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The development process applies predictive machine learning technology using data from over 130,000 soil samples, much of it based on years of research by the African Soil Information Service (AfSIS) and other soil data resources.", "formats": ["API", "WWW:Link", "citation", "canonical"], "keywords": ["Soil", "soil chemicophysical properties", "land cover", "soil fertility management", "soil water conservation", "nutrients", "digital soil mapping", "aluminium", "total carbon", "organic carbon", "nitrogen", "pH", "effective cation exchange capacity", "bulk density", "clay", "depth to bedrock", "sand", "silt", "slope angle", "stone content", "USDA texture class", "calcium", "iron", "magnesium", "phosphorus", "extractable potassium", "extractable sulphur", "zinc", "continental"], "providers": [{"name": null, "organization": null, "positionName": null, "roles": [{"name": "pointOfContact"}], "contactInfo": {"phone": {"work": "+44 (0)7877 699541"}, "email": {"work": "info@isda-africa.com"}, "address": {"work": {"deliveryPoint": null, "city": "Harpenden", "administrativeArea": null, "postalCode": null, "country": "UK"}}, "url": {"url": "https://www.isda-africa.com", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://api.isda-africa.com/v1/layers/?key=AIzaSyCruMPt43aekqITCooCNWGombhbcor3cf4", "name": "Google API for Soil data", "description": "API requires a API key", "type": "API", "rel": null}, {"href": "https://www.isda-africa.com/isdasoil/developer", "name": "API documentation", "description": "Developer documentation", "type": "WWW:Link", "rel": null}, {"href": "https://www.isda-africa.com/isdasoil", "name": "Project website", "description": null, "type": "WWW:Link", "rel": null}, {"href": "https://doi.org/10.1371/journal.pbio.3001441", "name": "iSDAsoil The first continent-scale soil property map at 30 m resolution provides a soil information revolution for Africa.", "description": null, "type": "citation", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/agrofims/isda-service.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "related", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/isda-soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "isda-service", "name": "item", "description": "isda-service", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/isda-service"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "wapor-koga-ethiopia", "type": "Feature", "geometry": null, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2024-06-24", "type": "service", "language": "en", "externalIds": [{"value": "https://data.apps.fao.org/map/catalog/srv/api/records/1f462050-8c95-41cd-be29-854ae0059251/formatters/xml"}], "title": "Wapor datasets for Koga, Ethiopia", "description": "The seasonal Gross Biomass Water Productivity expresses the quantity of output (above ground biomass production) in relation to the total volume of water consumed (actual EvapoTranspiration) during the growing cycle of the vegetation. By relating biomass production to total EvapoTranspiration (sum of soil evaporation, canopy transpiration and interception), this indicator provides insights on the impact of vegetation development on consumptive water use and thus on water balance in a given domain. When the focus is on monitoring performance of irrigated agriculture in relation to water consumption, it is more appropriate to use transpiration alone as a denominator, as a measure of water beneficially consumed by the plant. This latter indicator, for which we use the term \\\"net water productivity\\\", provides useful information on how effectively vegetation (and particularly crops) uses water to develop its biomass (and thus yield).", "formats": ["www:link", "canonical"], "keywords": ["wapor", "National", "crop", "land", "water", "evapotranspiration"], "providers": [{"name": null, "organization": "FAO", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "wapor@fao.org"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": "None", "country": null}}, "url": {"url": "https://wapor.apps.fao.org/", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://wapor.apps.fao.org/catalog/WAPOR_2/3?area=AWA", "name": null, "description": "Catalogue", "type": "www:link", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/ET/wapor/wapor-koga-ethiopia.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "wapor-koga-ethiopia", "name": "item", "description": "wapor-koga-ethiopia", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/wapor-koga-ethiopia"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "c59d0162-a258-4210-af80-777d7929c512", "type": "Feature", "geometry": null, "time": ["1986-01-01", "2016-12-31"], "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2022-02-08T10:51:27", "type": "dataset", "created": "2019-01-01", "language": "en", "externalIds": [{"value": "https://data.isric.org/geonetwork/srv/metadata/c59d0162-a258-4210-af80-777d7929c512/formatters/xml"}], "title": "Global Soil Salinity Map", "description": "This dataset includes global soil salinity layers for the years 1986, 1992, 2000, 2002, 2005, 2009 and 2016. The maps were generated with a random forest classifier that was trained using seven soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. The validation accuracy of the resulting maps was in the range of 67\u00e2\u20ac\u201c70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Further details are provided in a peer-reviewed journal article (https://doi.org/10.1016/j.rse.2019.111260). The code and data used to produce the global soil salinity maps can be accessed by registered Google Earth Engine users at https://code.earthengine.google.com/d43e5a92ae1deed32a0929f57b572756.", "formats": ["DOI", "WWW:DOWNLOAD-1.0-ftp--download", "WWW:DOWNLOAD-1.0-http--download", "WWW:LINK-1.0-http--related", "canonical"], "keywords": ["soil", "soil chemicophysical properties", " soil fertility", " soil water conservation", "salinity", "digital soil mapping", "electrical conductivy", "global map", "soilgrids", "wosis", "landsat", "thermal", "salinisation", "Soil science", "Global"], "providers": [{"name": "Harm Bartholomeus", "organization": "Wageningen University", "positionName": null, "roles": [{"name": "pointOfContact"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "harm.bartholomeus@wur.nl"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": "None", "country": null}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2019.111260", "name": "Article", "description": null, "type": "DOI", "rel": "download"}, {"href": "https://files.isric.org/public/global_soil_salinity/", "name": "Download VRT files (all years)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap1986/", "name": "Download GeoTIFF (1986)", "description": null, "type": "WWW:DOWNLOAD-1.0-http--download", "rel": "download"}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap1992/", "name": "Download GeoTIFF (1992)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap2000/", "name": "Download GeoTIFF (2000)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap2002/", "name": "Download GeoTIFF (2002)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": null}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap2005/", "name": "Download GeoTIFF (2005)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": null}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap2009/", "name": "Download GeoTIFF (2009)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": null}, {"href": "https://files.isric.org/public/global_soil_salinity/salmap2016/", "name": "Download GeoTIFF (2016)", "description": null, "type": "WWW:DOWNLOAD-1.0-ftp--download", "rel": null}, {"href": "https://code.earthengine.google.com/d43e5a92ae1deed32a0929f57b572756", "name": "Code on Earth Engine", "description": null, "type": "WWW:LINK-1.0-http--related", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/data.isric.org/c59d0162-a258-4210-af80-777d7929c512.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "c59d0162-a258-4210-af80-777d7929c512", "name": "item", "description": "c59d0162-a258-4210-af80-777d7929c512", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/c59d0162-a258-4210-af80-777d7929c512"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "gaez", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-180.0, -90.0], [-180.0, 90.0], [180.0, 90.0], [180.0, -90.0], [-180.0, -90.0]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}], "updated": "2024-06-24", "type": "dataset", "language": "en", "externalIds": [{"value": "https://doi.org/10.7910/DVN/HJYYTI"}], "title": "Agro-ecological Zones of Africa", "description": "Agroecological zones (AEZs) are geographical areas exhibiting similar climatic conditions that determine their ability to support rainfed agriculture. At a regional scale, AEZs are influenced by latitude, elevation, and temperature, as well as seasonality, and rainfall amounts and distribution during the growing season. The resulting AEZ classifications for Africa have three dimensions: major climate zone (tropics or subtropics), moisture zones (water availability) and highland/lowland (warm or cool based on elevation).", "formats": ["OGC:WMS", "canonical"], "keywords": ["Climate", "Altitude", "Rainfall", "Temperature", "Agroecology", "Agriculture", "Suitability", "Agroecological zones", "Land", "land suitability", "soil fertility management", "continental"], "providers": [{"name": null, "organization": "International Food Policy Research Institute (IFPRI)", "positionName": null, "roles": [{"name": "pointOfContact"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": null}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://gaez-services.fao.org/server/services/LR/ImageServer/WMSServer", "name": "LR:AEZ Classification (33 Classes)", "description": null, "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/gaez/gaez.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "gaez", "name": "item", "description": "gaez", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/gaez"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "isimip", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[18.0, 36.0], [18.0, 36.0], [60.0, 36.0], [60.0, 36.0], [18.0, 36.0]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}], "updated": "2024-06-24", "type": "service", "created": "2021-01-01", "language": "english", "title": "The Inter-Sectoral Impact Model Intercomparison Project", "description": "Here you will find data relating to the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Use of these data are subject to the ISIMIP terms of use. Documentation of the impact models that provided impact simulations for this archive can be found in the ISIMIP Impact Model Database.", "formats": ["www", "canonical"], "keywords": ["Land", "climate", "soil fertility management", "soil water conservation", "continental"], "providers": [{"name": null, "organization": "Potsdam Institute for Climate Impact Research (PIK).", "positionName": null, "roles": [{"name": "pointOfContact"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "info@isimip.org"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": null}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://data.isimip.org", "name": "Catalogue", "description": null, "type": "www", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/isimip/isimip.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "isimip", "name": "item", "description": "isimip", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/isimip"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "digitalearth-gm_s2_annual", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-27.86, -48.31], [-27.86, 39.0], [65.67, 39.0], [65.67, -48.31], [-27.86, -48.31]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2024-06-24", "type": "dataset", "language": "en", "title": "gm_s2_annual", "description": "Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.\nOne approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.\nIn addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:\n- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.\n- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.\n- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.\nMore techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)\nThis product has a spatial resolution of 10 m and is available annually for 2017 to 2021.\nIt is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.\nAnnual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.\nFor more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "gm_s2_annual", "description": "Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.\nOne approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.\nIn addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:\n- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.\n- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.\n- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.\nMore techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)\nThis product has a spatial resolution of 10 m and is available annually for 2017 to 2021.\nIt is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.\nAnnual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.\nFor more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312", "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/wms/digitalearth-gm_s2_annual.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "digitalearth-gm_s2_annual", "name": "item", "description": "digitalearth-gm_s2_annual", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/digitalearth-gm_s2_annual"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "aquamaps", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-180.0, -90.0], [-180.0, 90.0], [180.0, 90.0], [180.0, -90.0], [-180.0, -90.0]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2024-06-24", "type": "dataset", "language": "en", "title": "AQUAMAPS:gmia_v5", "formats": ["OGC:WMS", "canonical"], "keywords": ["gmia_v5", "WCS", "GeoTIFF", "land", "land management"], "providers": [{"name": null, "organization": "FAO", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": "None", "country": null}}, "url": {"url": "http://geoserver.org", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://io.apps.fao.org/geoserver/wms", "name": "AQUAMAPS:gmia_v5", "description": null, "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/wms/aquamaps.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "aquamaps", "name": "item", "description": "aquamaps", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/aquamaps"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "digitalearth-ls9_sr", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-28.2, -48.51], [-28.2, 39.95], [65.02, 39.95], [65.02, -48.51], [-28.2, -48.51]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2024-06-24", "type": "dataset", "language": "en", "title": "ls9_sr", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.\nMore techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).\nLandsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.\nLandsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.\nFor more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "ls9_sr", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.\nMore techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).\nLandsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.\nLandsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.\nFor more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/ethiopia-hub/-/blob/ethiopia/portals/Global/wms/digitalearth-ls9_sr.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "digitalearth-ls9_sr", "name": "item", "description": "digitalearth-ls9_sr", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main/items/digitalearth-ls9_sr"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://ethiopia.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "digitalearth-s2_l2a", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-27.0, -47.7], [-27.0, 38.85], [65.01, 38.85], [65.01, -47.7], [-27.0, -47.7]]]}, "time": "2024-06-24", "properties": {"themes": [{"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2024-06-24", "type": "dataset", "language": "en", "title": "s2_l2a", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180\u00b0 apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands.\nThis product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000.\nSentinel-2 Level-2A data are provided by the European Space Agency (ESA).  Data prior to 2017 are processed from Level-1C to Level-2A with ESA's Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc.\nFor more information on the Sentinel-2 Level-2A surface reflectance product, see https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "Land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "s2_l2a", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180\u00b0 apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands.\nThis product has a temporal coverage of 2017 to current and is updated as new images are acquired. 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