{"type": "FeatureCollection", "features": [{"id": "dea_wofs_ls", "type": "Feature", "geometry": null, "time": "2024-02-05", "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": "2024-02-05", "type": "dataset", "language": "en", "externalIds": [{"value": "https://explorer.digitalearth.africa/products/wofs_ls"}], "title": "Flood Mapping", "description": "Historic Flood Mapping Water Observations from Space", "formats": ["stac item", "canonical"], "keywords": ["Continental", "land", "hydrology"], "providers": [{"name": null, "organization": "Unknown", "positionName": null, "roles": [{"name": "example"}], "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://explorer.digitalearth.africa/stac/collections/wofs_ls", "name": "wofs_ls", "description": "Historic Flood Mapping Water Observations from Space", "type": "stac item", "rel": null}, {"href": "https://github.com/lsc-hubs/kenya-catalogue/tree/main/portals/Africa/explorer.digitalearth.africa/dea_wofs_ls.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "dea_wofs_ls", "name": "item", "description": "dea_wofs_ls", "href": "https://kenya.lsc-hubs.org/cat/collections/metadata:main/items/dea_wofs_ls"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://kenya.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "landportal", "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": "2024-02-05", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}], "updated": "2024-02-05", "type": "service", "language": "en", "externalIds": [{"value": "https://www.landportal.org/"}], "title": "landportal.org", "description": "The Land Portal Foundation believes that access to information is crucial for achieving good land governance and securing land rights for landless and vulnerable people. 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Online and offline dialogues help raise awareness of hidden issues (e.g. sexual extortion in the land sector) and provide opportunities for collaboration between diverse groups and organizations.  Visualize and synthesize data from diverse sources into unique portfolios of contextualized and actionable land governance information. Our thematic and country portfolios provide free virtual repositories of bibliographic and multimedia resources, helping to reveal new trends and expose existing gaps in the information landscape.  Support information providers from the land sector to utilize Open Data standards and encourage the creation and uptake of a standards-based data infrastructure based upon FAIR (Findable, Accessible, Interoperable and Reusable) principles. The increased adoption of commonly agreed data standards is helping information derived from local groups, national organizations and international institutions to coexist within the wider information ecosystem.  Explain and monitor indicators on land governance including the Sustainable Development Goals, the Voluntary Guidelines on the Responsible Governance of Tenure (VGGTs) and other international/regional frameworks for land monitoring. 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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": ["Global", "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://github.com/lsc-hubs/kenya-catalogue/tree/main/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://kenya.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://kenya.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": "2024-02-05", "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": []}, {"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}], "updated": "2024-02-05", "type": "dataset", "language": "en", "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", "Global", "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://github.com/lsc-hubs/kenya-catalogue/tree/main/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://kenya.lsc-hubs.org/cat/collections/metadata:main/items/gaez"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://kenya.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": "2024-02-05", "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": []}, {"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}], "updated": "2024-02-05", "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", "Global", "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://github.com/lsc-hubs/kenya-catalogue/tree/main/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://kenya.lsc-hubs.org/cat/collections/metadata:main/items/isimip"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://kenya.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": "2024-02-05", "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": "2024-02-05", "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", "Global", "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://github.com/lsc-hubs/kenya-catalogue/tree/main/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://kenya.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://kenya.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": "2024-02-05", "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": "2024-02-05", "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", "Global", "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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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", "Global", "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. 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