Procedure Composite Process List
Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations where used.
These may have a number of 2 or more components made up of combinations of Computation and Acquisition records.
The details of the underlying records have been serialised.
GET /api/v3/composites/?format=api&offset=600
{ "count": 662, "next": null, "previous": "https://api.catalogue.ceda.ac.uk/api/v3/composites/?format=api&limit=100&offset=500", "results": [ { "ob_id": 43582, "uuid": "f6ea609adda74d07916306c48d46cfae", "title": "Composite process for EOCIS: University of Leicester GOSAT-2 Proxy XCH4 v9.0_eocis", "abstract": "The latest version of the GOSAT-2 Level 1B files were processed with the Leicester Retrieval Preparation Toolset to extract the measured radiances along with all required sounding-specific ancillary information such as the measurement time, location and geometry. The data were processed through the UoL-FP retrieval algorithm where the Proxy retrieval approach is used to obtain the column-averaged dry-air mole fraction of methane (XCH4). Post-filtering and bias correction against the Total Carbon Column Observing Network is then performed, via the same methodology as used for the GOSAT-1 data.", "computationComponent": [ { "ob_id": 43580, "uuid": "1cef7aff4035496cbde00291b498c0fd", "short_code": "comp", "title": "Derivation of the EOCIS: University of Leicester GOSAT and GOSAT-2 Proxy XCH4 v9.0_eocis data", "abstract": "The GOSAT and GOSAT-2 EOCIS datasets were derived using the UoL-FP retrieval algorithm using a proxy retrieval approach. See the linked documentation for further information" } ], "acquisitionComponent": [ { "ob_id": 43583, "uuid": "57c9d51412a349a0afd9129c479511a9", "short_code": "acq", "title": "Acquisition for EOCIS: University of Leicester GOSAT-2 Proxy XCH4 v9.0_eocis", "abstract": "This dataset was derived from TANSO-FTS-2 instrument on the GOSAT-2 satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 208080 ] }, { "ob_id": 43647, "uuid": "9c5c37dea30341f7aa9fff29f8d148a9", "title": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "computationComponent": [ { "ob_id": 43646, "uuid": "8129dc20c94b4b53a3802ff83b2667df", "short_code": "comp", "title": "TLS2trees processing pipeline for FBRMS-01: Paracou, French Guiana 1ha plots", "abstract": "Data for each of the three French Guiana FBRMS plots is found within plot directories: FG5c1; FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-04_FG5c1_PROJ) with nine data subdirectories, a tile_index.dat file and a 2022-06-04_FG5c1.kmz file as shown in the attached ForestScan_example_directory_structure.pdf document. \r\n\r\nThe raw project subdirectory contains all registered scans for each FBRMS 1ha plot. The matrix project subdirectory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-04_FG5c1.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the three ForestScan FBRMS plots in French Guiana, the step-by-step processing summary below provides details for these files. \r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply. \r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat. \r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project subdirectory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports. \r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project subdirectory, e.g. figures/FG5c1.0.png." } ], "acquisitionComponent": [ { "ob_id": 43645, "uuid": "9b0e032030f445e88a9aac309bb31adf", "short_code": "acq", "title": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 208905 ] }, { "ob_id": 43660, "uuid": "a645e1bd6a774a659c2019985df25f3b", "title": "Composite process for 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", "abstract": "This dataset was derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi National Polar-orbitingPartnership (SNPP) satellite. For more information, see the associated documentation.", "computationComponent": [ { "ob_id": 43659, "uuid": "5d8107778649409f80143ca6db5432bf", "short_code": "comp", "title": "Computation for 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", "abstract": "For information on the derivation of this dataset see the associated documentation." } ], "acquisitionComponent": [ { "ob_id": 43658, "uuid": "b2b0de9091a044bcba77415f94a0983f", "short_code": "acq", "title": "Acquisition for 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", "abstract": "Data have been derived from the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 208954, 208955 ] }, { "ob_id": 43664, "uuid": "e3a44a7d11054a7bacb119cd033a90d9", "title": "Composite process for the 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", "abstract": "This dataset was derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the NOAA-20 (National Oceanic and Atmospheric Administration) satellite. For more information, see the associated documentation.", "computationComponent": [ { "ob_id": 43663, "uuid": "cc471b458b194741b400947b038ea9c2", "short_code": "comp", "title": "Computation for the 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", "abstract": "For information on the derivation of this dataset see the associated documentation." } ], "acquisitionComponent": [ { "ob_id": 43662, "uuid": "6b3284b8fe45401c8b95fb0560d1b967", "short_code": "acq", "title": "Acquisition for 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", "abstract": "Data have been derived from the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on the NOAA-20 (National Oceanic and Atmospheric Administration) satellite." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 208967, 208968 ] }, { "ob_id": 43714, "uuid": "4b29cca847d34921a138a95ceee3196a", "title": "ForestScan: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "ForestScan: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "computationComponent": [ { "ob_id": 43713, "uuid": "d170b6e232f74427bd820c1c2fee87aa", "short_code": "comp", "title": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "Once co-registered using RiScanPro software, individual scans were exported in las extrabyte format (including deviation) using LidarFomartConverter v.1.2.(AMAP code based on RivLib). Reflectance range was set to -30dB to +5dB and stored in the Intensity field as a long integer. Echoes outside this reflectance range were discarded. Coordinate precisions were set to 0.001 m. The full point cloud (all 249 scans) was then cropped to 1.4 ha plot (+10m buffer around 100x100m plot), and tiled per 20 x 20m (no buffer). Cropping and tiling were done with LAStools software. Scan position number was stored as flight line to allow selection of scans if needed. In particular, distant scans which contribute little more than noise could be deleted. LiDAR data were acquired without the “reflectance optimization filter”. In order to keep only returns with reflectance above -20dB (equivalent to setting reflectance optimization filter) all returns with Intensity below 18724 were dropped." } ], "acquisitionComponent": [ { "ob_id": 43645, "uuid": "9b0e032030f445e88a9aac309bb31adf", "short_code": "acq", "title": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 209146 ] }, { "ob_id": 43874, "uuid": "5ade1276557240b18823adae67c07991", "title": "Met Office Hadley Centre CET v2 data process", "abstract": "Met Office Hadley Centre CET v2 data process", "computationComponent": [ { "ob_id": 43873, "uuid": "dcae52d88dea47e0947e2eed2e08bd31", "short_code": "comp", "title": "Met Office Hadley Centre Daily Central England Temperature series v2 Data Processing Procedure", "abstract": "The Met Office Hadley Centre Central England Temperature series is computed through a series of processes applied to temperature observations from a range of sites around Central England.\r\n\r\nFor a description of this process please see Packman (2025, https://doi.org/10.5281/zenodo.15131212) linked to on this record." } ], "acquisitionComponent": [ { "ob_id": 43867, "uuid": "53730973f000415c8858006e54de7e02", "short_code": "acq", "title": "Acquisition Process for: Daily Central England Temperature series v2", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Rothamsted temperature sensor, Malvern temperature sensor, Squires Gate Temperature sensor, Ringway temperature sensor, Stonyhurst temperature sensor, Met Office station temperature sensor; PLATFORMS: Met Office: Rothamsted, Met Office: Malvern, Met Office: Squires Gate, Met Office: Ringway, Met Office: Stonyhurst, partially unknown set of UK CET stations, Set of UK stations used by Parker et al.;" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 209733, 209734 ] }, { "ob_id": 43892, "uuid": "5e1bb66a866b4d20adbe9233b3b9af0a", "title": "CCI Biomass v6.0", "abstract": "CCI Biomass", "computationComponent": [ { "ob_id": 43894, "uuid": "cb2e1150ed9a4d3e9f4a3856b1f046c2", "short_code": "comp", "title": "The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v6.0", "abstract": "For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document)." } ], "acquisitionComponent": [ { "ob_id": 43893, "uuid": "0debd39519bf412f99fea8950e655c42", "short_code": "acq", "title": "CCI Biomass, v6.0", "abstract": "CCI Biomass, v6.0" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 209819, 209820 ] }, { "ob_id": 43926, "uuid": "d763a207b0b64bfebb0485795c0fa342", "title": "Composite process for ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v3.0", "abstract": "The DTU Space v3.0 Greenland Gravimetric Mass Balance (GMB) product has been derived from monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\nThe mass change estimation is based on inversion method developed at DTU Space.", "computationComponent": [ { "ob_id": 43927, "uuid": "d5a40c7e64a24b52ad7a8b77bb72f020", "short_code": "comp", "title": "Derivation of the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v3.0", "abstract": "Estimates of mass change have been derived based on inversion methods developped at DTU Space.\r\n\r\nThe underlying L2 monthly gravity field solutions used in the derivation were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\n For more information see the linked documentation." } ], "acquisitionComponent": [ { "ob_id": 43928, "uuid": "8bb01a67b7cf40a3b299ee5ca4a78abc", "short_code": "acq", "title": "Acquisition for the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v3.0", "abstract": "The underlying L2 monthly gravity field solutions were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 209956, 209957 ] }, { "ob_id": 43964, "uuid": "fbd631896ffa4e56be292e2e61ed0370", "title": "Composite Process for: CALIPSO Lidar Level 2 Vertical Feature Mask Version 4-51 Product (CAL_LID_L2_VFM-V4-51)", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: CALIPSO Lidar Level 2 Vertical Feature Mask Version 4-51 Product (CAL_LID_L2_VFM-V4-51); \r\n2. Computation: Computation on Raw data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite. The new version 4.51 (V4.51) of the CALIPSO lidar (CALIOP) Level 2 (L2) data products contain a number of improvements and additions over the previous version (V4.2) that was released in October 2018. A summary of the major changes addressed in this release are detailed below, as well as a section high-lighting known issues.", "computationComponent": [ { "ob_id": 43965, "uuid": "29e87bafd01f46208ea8a567f0efe3ae", "short_code": "comp", "title": "Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite VFM product computation", "abstract": "Deployed on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite. Computation to create the Version 4-51 VFM product. The new version 4.51 (V4.51) of the CALIPSO lidar (CALIOP) Level 2 (L2) data products contain a number of improvements and additions over the previous version (V4.2) that was released in October 2018. A summary of the major changes addressed in this release are detailed in the data quality documentation link, as well as a section high-lighting known issues." } ], "acquisitionComponent": [ { "ob_id": 8390, "uuid": "004fde505485417d920e66655a1f65ff", "short_code": "acq", "title": "Acquisition Process for: CALIPSO Lidar Level 2 Vertical Feature Mask Version 3-30 Product (CAL_LID_L2_VFM-ValStage1-V3-30)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP); PLATFORMS: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite; " } ], "identifier_set": [], "responsiblepartyinfo_set": [ 210224 ] }, { "ob_id": 44115, "uuid": "4b3ad2a5993d409ca6c75b2e8eb60d4a", "title": "Composite Process for: CALIPSO Lidar Level 2 1km Cloud Layer Version 4-51 Product (CAL_LID_L2_01kmCLay-Standard-V4-51)", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: CALIPSO Lidar Level 2 1km Cloud Layer Version 4-51 Product (CAL_LID_L2_01kmCLay-Standard-V4-51); \r\n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite;", "computationComponent": [ { "ob_id": 8358, "uuid": "29917310e55f43e29e9b0bb29d1f9382", "short_code": "comp", "title": "Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite", "abstract": "Deployed on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite." } ], "acquisitionComponent": [ { "ob_id": 8420, "uuid": "0a43ba92f0944793a79f650e814e6e8b", "short_code": "acq", "title": "Acquisition Process for: CALIPSO Lidar Level 2 1km Cloud Layer Version 3-30 Product (CAL_LID_L2_01kmCLay-ValStage1-V3-30)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP); PLATFORMS: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Satellite; " } ], "identifier_set": [], "responsiblepartyinfo_set": [ 211006 ] }, { "ob_id": 44178, "uuid": "219b2cf277cf4c889731ff912a497f58", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRFP and CO2_GOS_SRFP v2.0.3 products", "abstract": "The CH4_GO2_SRFP and CO2_GO2_SRFP v2.0.3 products were derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRON-RemoTeC retrieval algorithm.", "computationComponent": [ { "ob_id": 44177, "uuid": "4aee97c1556a4145ac1bb33d97dee9fa", "short_code": "comp", "title": "The SRON-RemoTeC algorithm used to generate the CO2_GO2_SRFP and CH4_GO2_SRFP (SRON Full Physics) v2.0.3 products.", "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane and carbon dioxide using a 'Full Physics' retrieval technique. \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)" } ], "acquisitionComponent": [ { "ob_id": 32858, "uuid": "74166c97d5e74d51ac946aa36431ae95", "short_code": "acq", "title": "Aquisition for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR dataset", "abstract": "The CH4_GO2_SRPR dataset is derived from data from the TANSO-FTS/2 instrument on the GOSAT-2 satellite." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 211252, 211253 ] }, { "ob_id": 44179, "uuid": "b56f7d7a04c44fdf83a1b998751e10e6", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR v2.0.3 product", "abstract": "The CH4_GO2_SRPR v2.0.3 product was derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRON-RemoTeC retrieval algorithm.", "computationComponent": [ { "ob_id": 44180, "uuid": "43226643eaef4460aa5076b4f6575755", "short_code": "comp", "title": "The SRON-RemoTeC algorithm used to generate the CH4_GO2_SRPR (SRON Proxy) v2.0.3 product.", "abstract": "The SRON-RemoTeC retrieval algorithm retrieves column-averaged methane using a 'Proxy' retrieval technique. \r\n\r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)" } ], "acquisitionComponent": [ { "ob_id": 32858, "uuid": "74166c97d5e74d51ac946aa36431ae95", "short_code": "acq", "title": "Aquisition for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR dataset", "abstract": "The CH4_GO2_SRPR dataset is derived from data from the TANSO-FTS/2 instrument on the GOSAT-2 satellite." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 211254, 211255 ] }, { "ob_id": 44314, "uuid": "af808584a43c4712a3866531afcd327e", "title": "Composite Process for: Vertical Profiles of Ozone and other Trace Gases from OMI AURA - Version 2.14", "abstract": "The Ozone Monitoring Instrument (OMI) was an instrument aboard EOS AURA.", "computationComponent": [ { "ob_id": 42339, "uuid": "6f52e38ef1c84008b5f135015f870b35", "short_code": "comp", "title": "RAL Ozone Profile Algorithm", "abstract": "The RAL retrieval scheme derives profiles of number density on a set of pressure levels, spaced approximately every 4-6 km in altitude (taken from the SPARC-DI grid). The optimal estimation method is used. Averaging kernels are provided on this basis. It is noted that the vertical resolution of the retrieval is relatively coarse compared to the vertical grid and that tropospheric levels in particular have significant bias towards the assumed a priori state. It is therefore important to take account of the characterisation of the retrieval provided by the averaging kernels when comparing these results to other data-sets, particularly where those are more highly vertically resolved." } ], "acquisitionComponent": [ { "ob_id": 44313, "uuid": "cb040f5b979244ce8bfcaa1e6d5446a3", "short_code": "acq", "title": "Acquisition for: Vertical Profiles of Ozone and other Trace Gases from OMI AURA - Version 2.14", "abstract": "The Ozone Monitoring Instrument (OMI) was an instrument aboard EOS AURA." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 211993 ] }, { "ob_id": 44319, "uuid": "d97c80c363c04a54808d4807d9b67118", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDVI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025.\r\nDetails on the software can be found in the details/docs tab.", "computationComponent": [ { "ob_id": 44318, "uuid": "9e1b8173d7f3441dadaa60d513cfe32f", "short_code": "comp", "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDVI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" } ], "acquisitionComponent": [ { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 212004 ] }, { "ob_id": 44332, "uuid": "0a058f0b3eb940d09b5a7834fc9163b9", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v2", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset.", "computationComponent": [ { "ob_id": 44333, "uuid": "bf9ac3f9051d4988a45fb7acf9aa944c", "short_code": "comp", "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NBR index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025.\r\nInformation on the software packages can be found in the details/docs tab." } ], "acquisitionComponent": [ { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 212061 ] }, { "ob_id": 44336, "uuid": "d062f4a00382487cb604f54958834f30", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDMI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025", "computationComponent": [ { "ob_id": 44337, "uuid": "6c6e9a5ace66493aae24e254f1177c7d", "short_code": "comp", "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDMI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" } ], "acquisitionComponent": [ { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 212071 ] }, { "ob_id": 44340, "uuid": "ea3640b56554444e85f3df735dbf4ba1", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDWI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025", "computationComponent": [ { "ob_id": 44341, "uuid": "dbdfa589d8ba4e27ab3a67d3e5043a8f", "short_code": "comp", "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDWI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" } ], "acquisitionComponent": [ { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 212081 ] }, { "ob_id": 44532, "uuid": "dfa5d51244ed485a9c8dafbd0ed1f055", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2 (EVI2)", "abstract": "EVI2 equation from Jiang et al. 2008 (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. EVI2 index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "computationComponent": [ { "ob_id": 44533, "uuid": "61b36b80ecd64b9dad766c4ea9e81d9f", "short_code": "comp", "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2 (EVI)", "abstract": "EVI2 equation from Jiang et al. 2008 (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. EVI2 index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" } ], "acquisitionComponent": [ { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213124 ] }, { "ob_id": 44559, "uuid": "e9275ab19e9b4df99bfc98ae55d85d61", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from ERS-2 radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission.", "computationComponent": [ { "ob_id": 41417, "uuid": "722a203405214ccfb77e27ff9b307801", "short_code": "comp", "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load." } ], "acquisitionComponent": [ { "ob_id": 44558, "uuid": "cd3cddaf39584b97b9fffea6a3c65778", "short_code": "acq", "title": "Altimetry data acquired from the RA instrument on ERS2", "abstract": "Altimetry data has been obtained from the RA (Radar Altimeter) instrument on the ERS-2 satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213229 ] }, { "ob_id": 44561, "uuid": "bd7c06b624a94ebf8a5a9003f763653c", "title": "Composite process for JNCC Sentinel-1 indices Analysis Ready Data (ARD) VH/VV Cross Ratio Index", "abstract": "Sentinel-1 dual polarisation data are obtained from the Defra and JNCC Sentinel-1 ARD CEDA dataset. The data are converted from decibel to linear scale prior to the VH/VV calculation and generation of the index files. This is performed in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999. Pixels with values of 50 or higher, or -50 or lower, were assigned the no-data value.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "computationComponent": [ { "ob_id": 44562, "uuid": "132af0ab09d34dd0bf918b6addd96c14", "short_code": "comp", "title": "Computation for JNCC Sentinel-1 indices Analysis Ready Data (ARD) VH/VV Cross Ratio Index", "abstract": "Sentinel-1 dual polarisation data are obtained from the Defra and JNCC Sentinel-1 ARD CEDA dataset. The data are converted from decibel to linear scale prior to the VH/VV calculation and generation of the index files. This is performed in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999. Pixels with values of 50 or higher, or -50 or lower, were assigned the no-data value.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025" } ], "acquisitionComponent": [ { "ob_id": 44578, "uuid": "530e1180024a488884e502bfbf07de14", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1A Synthetic Aperture Radar (SAR) C-band, Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213230 ] }, { "ob_id": 44564, "uuid": "02cd0b5a1ecb4c7190a5a036945bba8d", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3A radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission.", "computationComponent": [ { "ob_id": 44563, "uuid": "09344f0c880a4a368816d9cfd514fb50", "short_code": "comp", "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3a radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load." } ], "acquisitionComponent": [ { "ob_id": 44565, "uuid": "385b69c3f4944fba9abfa64847a53564", "short_code": "acq", "title": "Altimetry data acquired from the SRAL instrument on Sentinel-3a", "abstract": "Altimetry data has been obtained from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel-3A satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213233 ] }, { "ob_id": 44566, "uuid": "90a7c1bc6d404c5bae66272ce6c9b242", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3B radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission.", "computationComponent": [ { "ob_id": 44567, "uuid": "b2860073d21346c597c51a8da6b0005b", "short_code": "comp", "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3B radar altimetry data", "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load." } ], "acquisitionComponent": [ { "ob_id": 44568, "uuid": "87e5162890034ae18a11a7394fa039d2", "short_code": "acq", "title": "Altimetry data acquired from the SRAL instrument on Sentinel-3B", "abstract": "Altimetry data has been obtained from the SRAL (Synthetic aperture Radar Altimeter) instrument on the Sentinel-3B satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213235 ] }, { "ob_id": 44592, "uuid": "035b92bb2c1547428b703a0e6d7f21a7", "title": "EOCIS: Total Column CO Product, V1.0", "abstract": "This dataset was generated from the instrument and computations as detailed below.", "computationComponent": [ { "ob_id": 44591, "uuid": "f639b44f609b4d19b47547876d48fc6b", "short_code": "comp", "title": "University of Leicester IASI retrieval scheme (ULIRS)", "abstract": "This computation involved: ULIRS deployed on JASMIN processing cluster. The ULIRS is an optimal estimation-based retrieval scheme, which utilises equidistant pressure levels and a floating pressure grid based on topography. ULIRS was developed at the University of Leicester through the National Centre for Earth Observation (NCEO)." } ], "acquisitionComponent": [ { "ob_id": 43672, "uuid": "abeb8e1ed6b3499aa7e64761c67ea8bb", "short_code": "acq", "title": "Acquisition for: EOCIS: Total Column CO Product, V1.0", "abstract": "The EOCIS: Total Column CO product, v1.0, has been derived from the IASI instrument on the METOP-B satellite." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213336 ] }, { "ob_id": 44603, "uuid": "88fcd969eb7e4c8dad13d64d0500ec62", "title": "Composite process for the 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 3.00, daily and monthly products", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.", "computationComponent": [ { "ob_id": 44605, "uuid": "f31d788506b94aab8822d8c3c98a958f", "short_code": "comp", "title": "Derivation of 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 3.00, daily and monthly products", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage." } ], "acquisitionComponent": [ { "ob_id": 44604, "uuid": "1b66d0dce4bf456180293dee04708cfb", "short_code": "acq", "title": "Acquisition Process for the 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 3.00, daily and monthly products", "abstract": "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 (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 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." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213524 ] }, { "ob_id": 44630, "uuid": "a04080c1c7984ad8824c57ffbb9303a8", "title": "Composite Process for Black and Bloom Work Package 2: Particulates", "abstract": "Composite process covering Acquisition for: Black and Bloom Work Package 2: Particulates and FLEXPART.", "computationComponent": [ { "ob_id": 44629, "uuid": "54b3003288914bcabb4c56c0b7b313ad", "short_code": "comp", "title": "FLEXPART", "abstract": "Ten-day particle dispersion calculations made using the FLEXPART model initiated from the ground site (67.07° N, 49.38° W 1073 m AMSL) in the ablation zone in southwest Greenland, approximately 35 km from the ice sheet margin. \r\nThe outputs were collated into animations" } ], "acquisitionComponent": [ { "ob_id": 44627, "uuid": "9e19779220d84f09b300e37b263fb8ea", "short_code": "acq", "title": "Acquisition for: Black and Bloom Work Package 2: Particulates", "abstract": "" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213686 ] }, { "ob_id": 44632, "uuid": "326ddde6fc0d49e3b97d59791465ec5c", "title": "Composite Process for Black and Bloom Work Package 2: Particulates", "abstract": "Composite process covering Acquisition for: Black and Bloom Work Package 2: Particulates and FLEXPART.", "computationComponent": [ { "ob_id": 44629, "uuid": "54b3003288914bcabb4c56c0b7b313ad", "short_code": "comp", "title": "FLEXPART", "abstract": "Ten-day particle dispersion calculations made using the FLEXPART model initiated from the ground site (67.07° N, 49.38° W 1073 m AMSL) in the ablation zone in southwest Greenland, approximately 35 km from the ice sheet margin. \r\nThe outputs were collated into animations" } ], "acquisitionComponent": [ { "ob_id": 44631, "uuid": "bd0361938c1a4ba693112a840dbc109f", "short_code": "acq", "title": "Acquisition for: Black and Bloom Work Package 2: Particulates", "abstract": "" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213688 ] }, { "ob_id": 44634, "uuid": "34d4f85f35c4432ab3bfee778b21cf29", "title": "Composite Process for Black and Bloom Work Package 2: Particulates", "abstract": "Composite process covering Acquisition for: Black and Bloom Work Package 2: Particulates and FLEXPART.", "computationComponent": [ { "ob_id": 44629, "uuid": "54b3003288914bcabb4c56c0b7b313ad", "short_code": "comp", "title": "FLEXPART", "abstract": "Ten-day particle dispersion calculations made using the FLEXPART model initiated from the ground site (67.07° N, 49.38° W 1073 m AMSL) in the ablation zone in southwest Greenland, approximately 35 km from the ice sheet margin. \r\nThe outputs were collated into animations" } ], "acquisitionComponent": [ { "ob_id": 44633, "uuid": "3294c782a1634688a0541111013e4cd9", "short_code": "acq", "title": "Acquisition for: Black and Bloom Work Package 2: Particulates", "abstract": "" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213690 ] }, { "ob_id": 44682, "uuid": "22f48eeaec684bd3b24fff794912d2cc", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product, version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpages (https://climate/esa/int/projects/land-surface-temperature)", "computationComponent": [ { "ob_id": 45227, "uuid": "7b19d7110d1344dea40af8fb6505320a", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product, version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpages: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44678, "uuid": "7f4935a24af840d08a5f35cac9389614", "short_code": "acq", "title": "Acquisition Process for the 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, version 3.00", "abstract": "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, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1 and MTSAT 2, Advanced Himawari Imager (AHI) on Himawari 8 and Himawari 9 ; and polar, Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Along-Track Scanning Radiometer 2 (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2), Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and Sentinel-3B, Advanced Very High Resolution Radiometer (AVHRR) on Metop-A, and Visible Infra-red Imaging Radiometer Suite(VIIRS) on Suomi National Polar-orbiting Partnership (Suomi NPP) . 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." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 213978 ] }, { "ob_id": 44694, "uuid": "231890873b5b4660ad5da1628ace4017", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data were derived from the AVHRR/3 (Advanced Very High Resolution Radiometer /3) on the NOAA-15 (National Oceanic and Atmospheric Administration -15) satellite.\r\n\r\nThe 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. For more information see the dataset documentation.", "computationComponent": [ { "ob_id": 44683, "uuid": "43dba09585b84c8d9ca195191fe5f0dd", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19", "abstract": "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.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44689, "uuid": "60c411fc097b4d3ea6a3b997b0d097bc", "short_code": "acq", "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data are derived from the AVHRR-3 (Advanced Very High Resolution Radiometer 3) on the NOAA-15 (National Oceanic and Atmospheric Administration - 15) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214016 ] }, { "ob_id": 44695, "uuid": "2ddea8bf5d5148abba642d4ec3aa02a5", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-16 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data were derived from the AVHRR/3 (Advanced Very High Resolution Radiometer /3) on the NOAA-16 (National Oceanic and Atmospheric Administration -16) satellite.\r\n\r\nThe 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. For more information see the dataset documentation.", "computationComponent": [ { "ob_id": 44683, "uuid": "43dba09585b84c8d9ca195191fe5f0dd", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19", "abstract": "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.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44690, "uuid": "85ccb02c37014d85b41122bcb23230c5", "short_code": "acq", "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-16 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data are derived from the AVHRR-3 (Advanced Very High Resolution Radiometer 3) on the NOAA-16 (National Oceanic and Atmospheric Administration - 16) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214017 ] }, { "ob_id": 44696, "uuid": "fa017794f88848bc8b69e1b9bbda79ec", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-17 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data were derived from the AVHRR/3 (Advanced Very High Resolution Radiometer /3) on the NOAA-17 (National Oceanic and Atmospheric Administration -17) satellite.\r\n\r\nThe 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. For more information see the dataset documentation.", "computationComponent": [ { "ob_id": 44683, "uuid": "43dba09585b84c8d9ca195191fe5f0dd", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19", "abstract": "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.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44691, "uuid": "127cbaf79408467780110aa28a5f2fdd", "short_code": "acq", "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-17 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data are derived from the AVHRR-3 (Advanced Very High Resolution Radiometer 3) on the NOAA-17 (National Oceanic and Atmospheric Administration - 17) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214018 ] }, { "ob_id": 44697, "uuid": "b9b5149f86124b32a94a5faa97db76b1", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-18 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data were derived from the AVHRR/3 (Advanced Very High Resolution Radiometer /3) on the NOAA-18 (National Oceanic and Atmospheric Administration -18) satellite.\r\n\r\nThe 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. For more information see the dataset documentation.", "computationComponent": [ { "ob_id": 44683, "uuid": "43dba09585b84c8d9ca195191fe5f0dd", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19", "abstract": "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.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44692, "uuid": "7f76889d0a804a7d9099fb0fda22d874", "short_code": "acq", "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-18 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data are derived from the AVHRR-3 (Advanced Very High Resolution Radiometer 3) on the NOAA-18 (National Oceanic and Atmospheric Administration - 18) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214019 ] }, { "ob_id": 44698, "uuid": "dafde8e07a354c3d8fe16ee63dbccf25", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-19 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data were derived from the AVHRR/3 (Advanced Very High Resolution Radiometer /3) on the NOAA-19 (National Oceanic and Atmospheric Administration -19) satellite.\r\n\r\nThe 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. For more information see the dataset documentation.", "computationComponent": [ { "ob_id": 44683, "uuid": "43dba09585b84c8d9ca195191fe5f0dd", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19", "abstract": "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.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 44693, "uuid": "8c57156a1fb146d7a1b0bf1a708c9fe3", "short_code": "acq", "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-19 (National Oceanic and Atmospheric Administration), level 3 collated (L3C) global product, version 1.50", "abstract": "The data are derived from the AVHRR-3 (Advanced Very High Resolution Radiometer 3) on the NOAA-19 (National Oceanic and Atmospheric Administration - 19) satellite" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214020 ] }, { "ob_id": 44816, "uuid": "86b83f9979bf403cae40cbda9f31f794", "title": "Composite Process for Copernicus Climate Change Service: Brokered ESA Cloud CCI Cloud Properties L3 data", "abstract": "Composite process covering Acquisition for: Copernicus Climate Change Service: Brokered ESA Cloud CCI Cloud Properties L3 data and Optimal Retrieval of Aerosol and Cloud (ORAC).", "computationComponent": [ { "ob_id": 44815, "uuid": "734f0774e7d9409789fb749c94ceb5d7", "short_code": "comp", "title": "Optimal Retrieval of Aerosol and Cloud (ORAC)", "abstract": "Optimal Retrieval of Aerosol and Cloud (ORAC) - more details on request" } ], "acquisitionComponent": [ { "ob_id": 44813, "uuid": "be157d60b4ec40fabf0833bca73362b2", "short_code": "acq", "title": "Acquisition for: Copernicus Climate Change Service: Brokered ESA Cloud CCI Cloud Properties L3 data", "abstract": "Acquisition for: Copernicus Climate Change Service: Brokered ESA Cloud CCI Cloud Properties L3 data from the Along Track Scanning Radiometer 2 (ATSR-2) and Advanced Along Track Scanning Radiometer (AATSR)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214609 ] }, { "ob_id": 44822, "uuid": "b831a485ecd04b9692bcca0bb8cb35d4", "title": "Composite process for Deformation, Strains, and Velocities for the Tibetan Plateau from Sentinel-1 InSAR, GNSS, and Levelling Data", "abstract": "Using LiCSBAS, we invert for line-of-sight displacement time series and average velocities at ~1 km resolution, applying corrections for troposphere, ionosphere, Earth tides, and plate motions. We also compile GNSS velocities and levelling observations from published studies. Following the velmap approach, we obtain a unified coarse 3D velocity field that fits the GNSS, levelling, and InSAR data, from which we create mosaics of ascending and descending line-of-sight velocities in a Eurasia reference frame. We then invert pixel-by-pixel for the east-west and vertical velocities directly from the referenced line-of-sight velocities, using the north–south velocities from the coarse 3D velocity model as a constraint. Strain and rotation rates are calculated from the horizontal gradients of the median-filtered east-west velocities at InSAR resolution and the north-south velocities from the coarse velocity model. Further details are provided in Wright et al. (2025, Science).", "computationComponent": [ { "ob_id": 44825, "uuid": "74c87adf0d5d43f2a5e9336754afcfe3", "short_code": "comp", "title": "Computation for Deformation, Strains, and Velocities for the Tibetan Plateau", "abstract": "Approximately 44,500 Sentinel-1 SAR acquisitions were processed to generate around 341,400 interferograms at ~100 m resolution using the automated COMET-LiCSAR system. The Tibetan Plateau was divided into 127 ascending and 114 descending frames (each approximately 250 km wide), forming short-baseline interferometric networks with additional 6- and 12-month pairs to reduce phase bias.\r\n\r\nUsing LiCSBAS, line-of-sight displacement time series and average velocities at ~1 km resolution were estimated. Corrections were applied for tropospheric, ionospheric, and solid Earth tidal effects. Velocity uncertainties were flattened via semi-variogram analysis, and Eurasian plate motion was subtracted from all frames.\r\n\r\nA total of 18,203 GNSS velocities and 6,607 levelling rates were compiled from 131 studies published since 2013. The dataset was refined by removing outliers, aligning studies via Euler poles, eliminating outdated or duplicate entries, and merging collocated measurements.\r\n\r\nTo construct a unified coarse velocity model integrating GNSS, levelling, and InSAR data, the velmap approach was followed. This involved inverting for 3D velocities at each node of a triangular mesh spaced by ~0.2° in longitude and latitude, along with frame-specific reference frame adjustment parameters and linear-with-height atmospheric correction terms. The reference frame adjustment parameters consisted of a second-order polynomial surface, and regularization was applied using Laplacian smoothing.\r\n\r\nEast-west and vertical velocities were derived from georeferenced mosaics of ascending and descending line-of-sight velocities, using coarse north-south velocities as constraints. Horizontal strain and rotation rates were calculated from velocity gradients, with spherical corrections applied. A 150 km median filter was applied to east-west velocities to balance resolution and noise.\r\n\r\nFurther details are available in Wright et al. (2025, Science)." } ], "acquisitionComponent": [ { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." }, { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214642 ] }, { "ob_id": 44837, "uuid": "609ae3b07363459e84d4d4f39cf0cacb", "title": "Composite process for: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2022), version 4.0", "abstract": "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.", "computationComponent": [ { "ob_id": 44836, "uuid": "0b9ad9b988ad419fb2fab93ebc4f38c6", "short_code": "comp", "title": "ESA Snow Climate Change Initiative (snow_cci): SWE, v4", "abstract": "The snow_cci SWE product has been based on the ESA GlobSnow SWE retrieval approach (Takala et al. 2011). The retrieval is based on passive microwave radiometer (PMR) data considering the change of brightness temperature due to different snow depth, snow density, grain size and more. The retrieval algorithm handles data from the sensors SMMR, SSM/I, SSMIS, AMSR-E and AMSR-2. The retrieval methodology combines the 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 GlobSnow algorithm implemented for snow_cci version 4 includes the utilisation of an advanced emission model with an improved forest transmissivity module and treatment of sub-grid lake ice. Because of the importance of the weather station snow-depth observations on the SWE retrieval, there is improved screening for consistency through the time series. 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/) The retrieval algorithm has been modified relative to snow_cci v3.1 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. The time series has been extended from version 3.1 by one year, to 2023. SWE products are based on SMMR, SSM/I and SSMIS passive microwave radiometer data for non-alpine regions of the Northern Hemisphere." } ], "acquisitionComponent": [ { "ob_id": 44838, "uuid": "c58d0219d8564f8db93a088001a47c8a", "short_code": "acq", "title": "Acquisition for: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2022), version 4.0", "abstract": "The ESA Snow_cci Snow Water Equivalent (SWE) data 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." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214740, 214741 ] }, { "ob_id": 44839, "uuid": "f305f0d44f194b71ba489f7f8f8119de", "title": "__MUST_UPDATE__20250828102104__ Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v3.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nInput data for v3.0 SCF AVHRR products are as follows: \r\n• EUMETSAT FDR replaces ESA CCI cloud products \r\n(AVHRR global composites and cloud) \r\no Morning and afternoon orbits. \r\no MetOp-Satellites are included \r\no CLARA A3 replaces ESA CCI cloud mask \r\n\r\nAlgorithm improvements for v3.0 SCF AVHRR products are as follows: \r\n• Improved pre-classification of snow-free areas: \r\no Considering the additional orbits \r\n• Improved SCF retrieval: \r\no Update of ref_reflectance values (snow, forest, \r\nground) to consider SZA and VZA changes applying LUT. \r\n• Updated uncertainty estimation accounting for the morning \r\nand afternoon orbits \r\n• Updated post-classification to remove erroneous snow \r\npixels in desert areas. \r\n\r\nAdditional variables for v3.0 SCF AVHRR products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per AVHRR swath) \r\n\r\nExtension of time series (start in 1979): \r\n• Extended from 2019 to 2022", "computationComponent": [ { "ob_id": 33467, "uuid": "0b0e05e096b24d4a92edac27d43fb6cc", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v2.0 product.", "abstract": "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 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 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. \r\n\r\nThe 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." } ], "acquisitionComponent": [ { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0", "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214745, 214746 ] }, { "ob_id": 44840, "uuid": "1e248c1a39d641a7945847d3f70765c4", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v4.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nInput data for v3.0 SCF AVHRR products are as follows: \r\n• EUMETSAT FDR replaces ESA CCI cloud products \r\n(AVHRR global composites and cloud) \r\no Morning and afternoon orbits. \r\no MetOp-Satellites are included \r\no CLARA A3 replaces ESA CCI cloud mask \r\n\r\nAlgorithm improvements for v3.0 SCF AVHRR products are as follows: \r\n• Improved pre-classification of snow-free areas: \r\no Considering the additional orbits \r\n• Improved SCF retrieval: \r\no Update of ref_reflectance values (snow, forest, ground) to consider SZA and VZA changes applying LUT. \r\n• Updated uncertainty estimation accounting for the morning \r\nand afternoon orbits \r\n• Updated post-classification to remove erroneous snow \r\npixels in desert areas. \r\n\r\nAdditional variables for v3.0 SCF AVHRR products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per AVHRR swath) \r\n\r\nExtension of time series (start in 1979): \r\n• Extended from 2019 to 2022", "computationComponent": [ { "ob_id": 44834, "uuid": "274c1d78e3f34dad8d84c8bcb6aa8d83", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v4.0 product", "abstract": "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." } ], "acquisitionComponent": [ { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0", "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 214747, 214748 ] }, { "ob_id": 44895, "uuid": "17e46254bd6b4317a1df978539533bc4", "title": "Composite Process for European Windstorm Events with Damage Potential ", "abstract": "Composite process covering Acquisition for: European Windstorm Events with Damage Potential and Windstorm Tracks.", "computationComponent": [ { "ob_id": 44894, "uuid": "2bb0fafcd143488497e6833baa416bbb", "short_code": "comp", "title": "Windstorm Tracks", "abstract": "cf. Leckebusch et al., 2008" } ], "acquisitionComponent": [ { "ob_id": 44893, "uuid": "c7e6d323146c4ea79e3ff52c6fa5f1ac", "short_code": "acq", "title": "Acquisition for: European Windstorm Events with Damage Potential ", "abstract": "" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215097 ] }, { "ob_id": 44897, "uuid": "199d3b6c94e74a3b9175605fbbaf8933", "title": "Composite Process for European Windstorm Events with Damage Potential ", "abstract": "Composite process covering Acquisition for: European Windstorm Events with Damage Potential and Windstorm Tracks.", "computationComponent": [ { "ob_id": 44894, "uuid": "2bb0fafcd143488497e6833baa416bbb", "short_code": "comp", "title": "Windstorm Tracks", "abstract": "cf. Leckebusch et al., 2008" } ], "acquisitionComponent": [ { "ob_id": 44896, "uuid": "29eb792072bf4a19b8aa7d77bcd57518", "short_code": "acq", "title": "Acquisition for: European Windstorm Events with Damage Potential ", "abstract": "" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215099 ] }, { "ob_id": 44907, "uuid": "c903da4cf1074c9f9761f4439f89a4cd", "title": "Composite Process for Copernicus Land Cover product", "abstract": "This product is developed within the Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring service (LCFM). The workflow consists of four main stages: input data ingestion, pre-processing, modeling, and finally post-processing of output products.\r\n\r\nInput data ingestion:\r\n\r\nCopernicus Sentinel-1 L3 Monthly Mosaics·\r\nCopernicus Sentinel-2 L2A·\r\nCopernicus GLO-30 Digital Elevation Model (DEM)\r\nAgERA5 V1\r\nLand Cover Map Training Data (LCM-TD)\r\nPre-processing:\r\n\r\nDeep-learning (DL) based clouds detection: Land Occlusion Score (LOS) product\r\nLOS weighted compositing and timeseries interpolation\r\nLSF-ANNUAL-S2 and LSF-ANNUAL-S1 extraction\r\nAncillary data preparation: AgERA5 climatic regions embeddings processing\r\nModelling: The backbone to produce the LCM-10 layers is EvoNet, a novel algorithm that integrates the strengths of convolutional neural networks (CNNs) and pixel-based classifiers into a unified framework. EvoNet avoids the inefficiencies of conventional approaches that either rely on multiple regional models, requiring complex post-processing, or exclusively use CNNs or pixel classifiers, each of which has limitations. CNNs excel in generalization but struggle with fine spatial details, while pixel classifiers offer high spatial resolution but are prone to noise and overfitting. The core innovation of EvoNet lies in unifying these strengths with its dual architecture: a CNN-based spatial feature extractor and a multi-layer perceptron (MLP) pixel classifier. \r\n\r\nPost-processing: expert rules polishing and tiling of the final product.", "computationComponent": [ { "ob_id": 44906, "uuid": "7e7cc1392e694b149ae5b4858550a47d", "short_code": "comp", "title": "Computation for the Copernicus Land Cover product", "abstract": "Pre-processing:\r\n\r\nDeep-learning (DL) based clouds detection: Land Occlusion Score (LOS) product\r\nLOS weighted compositing and timeseries interpolation\r\nLSF-ANNUAL-S2 and LSF-ANNUAL-S1 extraction\r\nAncillary data preparation: AgERA5 climatic regions embeddings processing\r\nModelling: The backbone to produce the LCM-10 layers is EvoNet, a novel algorithm that integrates the strengths of convolutional neural networks (CNNs) and pixel-based classifiers into a unified framework. EvoNet avoids the inefficiencies of conventional approaches that either rely on multiple regional models, requiring complex post-processing, or exclusively use CNNs or pixel classifiers, each of which has limitations. CNNs excel in generalization but struggle with fine spatial details, while pixel classifiers offer high spatial resolution but are prone to noise and overfitting. The core innovation of EvoNet lies in unifying these strengths with its dual architecture: a CNN-based spatial feature extractor and a multi-layer perceptron (MLP) pixel classifier. \r\n\r\nPost-processing: expert rules polishing and tiling of the final product." } ], "acquisitionComponent": [ { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." }, { "ob_id": 25439, "uuid": "18f84df32d934058862f2c3990885a4c", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)." }, { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." }, { "ob_id": 13191, "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6", "short_code": "acq", "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)", "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215147 ] }, { "ob_id": 44953, "uuid": "669f35dfbb61483fbd0d8855ddef8124", "title": "NCEO Aboveground Biomass Mexico Map v5.0 2010", "abstract": "The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration / validation framework.", "computationComponent": [ { "ob_id": 32595, "uuid": "6802c93ca9d64942bd1cdb88e23b3546", "short_code": "comp", "title": "NCEO Aboveground Biomass Map v21 2015", "abstract": "Algorithm / method The map shows aboveground woody biomass (AGB) in Kenyan forests. The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration / validation framework. \r\n\r\nTraining dataset Forest inventory dataset collected consisting of 30 m diameter plots gathered in 4-plot clusters. The AGB pools measured were trees, bamboos and lianas. Pantropical allometries (1) were used to estimate AGB. A few plots with extremely large AGB (potentially due to the small plot size) were excluded (>98 percentile) \r\n\r\nSpatial data inputs ALOS-2 PALSAR-2 dual-polarization (2015) and Landsat 8 Surface Reflectance (2015±1)" } ], "acquisitionComponent": [ { "ob_id": 32594, "uuid": "2b5e6abb44844bbfa34220b674d23461", "short_code": "acq", "title": "NCEO Aboveground Biomass Map V21 2015", "abstract": "The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2)" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215365 ] }, { "ob_id": 45095, "uuid": "936e701f5b624bceb37cc958fb65cfb0", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Terra\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/", "computationComponent": [ { "ob_id": 45091, "uuid": "3a36c1fe5a3c461497690d3451b862dc", "short_code": "comp", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 45092, "uuid": "c05705f58bfc4ae396bcc0671c9a1825", "short_code": "acq", "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MODIS; PLATFORMS: Terra" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215895 ] }, { "ob_id": 45096, "uuid": "9bb090bcb10b4a57a797bda7476d173e", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/", "computationComponent": [ { "ob_id": 45094, "uuid": "4e2a942d249347f1bcac25bf1ba25fa4", "short_code": "comp", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 45093, "uuid": "d24761de50cc47da8e09b03a85340f70", "short_code": "acq", "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MODIS; PLATFORMS: Aqua" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215896 ] }, { "ob_id": 45102, "uuid": "ead330d843e743afabd6883e005154ab", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3B satellite.\r\n\r\nFor more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403", "computationComponent": [ { "ob_id": 45099, "uuid": "fa02152813cb4a988a2bfbbb9e37a2f3", "short_code": "comp", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A and Sentinel 3B, level 3 collated (L3C) global product, version 4.00", "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403" } ], "acquisitionComponent": [ { "ob_id": 45101, "uuid": "c542218cdb3c425eb88860da1074982a", "short_code": "acq", "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: SLSTR; PLATFORMS: Sentinel3A;" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215901 ] }, { "ob_id": 45103, "uuid": "de4404e9934147fc9710f5403a0222c8", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3A satellite.\r\n\r\nFor more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403", "computationComponent": [ { "ob_id": 45099, "uuid": "fa02152813cb4a988a2bfbbb9e37a2f3", "short_code": "comp", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A and Sentinel 3B, level 3 collated (L3C) global product, version 4.00", "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403" } ], "acquisitionComponent": [ { "ob_id": 45100, "uuid": "4e850b33e0444559b0896bb4664b0ad1", "short_code": "acq", "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: SLSTR; PLATFORMS: Sentinel3B;" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215902 ] }, { "ob_id": 45110, "uuid": "94e1ee9ff82b468691b44d14e438c2bd", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG MODIS v4.0 product", "abstract": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nAlgorithm improvements for v3.0 SCF MODIS products are as follows: \r\n• Improved pre-classification of snow-free areas (updated NDSI basemap) \r\n• Improved SCF retrieval (update of snow reflectance parameter based on statistical analysis)\r\n• Salt lakes added as additional static mask \r\n• Updated uncertainty estimation accounting for changes in \r\nalgorithm\r\n\r\nAdditional variables in v3.0 SCF MODIS products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per MODIS granule) \r\n\r\nExtension of time series (start in 2000): \r\n• Extended from 2020 to 2022", "computationComponent": [ { "ob_id": 45109, "uuid": "5406563f6d4844d991b5c46f28d3ca4b", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v4.0 product.", "abstract": "The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) 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 (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, 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 method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFG retrieval for remaining pixels. \r\n\r\nThe 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 snow free ground reflectance and snow free forest reflectance maps instead of global constant values, (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 usage of a global forest canopy transmissivity based on tree canopy cover of the year 2000 from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) of the year 2000. The retrieval approach ensures consistency between the SCFG CRDP v4.0 and the Snow Cover Fraction Viewable from above (SCFV) CRDP 4.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ bc13bb02a958449aac139853c4638f32). In non-forested areas, the SCFG and SCFV estimations from MODIS data are the same.\r\n\r\nPermanent 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 manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFG products and associated layers from individual MODIS tiles are merged into daily global SCFG products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.\r\n\r\nInput description:\r\n•\tTerra MODIS Collection 6.1 MOD021KM (Level 1B Calibrated Radiances - 1km; DOI: 10.5067/MODIS/MOD021KM.061) and MOD03 (Geolocation - 1km; DOI: 10.5067/MODIS/MOD03.061) products\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-24) and snow free forest (v4, 2025-05-24), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:" } ], "acquisitionComponent": [ { "ob_id": 32512, "uuid": "b7f993e0c3e745dc9975da8aa580a654", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0", "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215934, 215935 ] }, { "ob_id": 45111, "uuid": "9621afba9d0242e5b0734a90372fa10e", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV MODIS v4.0 product", "abstract": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nAlgorithm improvements for v3.0 SCF MODIS products are as follows: \r\n• Improved pre-classification of snow-free areas (updated NDSI basemap) \r\n• Improved SCF retrieval (update of snow reflectance parameter based on statistical analysis)\r\n• Salt lakes added as additional static mask \r\n• Updated uncertainty estimation accounting for changes in \r\nalgorithm\r\n\r\nAdditional variables in v3.0 SCF MODIS products are as follows: \r\n• Sensor zenith angle in degrees per pixel \r\n• Image acquisition time (scanline time per MODIS granule) \r\n\r\nExtension of time series (start in 2000): \r\n• Extended from 2020 to 2022", "computationComponent": [ { "ob_id": 45108, "uuid": "def822b6166c4e5da4c732b86f72c5a5", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v4.0 product.", "abstract": "The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/375ffdb8f0a445e380b4b9548655f5f9/) which is based on an enhanced version of 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 (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, 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 method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels. \r\n\r\nSpatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from MODIS data are the same. \r\n\r\nPermanent 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 manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFV products and associated layers from individual MODIS tiles are merged into daily global SCFV products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.\r\n\r\nInput description:\r\n•\tTerra MODIS Collection 6.1 MOD021KM (Level 1B Calibrated Radiances - 1km; DOI: 10.5067/MODIS/MOD021KM.061) and MOD03 (Geolocation - 1km; DOI: 10.5067/MODIS/MOD03.061) products\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\no\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-24) and snow free forest (v4, 2025-05-24), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel" } ], "acquisitionComponent": [ { "ob_id": 32512, "uuid": "b7f993e0c3e745dc9975da8aa580a654", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0", "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215936, 215937 ] }, { "ob_id": 45117, "uuid": "f19c7812de3f4b019249e275b368ce9b", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV SLSTR v1.0 product", "abstract": "See the snow_cci documentation for further information on the SLSTR SCFV v1.0 product.", "computationComponent": [ { "ob_id": 45120, "uuid": "1738cfe71a9d4e3e8d75d1cf943fa047", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV SLSTR v1.0 product.", "abstract": "The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites.\r\n\r\nThe retrieval method of the snow_cci SCFV product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498/) which is based on an enhanced version of 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 SLSTR, 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 (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, 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 method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels. \r\n\r\nSpatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from SLSTR data are the same. \r\n\r\nPermanent 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 manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFV products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFV products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification.\r\n\r\nInput Description:\r\n•\tSentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4.\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput Description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel" } ], "acquisitionComponent": [ { "ob_id": 45121, "uuid": "714a364f634a47bd94ea524a5c9767a2", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from SLSTR, v1.0", "abstract": "The snow_cci SCFG and SCFV products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes.\r\n\r\nThe snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCF CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/) is used to ensure consistency between the SCF products across the different sensors and time series. \r\n\r\nSCF products from individual frames are merged into daily global SCFV products.\r\n\r\nAll SCF products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215977, 215978 ] }, { "ob_id": 45118, "uuid": "3de2897374784acd9e4ff6a7c6a20ffe", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG SLSTR v1.0 product", "abstract": "See the snow_cci documentation for further information on the SLSTR SCFG v1.0 product.", "computationComponent": [ { "ob_id": 45119, "uuid": "c7c98daad12c47e884ace9a30e433800", "short_code": "comp", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG SLSTR v1.0 product.", "abstract": "The SCFG product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites.\r\n\r\nThe retrieval method of the snow_cci SCFG product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) 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 SLSTR, 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 (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, 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 method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFG retrieval for remaining pixels. \r\n\r\nThe 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 snow free ground reflectance and snow free forest reflectance maps instead of global constant values, (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 usage of a global forest canopy transmissivity based on tree canopy cover of the year 2000 from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) of the year 2000. \r\n\r\nThe retrieval approach ensures consistency between the SCFG CRDP v1.0 and the Snow Cover Fraction Viewable from above (SCFV) CRDP 1.0 from SLSTR data (https://catalogue.ceda.ac.uk/uuid/f5dce1f7bec2447093cf460a4d3ba2c2) In non-forested areas, the SCFG and SCFV estimations from SLSTR data are the same.\r\n\r\nPermanent 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 manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFG products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFG products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification.\r\n\r\nInput description:\r\n•\tSentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4.\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel" } ], "acquisitionComponent": [ { "ob_id": 45121, "uuid": "714a364f634a47bd94ea524a5c9767a2", "short_code": "acq", "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from SLSTR, v1.0", "abstract": "The snow_cci SCFG and SCFV products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes.\r\n\r\nThe snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCF CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/) is used to ensure consistency between the SCF products across the different sensors and time series. \r\n\r\nSCF products from individual frames are merged into daily global SCFV products.\r\n\r\nAll SCF products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 215979, 215980 ] }, { "ob_id": 45148, "uuid": "79e4f3bf7eff49f0bca0e2ed787d191a", "title": "Composite process for ESA River Discharge Climate Change Initiative (RD_cci): Water Level product, v2.0", "abstract": "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).", "computationComponent": [ { "ob_id": 45146, "uuid": "4a53771be2af4e5e960e5942eb155bb6", "short_code": "comp", "title": "Derivation of the ESA Climate Change Initiative River Discharge Water Level product, v2.0", "abstract": "For information on the derivation of the Water Level dataset see the project documentation \r\n(https://climate.esa.int/projects/river-discharge)" } ], "acquisitionComponent": [ { "ob_id": 45147, "uuid": "84ff965f93fa4a4383baadfcbf60b464", "short_code": "acq", "title": "Acquisition Process for the ESA River Discharge Climate Change Initiative (RD_cci): Water Level product, v2.0", "abstract": "The water level product was derived from the following nadir-viewing satellite radar altimeter missions : ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B, and Sentinel-6" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216062, 216063 ] }, { "ob_id": 45160, "uuid": "67adf22715454dcdb423ad0a4ba7f238", "title": "Composite process for the ESA Precursors for Aerosols and Ozone Climate Change Initiative CO Merged v1.0 product", "abstract": "Level 3 CO product developed by merging satellite data from IASI (on METOP-A, B, and C) and MOPITT (on TERRA).\r\n\r\nAn intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio.\r\n\r\nThe merged dataset includes CO total column monthly 1°x1° resolution grids as well as uncertainty grids, for both daytime and nighttime from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.", "computationComponent": [ { "ob_id": 45158, "uuid": "a210d4aa862246ec96773b0d04761da3", "short_code": "comp", "title": "ESA Precursors for Aerosol and Ozone Climate Change Initiative: Derivation of Merged CO v1.0 product.", "abstract": "An intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio. \r\n\r\nThe merged dataset includes CO total column monthly 1°x1° resolution grids as well as uncertainty grids, for both daytime and nighttime from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.\r\n\r\nThe version number is 1.0. Data are available in NetCDF format." } ], "acquisitionComponent": [ { "ob_id": 45159, "uuid": "b251e2b2bc564410bcdfb215cedf18f3", "short_code": "acq", "title": "Acquisition Process for the ESA Precursors for Aerosol and Ozone Climate Change Initiative: Merged CO v1.0 product", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: IASI, MOPITT; PLATFORMS: METOP-A, B AND C, TERRA" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216109 ] }, { "ob_id": 45171, "uuid": "8722851cddde41b38b7ca835dd4c5dc7", "title": "Composite process for the 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 2.00", "abstract": "Data has been derived from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. \r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage", "computationComponent": [ { "ob_id": 45170, "uuid": "6b1d65fa55984f8d8dea315c9e19bc9f", "short_code": "comp", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 2.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/" } ], "acquisitionComponent": [ { "ob_id": 45173, "uuid": "745f34a5edde4b66b48fc00297faef28", "short_code": "acq", "title": "Acquisition for the 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 2.00", "abstract": "This product uses data from the AVHRR-3 instrument on the METOP-A satellite." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216154 ] }, { "ob_id": 45235, "uuid": "09ece963f03d4b5ba96eb164394ed337", "title": "MACDA 2 Composite Process", "abstract": "MACDA 2 Composite Process", "computationComponent": [ { "ob_id": 45234, "uuid": "5d30c46f88c740e6837ee30a7263d1b8", "short_code": "comp", "title": "Mars Planetary Climate Model (Mars-PCM): Mars_PCM-UK-spectral version", "abstract": "The Mars Planetary Climate Model (Mars-PCM) is maintained by the Laboratoire de Météorologie Dynamique at Sorbonne Université in Paris, France. The version of the model used to produce the MACDA dataset in the CEDA archive is the UK spectral version, which is developed by the Oxford University and the Open University in the UK. The Analysis Correction scheme used to assimilate spacecraft observations in the Mars_PCM-UK-spectral model was originally developed at the UK Met Office and implemented in the model at the Oxford University and The Open University." } ], "acquisitionComponent": [ { "ob_id": 45241, "uuid": "aec6212998af4d29ab195b3236377079", "short_code": "acq", "title": "Acquisition for MACDA 2", "abstract": "Acquisition for MACDA 2" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216449, 216450 ] }, { "ob_id": 45265, "uuid": "09229538315d4f2d88ef2c0183fb1276", "title": "Composite process for the ESA Precursors for Aerosols and Ozone Climate Change Initiative TROPOMI monthly mean level 3 tropospheric nitrogen dioxide (NO2) version 1.0 May 2018- December 2021", "abstract": "L3 data were generated by spatiotemporally averaging operational TROPOMI L2 NO2 v2.3.1 data as part of the ESA CCI precursors project\r\n\r\nTROPOspheric Monitoring Instrument (TROPOMI) global tropospheric nitrogen dioxide (NO2) spatiotemporally averaged per month over a standard grid from May 2018 to December 2021. The dataset is provided in four different spatial resolutions: 0.2x0.2 (900x1800 grid cells), 0.5x0.5 (360x720), 1x1 (180x360), 2x2.5 (91x144) and includes auxilliary variables (e.g. cloud and surface properties, propagated level 3 uncertainties, averaging kernels). The data are provided in monthly netCDF files for each spatial resolution.", "computationComponent": [ { "ob_id": 45266, "uuid": "88d080fefc274ddf848320a1171e8419", "short_code": "comp", "title": "Computation for the ESA Precursors for Aerosols and Ozone Climate Change Initiative TROPOMI monthly mean level 3 tropospheric nitrogen dioxide (NO2) version 1.0 May 2018- December 2021", "abstract": "TROPOspheric Monitoring Instrument (TROPOMI) global tropospheric nitrogen dioxide (NO2) spatiotemporally averaged per month over a standard grid from May 2018 to December 2021. The dataset is provided in four different spatial resolutions: 0.2x0.2 (900x1800 grid cells), 0.5x0.5 (360x720), 1x1 (180x360), 2x2.5 (91x144) and includes auxilliary variables (e.g. cloud and surface properties, propagated level 3 uncertainties, averaging kernels). The data are provided in monthly netCDF files for each spatial resolution.\r\nL3 data were generated by spatiotemporally averaging operational TROPOMI L2 NO2 v2.3.1 data as part of the ESA CCI precursors project." } ], "acquisitionComponent": [ { "ob_id": 45267, "uuid": "a29f931bd9fc4752876ed18453ad381c", "short_code": "acq", "title": "Acquisition process for the ESA Precursors for Aerosols and Ozone Climate Change Initiative TROPOMI monthly mean level 3 tropospheric nitrogen dioxide (NO2) version 1.0 May 2018- December 2021", "abstract": "This acquisition is comprised of the following: Instruments: TROPOMI; and Platforms: Sentinel-5 precursor" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216542 ] }, { "ob_id": 45270, "uuid": "7e15a34786ea4c25ad9451d988fa2841", "title": "Composite process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.", "computationComponent": [ { "ob_id": 45271, "uuid": "a078f93f707542f5b5833c54095a806d", "short_code": "comp", "title": "Computation process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "acquisitionComponent": [ { "ob_id": 45272, "uuid": "9abdfa86b4d54ed4b278d31773e86037", "short_code": "acq", "title": "Acquisition for the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216562 ] }, { "ob_id": 45273, "uuid": "eb361011b5d443ee9ade3dfccce49d78", "title": "Composite process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-B.", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-B data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.", "computationComponent": [ { "ob_id": 45274, "uuid": "d31f7097bd724286a3c170a13f46b234", "short_code": "comp", "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-B.", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-B data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "acquisitionComponent": [ { "ob_id": 45275, "uuid": "67b11c1feec34dc680fffcf6529b1411", "short_code": "acq", "title": "Acquisition of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-B.", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-B data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216567 ] }, { "ob_id": 45276, "uuid": "a655135faae44c4384290909d2fad911", "title": "Composite process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-C", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.", "computationComponent": [ { "ob_id": 45277, "uuid": "edbf03e6afb74a9594ba91062b2eaaa6", "short_code": "comp", "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-C.", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "acquisitionComponent": [ { "ob_id": 45278, "uuid": "467bcd03033e44999e431d3374096835", "short_code": "acq", "title": "Acquisition of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-C", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216572 ] }, { "ob_id": 45279, "uuid": "f10fd7cff9594506a3e26309904336ae", "title": "Composite process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A/B/C.", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A/B/C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.", "computationComponent": [ { "ob_id": 45280, "uuid": "a70005a91d5642a78a8342ca01b2ad9c", "short_code": "comp", "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A/B/C", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A/B/C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "acquisitionComponent": [ { "ob_id": 45281, "uuid": "c2af792c03e748d0a97fb85c879dc872", "short_code": "acq", "title": "Acquisition process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A/B/C", "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A/B/C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216577 ] }, { "ob_id": 45285, "uuid": "5b14cf60490245d8aefebb5bb205b3ef", "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 4 datasets", "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 4 datasets come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A).", "computationComponent": [ { "ob_id": 45284, "uuid": "10a684056adb42d8b3f8e04f310ba9db", "short_code": "comp", "title": "Derivation of ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 4 datasets", "abstract": "For information on the derivation of the Sea_State_cci Global remote sensingmulti-mission along-track significant wave height from altimetery version 4 products please see the product user guide." } ], "acquisitionComponent": [ { "ob_id": 45283, "uuid": "8c7166d346f54395b0d4c5fdff777e68", "short_code": "acq", "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission gridded significant wave height from altimetery, v4 datasets", "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission gridded significant wave height from altimeter, v4 datasets come from multiple satellite missions spanning from 2002 to 2020 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A)." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216599 ] }, { "ob_id": 45292, "uuid": "9b92b577e09440bca9941549654f3d19", "title": "Composite process of ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0", "abstract": "Composite process of ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0", "computationComponent": [ { "ob_id": 45293, "uuid": "8a5d84c459c544189c12f7cb6d0fc0e5", "short_code": "comp", "title": "Computation of ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0", "abstract": "The Formaldehyde(HCHO) Climate Data Record (CDR) product is a Level 3 HCHO product developed by using satellite data from the TROPOMI instrument (on S5P) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\nThis dataset provides gridded HCHO tropospheric column densities of monthly 0.125°x0.125° resolution grids from May 2018 to December 2024.\r\n\r\nCompared to the operational TROPOMI product, the air mass factors have been reprocessed using an update albedo climatology, and the CAMS Reanalysis Model for the a priori vertical profiles. The background correction and the quality values have also been updated.\r\n\r\nIn addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the Level 3 data files contain several additional parameters and diagnostic information such as uncertainties, a priori profiles and averaging kernels." } ], "acquisitionComponent": [ { "ob_id": 45294, "uuid": "dee9b03d75f841039aafa493073b3563", "short_code": "acq", "title": "Acquisition of ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0", "abstract": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0" } ], "identifier_set": [], "responsiblepartyinfo_set": [ 216651 ] }, { "ob_id": 45380, "uuid": "a552ac7bbe954199848281b68a5e7e34", "title": "Composite process for the Deformation, Strains and Velocities for the Alpine Himalayan Belt from trans-continental Sentinel-1 InSAR & GNSS", "abstract": "The interferograms are processed from Sentinel-1 Level 1 (L1) Synthetic Aperture Radar (SAR) imagery using the Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) routine, further corrected for troposphere, ionosphere and solid earth tides. The average line-of-sight (LOS) velocities and associated uncertainties are derived from frame-based eight-year time series, which are inverted from networks of short temporal baseline interferograms using the Looking Into Continents from Space with Small Baseline Subset (LiCSBAS) method. The scaled uncertainties are the LOS uncertainties with referencing effects corrected by fitting a spherical, exponential, or linear model to the scatter points between uncertainty and distance from the reference. Further details are provided in Elliott et al. (2026, Remote Sensing of Environment).", "computationComponent": [], "acquisitionComponent": [ { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." }, { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq", "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.", "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode." } ], "identifier_set": [], "responsiblepartyinfo_set": [ 217141 ] } ] }