :

  179.7335

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  spatial_resolution :

  50km grid spacing

  geospatial_bounds_crs :

  EPSG:6932

  time_coverage_start :

  2008-01-01T00:00:00

  time_coverage_end :

  2008-01-31T23:59:59.999999

  time_coverage_duration :

  P1M

  time_coverage_resolution :

  P1M

  standard_name_vocabulary :

  CF Standard Name Table v77

  license :

  ESA CCI Data Policy: free and open access

  datetime :

  2008-01-01T00:00:00

  end_datetime :

  2008-01-31T23:59:59.999999

  start_datetime :

  2008-01-01T00:00:00

  eodag:download_link :

  https://fedeo.ceos.org/collections/ab6a05baacce4c848d137a0bc9921e6e/items/f4e5c7465abb04639aefd4b6d19af7b47273480f?httpAccept=application/geo%2Bjson;profile=https://stacspec.org

  fedeo_ceda:updated :

  2024-03-14T12:00:25

  order:status :

  succeeded

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Take the first time DataArray for snow_depth
da = ds.snow_depth.isel(time=0)

# Use a South Polar Stereographic projection
proj = ccrs.SouthPolarStereo()

fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection=proj)

# Add coastlines, land and gridlines\n",
ax.coastlines()
ax.add_feature(cfeature.LAND, facecolor="lightgray")
ax.gridlines(draw_labels=True)

# Plot the data
pcm = ax.pcolormesh(da["lon"], da["lat"], da, transform=ccrs.PlateCarree(), cmap="rainbow")

# Colorbar
cb = plt.colorbar(pcm, ax=ax, orientation="vertical", shrink=0.7, pad=0.05)
cb.set_label(f"{da.attrs.get('long_name', 'value')} [{da.attrs.get('units', '')}]")

plt.title(da['time'].values)
plt.show()

Download data

path = products[0].download(output_dir="/tmp")
path

'/tmp/ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-SH_50KM_EASE2-200801-fv3p0'