EOC Land Coverage ServiceThis Web Coverage Service provides access to land products within the Earth Observation Center (EOC).DLREOCurn:ogc:service:wcs2.0.11.1.11.1.0http://www.opengis.net/spec/WCS/2.0/conf/corehttp://www.opengis.net/spec/WCS_protocol-binding_get-kvp/1.0.1http://www.opengis.net/spec/WCS_protocol-binding_post-xml/1.0http://www.opengis.net/spec/WCS_service-extension_crs/1.0/conf/crs-gridded-coveragehttp://www.opengis.net/spec/GMLCOV/1.0/conf/gml-coveragehttp://www.opengis.net/spec/GMLCOV/1.0/conf/special-formathttp://www.opengis.net/spec/GMLCOV/1.0/conf/multiparthttp://www.opengis.net/spec/WCS_service-extension_scaling/1.0/conf/scalinghttp://www.opengis.net/spec/WCS_service-extension_crs/1.0/conf/crshttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/interpolationhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/interpolation-per-axishttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/nearest-neighborhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/linearhttp://www.opengis.net/spec/WCS_service-extension_interpolation/1.0/conf/cubichttp://www.opengis.net/spec/WCS_service-extension_range-subsetting/1.0/conf/record-subsettingNONENONEEarth Observation Center (EOC), DLRGeoservice ManagerGeospatial Service ManagerMünchener Straße 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Type Maps Germany, YearlyThe layers depict 18 crop types for Germany since 2018 based on Sentinel-1 and Sentinel-2 data with a resolution of 10m.DLREOCland__CROPTYPES_DE_P1YRectifiedGridCoverage5.255118495406184 46.70231597736549416.441881475621066 55.888812061049015.25505226972244 46.7023419406880816.44182029833741 55.8887694602763Degree of Urbanization - Continuous - Germany, 2018This layer shows the probability of the degree of urbanization in Germany in % in 2018.URBAN_DEG_PERC_DE_2018WCSImageMosaicland__URBAN_DEG_PERC_DE_2018RectifiedGridCoverage5.472240368312381 47.1705110471236915.490696346402764 55.0685963447477465.472240368312381 47.1705110471236915.490696346402764 55.068596344747746Degree of Urbanization - Discrete - Germany, 2018This layer shows the classification into 5 categories of the degree of urbanization in Germany in 2018.URBAN_DEG_DISC_DE_2018WCSImageMosaicland__URBAN_DEG_DISC_DE_2018RectifiedGridCoverage5.472240368312381 47.1705110471236915.490696346402764 55.0685963447477465.472240368312381 47.1705110471236915.490696346402764 55.068596344747746ESA-CCI Land-Ocean MapLand-ocean map derived from ESA CCI Land Cover Dataset.DLREOCGWPMODISESA CCILandcoverLand-ocean mapland__ESA_LAND_OCEAN_MAPRectifiedGridCoverage-180.0 -89.99998848179.99997696000003 90.00000000000001-180.0 -89.99998848179.99997696000003 90.00000000000001FCCL Germany, MonthlyThe Forest Canopy Cover Loss (FCCL) shows the forest canopy cover loss in Germany in monthly resolution between September 2017 and September 2024 at 10 m spatial resolution.FCCL_DE_P1MWCSGeoTIFFland__FCCL_DE_P1MRectifiedGridCoverage4016550.0 2672950.04684770.0 3562840.05.230971659340296 47.0643145039290715.694865911359667 55.169891337305664FCCL Germany, Observed ChangesThe Forest Canopy Cover Loss (FCCL) monthly observed changes displays how often an observation exceeded a threshold. It is also counted in pixels not labeled as FCCL.FCCL_DE_P1M_CHANGESWCSGeoTIFFland__FCCL_DE_P1M_CHANGESRectifiedGridCoverage4016550.0 2672950.04684770.0 3562840.05.230971659340296 47.0643145039290715.694865911359667 55.169891337305664FCCL Germany, Percentage of GapsDer Prozentsatz der Lücken in der Waldbedeckung (FCCL) zeigt den Prozentsatz der Lücken (d.h. eine Beobachtung, die einen Schwellenwert nicht überschreitet) in Pixeln, die als FCCL gekennzeichnet sind.FCCL_DE_P1M_GAPSWCSGeoTIFFland__FCCL_DE_P1M_GAPSRectifiedGridCoverage4016550.0 2672950.04684770.0 3562840.05.230971659340296 47.0643145039290715.694865911359667 55.169891337305664FCCL Germany, Result MaskDie monatliche Ergebnismaske für den Verlust der Waldbedeckung (Forest Canopy Cover Loss, FCCL) zeigt eine auf Entscheidungsbäumen basierende Klassifizierung der FCCL-Schicht in intakten Wald, (konsistenten) FCCL und zeitliche Veränderungen in Nadel-, Laub- und nicht klassifizierten Wäldern.FCCL_DE_P1M_MASKWCSGeoTIFFland__FCCL_DE_P1M_MASKRectifiedGridCoverage4016550.0 2672950.04684770.0 3562840.05.230971659340296 47.0643145039290715.694865911359667 55.169891337305664Forest Structure - Canopy Height (95th percentile)DLREOCGeoForPyForestForest CoverDeforestationParaguayLandsat-8Sentinel-2Time Seriesland__FOREST_RH95_S2_PRYRectifiedGridCoverage-93819.39611245238 7183800.092289128484970.6038875476 7867130.092289128-62.89458286131733 -25.462772398555895-57.14304636612077 -19.20248473003372Forest Structure - Foliage Height Diversity IndexDLREOCGeoForPyForestForest CoverDeforestationParaguayLandsat-8Sentinel-2Time Seriesland__FOREST_FHD_S2_PRYRectifiedGridCoverage-93819.39611245238 7183800.092289128484970.6038875476 7867130.092289128-62.89458286131733 -25.462772398555895-57.14304636612077 -19.20248473003372Forest Structure - Plant Area IndexDLREOCGeoForPyForestForest CoverDeforestationParaguayLandsat-8Sentinel-2Time Seriesland__FOREST_PAI_S2_PRYRectifiedGridCoverage-93819.39611245238 7183800.092289128484970.6038875476 7867130.092289128-62.89458286131733 -25.462772398555895-57.14304636612077 -19.20248473003372Forest Structure - Total Canopy CoverDLREOCGeoForPyForestForest CoverDeforestationParaguayLandsat-8Sentinel-2Time Seriesland__FOREST_COVER_S2_PRYRectifiedGridCoverage-93819.39611245238 7183800.092289128484970.6038875476 7867130.092289128-62.89458286131733 -25.462772398555895-57.14304636612077 -19.20248473003372Forest Structure Germany - Above-Ground Biomass Density - 2017-2023This map shows the Above-ground biomass density (AGBD) in Germany for the years 2017 to 2023DLREOCLAXLandSentinel-1Sentinel-2GEDIForest StructureYearlyGermanyAbove-Ground Biomass Densityland__FOREST_STRUCTURE_DE_AGBD_P1YRectifiedGridCoverage5.867615772811891 47.2700689025965115.041211454240443 55.058192921327535.867615772811891 47.2700689025965115.041211454240443 55.05819292132753Forest Structure Germany - Canopy Cover - 2017-2023This map shows the canopy cover in Germany for the years 2017 to 2023DLREOCLAXLandSentinel-1Sentinel-2GEDIForest StructureYearlyGermanyCanopy Coverland__FOREST_STRUCTURE_DE_COVER_P1YRectifiedGridCoverage5.867615772811891 47.2700689025965115.041211454240443 55.058192921327535.867615772811891 47.2700689025965115.041211454240443 55.05819292132753Forest Structure Germany - Canopy Height - 2017-2023This map shows the canopy height in Germany for the years 2017 to 2023DLREOCLAXLandSentinel-1Sentinel-2GEDIForest StructureYearlyGermanyCanopy Heightland__FOREST_STRUCTURE_DE_RH95_P1YRectifiedGridCoverage5.867615772811891 47.2700689025965115.041211454240443 55.058192921327535.867615772811891 47.2700689025965115.041211454240443 55.05819292132753Forest cover changes in the Paraguayan Chaco 1987-2020The products depicts per-pixel forest cover for Paraguay in 30 m spatial resolution from 1986 till 2020.DLREOCGeoForPyForestForest CoverDeforestationParaguayLandsat-8Sentinel-2Time Seriesland__FOREST_COVER_PRY_1986_2020_P1YRectifiedGridCoverage-82375.0 7195626.0484895.0 7865856.0-62.77638749149846 -25.355977047140737-57.14377600015042 -19.217257950131053Global SnowPack - Accuracy (Days-To-Cloudfree)The XCC10A1 files show the time distance to the preceding day on which no gap-filling took place. However, these can only be used as a proxy for the overall accuracy, since gaps within the polar night are very likely to have snow cover, but at lower latitudes, the accuracy can drop drastically after just a few days.DLREOCLandGlobal SnowpackdailySnow Cover ExtentNear Real-TimeSCE-NRTland__GSP_XCC10A1_P1DRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - Snow Cover Duration (SCD)The snow cover duration for the entire hydrological year (SCD).DLREOCLandGlobal SnowpackSnow Cover DurationSCDland__GSP_SCD_P1YRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - Snow Cover Duration Early Season (SCDE)The snow cover duration for early season, which extends from autumn to midwinter (SCDE).DLREOCLandGlobal SnowpackSnow Cover Duration Early SeasonSCDEland__GSP_SCDE_P1YRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - Snow Cover Duration Late Season (SCDL)The snow cover duration for late season, which extends over the period from mid-winter to the end of summer (SCDL).DLREOCLandGlobal SnowpackSnow Cover Duration Late SeasonSCDLland__GSP_SCDL_P1YRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - Snow Cover Extent (SCE)The snow cover extent (SCE) is the result of the Global SnowPack processor's interpolation steps and all data gaps have been filled.DLREOCLandGlobal SnowpackdailySnow Cover ExtentSCEland__GSP_SCE_P1DRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - Snow Cover Extent Near Real-Time (SCE NRT)GSP - Global Snowpack Daily - SCE NRTDLRland__GSP_SCE_NRT_P1DRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - The arithmetic mean of the Snow Cover Duration (SCDmean)The arithmetic mean of the snow cover duration for the entire hydrological year (SCDmean) from 2001 until the current year.DLREOCLandGlobal SnowpackSnow Cover DurationSCDArithmethic Meanland__GSP_SCD_MEANRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - The arithmetic mean of the Snow Cover Duration Early Season (SCDEmean)The arithmetic mean of the snow cover duration for early season, which extends from autumn to midwinter (SCDEmean) from 2001 until the current year.DLREOCLandGlobal SnowpackSnow Cover Duration Early SeasonSCDEArithmethic Meanland__GSP_SCDE_MEANRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global SnowPack - The arithmetic mean of the Snow Cover Duration Late Season (SCDLmean)The arithmetic mean of the snow cover duration for late season, which extends over the period from mid-winter to the end of summer (SCDLmean) from 2001 until the current year.DLREOCLandGlobal SnowpackSnow Cover Duration Late SeasonSCDLArithmethic Meanland__GSP_SCDL_MEANRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global WaterPack MonthlyThis monthly layer contains information on how often a pixel was detected as open surface water with pixel values between 0 and 31.DLREOCGWPMODISMonthlyland__GWP_P1MRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0Global WaterPack YearlyThis yearly layer contains information on how often a pixel was detected as open surface water with pixel values between 0 and 365 (366 for leap years).DLREOCGWPMODISYearlyland__GWP_P1YRectifiedGridCoverage-180.0 -90.0180.0 90.0-180.0 -90.0180.0 90.0GrassLands - First CutThe layer shows the date of first mowing event in.DLREOCland__GL_MOW_DE_P1Y_FCRectifiedGridCoverage5.763545147588102 46.79367161643677515.152746853982658 55.945732920849475.763545147588102 46.79367161643677515.152746853982658 55.94573292084947GrassLands - Mowing FrequencyThe layer shows the number of times grassland is mown per year.DLREOCland__GL_MOW_DE_P1Y_FREQRectifiedGridCoverage5.763545147588102 46.79367161643677515.152746853982658 55.9457329215.763545147588102 46.79367161643677515.152746853982658 55.945732921Input Data Consistency (IDC) ScoreInput Data Consistency for Landsat datalandsatqualityconsistencyland__IDC_ScoreRectifiedGridCoverage-180.0102556812165 -60.010245756564814180.0102556812165 78.0100585990529-180.010255681217 -60.0102457565648180.010255681217 78.0100585990529Land Cover DE, 2015This layer contains a land cover classification of Germany which was created using Sentinel-2 imagery and LUCAS in-situ reference data (https://ec.europa.eu/eurostat/web/lucas). It contains seven land cover types: (1) artificial land, (2) open soil, (3) high seasonal vegetation, (4) high perennial vegetation, (5) low seasonal vegetation, (6) low perennial vegetation and (7) water with a spatial resolution of 10m x 10m.DLREOCGermanyLand cover mapSentinel-2land__LCC_DE_2015RectifiedGridCoverage3985270.0 2670630.04731190.0 3572740.04.730999535505982 47.01480232040531616.432222328775413 55.25836025427186NOAA AVHRR Sea Surface Temperature - Monthly"AVHRR - Monthly Sea Surface Temperature (SST)" is an AVHRR (Advanced Very High Resolution Radiometer) subset from 1993 to 2014 integrated to facilitate valuable time series exploitation of historic data. This subset represents the Monthly Average of Sea Surface Temperature in degrees celsius.DLRAVHRREOCNOAASSTMONTHLYSea Surface TemperatureMonthlyland__NOAA_AVHRR_SST_P1MRectifiedGridCoverage-34.94160000447138 26.21724000823240550.93317409155591 73.1063000067832-22.5595999595764 26.2172400082324-5.44067902556616 48.1549000212745Noise - Road Traffic Lden (2002/49/EC) - Germany, 2017This dataset includes a road traffic noise indicator mapped according to the European Noise Directive (2002/49/EC).NOISEland__N2N_NOISE_DE_2017RectifiedGridCoverage4026300.0 2679000.04677500.0 3556400.05.389879873253458 47.1228368129314615.573542540854344 55.11194621255036Noise - Road Traffic Lden (AI Prediction) - Germany, 2017This dataset includes a road traffic noise estimation using ensemble learning and multimodal geodata.NOISEland__N2N_NOISE_AI_DE_2017RectifiedGridCoverage5.38987987325346 47.1228367662376815.573542540854344 55.028218595868355.38987987325346 47.1228367662376815.573542540854344 55.02821859586835SCMaP Exposed Soil CoverageExposed Soil Coverage v1.0 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorSoil ClassificationExposed Soil CoverageSoil Coverageland__SCMAP_SC_LSRectifiedGridCoverage5.866250142023188 47.2701400336254615.041805919628125 55.056529110945745.86625014202331 47.270140033625515.0418059196273 55.056529110945SCMaP Exposed Soil Coverage 1984-2014Exposed Soil Coverage v1.0 for 1984-2014 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorSoil ClassificationExposed Soil CoverageSoil Coverageland__SCMAP_SC_LS_1984-2014RectifiedGridCoverage5.866250142023187 47.2701400336262515.041805919627175 55.056529110945745.86625014202319 47.270140033626215.0418059196272 55.0565291109457SCMaP Exposed Soil FrequencyExposed Soil Frequency v1.0 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorSoil ClassificationExposed Soil CoverageSoil Frequencyland__SCMAP_SF_LSRectifiedGridCoverage5.866250142023188 47.2701400336254615.041805919628125 55.056529110945745.86625014202326 47.270140033626215.0418059196272 55.0565291109457SCMaP Exposed Soil Frequency 1984-2014Exposed Soil Frequency v1.0 for 1984-2014 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorSoil ClassificationExposed Soil CoverageSoil Frequencyland__SCMAP_SF_LS_1984-2014RectifiedGridCoverage5.866250142023187 47.2701400336262515.041805919627175 55.056529110945745.86625014202319 47.270140033626215.0418059196272 55.0565291109457SCMaP Reflectance CompositeReflectance Composite v1.0 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorNormalized Reflectance Soil CompositeReflectanceland__SCMAP_SR_LSRectifiedGridCoverage5.866250142023188 47.2701400336254615.041805919628125 55.056529110945745.86625014202338 47.270140033625515.0418059196274 55.056529110945SCMaP Reflectance Composite 1984-2014Reflectance Composite v1.0 for 1984-2014 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorNormalized Reflectance Soil CompositeReflectanceland__SCMAP_SR_LS_1984-2014RectifiedGridCoverage5.866250142023187 47.2701400336262515.041805919627175 55.056529110945745.86625014202319 47.270140033626215.0418059196272 55.0565291109457SCMaP Reflectance Soil CompositeNormalized Reflectance Soil Composite v1.0 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorNormalized Reflectance Soil CompositeReflectanceland__SCMAP_SN_LSRectifiedGridCoverage5.866250142023188 47.2701400336254615.041805919628125 55.056529110945745.86625014202338 47.270140033625515.0418059196274 55.056529110945SCMaP Reflectance Soil Composite 1984-2014 (normalized)Normalized Reflectance Soil Composite v1.0 for 1984-2014 processed by Soil Composite Mapping Processor (SCMaP)DLREOCLandLandsatSoil MapSCMaPSoil Composite Mapping ProcessorNormalized Reflectance Soil CompositeReflectanceland__SCMAP_SN_LS_1984-2014RectifiedGridCoverage5.866250142023187 47.2701400336262515.041805919627175 55.056529110945745.86625014202319 47.270140033626215.0418059196272 55.0565291109457SMAP_EURASIA_EAST_ASCEland__SMAP_EURASIA_EAST_ASCERectifiedGridCoverage106.8689 33.711037072310404112.2154042074719 44.8625106.8689 33.711037072310404112.2154042074719 44.8625Sar4Tectonics - East Eurasia (Ascending)Sar4Tectonics data for eurasia (Ascending)DLREOCSar4TectonicsSentinel-1AscendingEurasialand__SAR4TEC_EURASIA_EAST_ASCERectifiedGridCoverage-8.643509 13.51874144.533 51.99475-8.643509 13.51874144.533 51.99475Sar4Tectonics - East Eurasia (Descending)Sar4Tectonics data for eurasia (Descending)DLREOCSar4TectonicsSentinel-1DescendingEurasialand__SAR4TEC_EURASIA_EAST_DESCRectifiedGridCoverage-9.492298 13.45345144.2898 57.98615-9.492298 13.45345144.2898 57.98615SoilSuite – Bare Surface Reflectance Composite - Standard deviation, AfricaThe Bare Surface Reflectance Composite (Std Dev) contains the standard deviation of all Sentinel-2 bare surface reflectance values occuring between 2018 - 2021. It is scaled between 0 and 10.000 for each band and excludes urban areas, water bodies, clouds, haze, cloud shadows and snow. This product quantifies e.g. the spectral dynamic of soils, which might differ according to their physical and chemical properties and thus, can be taken as an additional characteristic of the soil.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – Std Devland__SOILSUITE_SRC-STD_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Bare Surface Reflectance Composite - Standard deviation, EuropeThe Bare Surface Reflectance Composite (Std Dev) contains the standard deviation of all Sentinel-2 bare surface reflectance values occuring between 2018 - 2022. It is scaled between 0 and 10.000 for each band and excludes urban areas, water bodies, clouds, haze, cloud shadows and snow. This product quantifies e.g. the spectral dynamic of soils, which might differ according to their physical and chemical properties and thus, can be taken as an additional characteristic of the soil.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – Std Devland__SOILSUITE_SRC-STD_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.0-34.84229818529844 28.08392845770274.35811444109153 73.01747580668338SoilSuite – Bare Surface Reflectance Composite – 95% Confidence, AfricaThe Bare Surface Reflectance Composite (Std Dev) contains the standard deviation of all Sentinel-2 bare surface reflectance values occuring between 2018 - 2021. It is scaled between 0 and 10.000 for each band and excludes urban areas, water bodies, clouds, haze, cloud shadows and snow. This product quantifies e.g. the spectral dynamic of soils, which might differ according to their physical and chemical properties and thus, can be taken as an additional characteristic of the soil.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – 95% Confidenceland__SOILSUITE_SRC-CI95_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Bare Surface Reflectance Composite – 95% Confidence, EuropeThe Bare Surface Reflectance Composite (Std Dev) contains the standard deviation of all Sentinel-2 bare surface reflectance values occuring between 2018 - 2022. It is scaled between 0 and 10.000 for each band and excludes urban areas, water bodies, clouds, haze, cloud shadows and snow. This product quantifies e.g. the spectral dynamic of soils, which might differ according to their physical and chemical properties and thus, can be taken as an additional characteristic of the soil.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – 95% Confidenceland__SOILSUITE_SRC-CI95_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.0-34.84229818529844 28.08392845770274.35811444109153 73.01747580668338SoilSuite – Bare Surface Reflectance Composite – Mean, AfricaThe Bare Surface Reflectance Composite (Mean) contains reflectance values scaled between 0 and 10.000. It represents the mean reflectance of all Sentinel-2 bare surface occurrences between 2018 - 2021 excluding urban areas, water bodies, clouds, haze, cloud shadows and snow. This product is one of the main inputs for spectral as well as digital soil mapping approaches.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – Meanland__SOILSUITE_SRC_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Bare Surface Reflectance Composite – Mean, EuropeThe Bare Surface Reflectance Composite (Mean) contains reflectance values scaled between 0 and 10.000. It represents the mean reflectance of all Sentinel-2 bare surface occurrences between 2018 - 2022 excluding urban areas, water bodies, clouds, haze, cloud shadows and snow. This product is one of the main inputs for spectral as well as digital soil mapping approaches.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Reflectance Composite – Meanland__SOILSUITE_SRC_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.0-34.84229818529844 28.08392845770274.35811444109153 73.01747580668338SoilSuite – Bare surface count, AfricaThe Bare Surface Count is defined as the number of occurrences detected as bare surface between 2018 - 2021. It is not normalized to the total number of valid Sentinel-2 observations. The value reveals the dependence of the pixel position across the ground track that can lead to nearly double observations in overlapping orbits.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Countland__SOILSUITE_SFREQ-BSC_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Bare surface count, EuropeThe Bare Surface Count is defined as the number of occurrences detected as bare surface between 2018 - 2022. It is not normalized to the total number of valid Sentinel-2 observations. The value reveals the dependence of the pixel position across the ground track that can lead to nearly double observations in overlapping orbits.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Countland__SOILSUITE_SFREQ-BSC_EUR_P5YRectifiedGridCoverage-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056SoilSuite – Bare surface frequency, AfricaThe Bare Surface Frequency is scaled between 0 and 1 and quantifies the ratio of bare surface occurrences over the total number of valid Sentinel-2 observations in the observed time period between 2018 - 2021.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Frequencyland__SOILSUITE_SFREQ-BSF_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Bare surface frequency, EuropeThe Bare Surface Frequency is scaled between 0 and 1 and quantifies the ratio of bare surface occurrences over the total number of valid Sentinel-2 observations in the observed time period between 2018 - 2022.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyBare Surface Frequencyland__SOILSUITE_SFREQ-BSF_EUR_P5YRectifiedGridCoverage-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056SoilSuite – Mask, AfricaThe MASK product aggregates simple landcover classes that occur during the time period between 2018 - 2021 (Sentinel-2). The three-class Mask contains: (1) Bare surface occurrences, (2) Permanent vegetation, (3) Other surfaces (e.g. water bodies, urban areas, roads).DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyMaskland__SOILSUITE_MASK_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Mask, EuropeThe MASK product aggregates simple landcover classes that occur during the time period between 2018 - 2022 (Sentinel-2). The three-class Mask contains: (1) Bare surface occurrences, (2) Permanent vegetation, (3) Other surfaces (e.g. water bodies, urban areas, roads).DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyMaskland__SOILSUITE_MASK_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.045.11997737153057 39.4684665391403148.3031470022221 41.893378728158574SoilSuite – Reflectance Composite – Standard deviation, AfricaThe Reflectance Composite (Std Dev) contains the standard deviation per band for all valid Sentinel-2 observations between 2018 - 2021, scaled from 0 to 10.000. The valid observations include vegetation, bare surfaces and other surfaces and it excludes clouds, haze, cloud shadows and snow. The product quantifies the spectral dynamic of all landcover types occurring in the images. As an example, it shows a higher standard deviation in agricultural areas compared to natural grasslands.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyReflectance Composite – Std Devland__SOILSUITE_MREF-STD_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Reflectance Composite – Standard deviation, EuropeThe Reflectance Composite (Std Dev) contains the standard deviation per band for all valid Sentinel-2 observations between 2018 - 2022, scaled from 0 to 10.000. The valid observations include vegetation, bare surfaces and other surfaces and it excludes clouds, haze, cloud shadows and snow. The product quantifies the spectral dynamic of all landcover types occurring in the images. As an example, it shows a higher standard deviation in agricultural areas compared to natural grasslands.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyReflectance Composite – Std Devland__SOILSUITE_MREF-STD_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.0-34.84229818529844 28.08392845770274.35811444109153 73.01747580668338SoilSuite – Reflectance Composite, Mean - AfricaThe Reflectance Composte (Mean) contains reflectance values per band, scaled between 0 and 10.000. It represents the mean reflectance of all valid Sentinel-2 observations between 2018 - 2021. Valid observations include vegetation, bare and other surfaces and exclude clouds, haze, cloud shadows and snow.DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyReflectance Composite – Meanland__SOILSUITE_MREF_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Reflectance Composite, Mean - EuropeThe Reflectance Composte (Mean) contains reflectance values per band, scaled between 0 and 10.000. It represents the mean reflectance of all valid Sentinel-2 observations between 2018 - 2022. Valid observations include vegetation, bare and other surfaces and exclude clouds, haze, cloud shadows and snow.DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyReflectance Composite – Meanland__SOILSUITE_MREF_EUR_P5YRectifiedGridCoverage2600000.0 1200000.07400000.0 5600000.0-34.84229818529844 28.08392845770274.35811444109153 73.01747580668338SoilSuite – Valid pixel count, AfricaThis Valid Pixel Count contains the total number of valid Sentinel-2 observations in the time period between 2018 - 2021 excluding cloud, haze, cloud shadow, snow, etc..DLREOCLandSCMaPSoilSentinel-2AfricaTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyValid Pixel Countland__SOILSUITE_SFREQ-VPC_AFR_P4YRectifiedGridCoverage28.746089091824686 -7.21555136681218850.3056559106932 16.061232742453428.746089091824686 -7.21555136681218850.3056559106932 16.0612327424534SoilSuite – Valid pixel count, EuropeThis Valid Pixel Count contains the total number of valid Sentinel-2 observations in the time period between 2018 - 2022 excluding cloud, haze, cloud shadow, snow, etc..DLREOCLandSCMaPSoilSentinel-2EuropeTemporal CompositesSoil CoverageSoil Composite Mapping ProcessorReflectance CompositeSurface Reflectance CompositeBare Soil CoverageBare Soil FrequencyValid Pixel Countland__SOILSUITE_SFREQ-VPC_EUR_P5YRectifiedGridCoverage-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056-29.177180913776336 32.3768719550177149.42179460501849 73.10277281336056TIMELINE AVHRR L3 Monthly LSTD - MaxTIMELINE Monthly Composite of Max Land Surface Temperature of the respective period (during day in K) (AVHRR)DLREOCTIMELINEAVHRRL3Timeseries for global change analysisLSTDLand Surface Temperature (Day)Monthly Maxland__TIMELINE_AVHRR_L3_P1M_LSTD_MAXRectifiedGridCoverage-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004TIMELINE AVHRR L3 Monthly LSTD - MeanTIMELINE Monthly Composite of Mean Land Surface Temperature of the respective period (during day in K) (AVHRR)DLREOCTIMELINEAVHRRL3Timeseries for global change analysisLSTDLand Surface Temperature (Day)Monthly Meanland__TIMELINE_AVHRR_L3_P1M_LSTD_MEANRectifiedGridCoverage-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004TIMELINE AVHRR L3 Monthly LSTD - MinTIMELINE Monthly Composite of Min Land Surface Temperature of the respective period (during day in K) (AVHRR)DLREOCTIMELINEAVHRRL3Timeseries for global change analysisLSTDLand Surface Temperature (Day)Monthly Minland__TIMELINE_AVHRR_L3_P1M_LSTD_MINRectifiedGridCoverage-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004TIMELINE AVHRR L3 Monthly LSTD - Standard DeviationTIMELINE Monthly Composite of Standard Deviation of Land Surface Temperature in the respective period (during day in K) (AVHRR)DLREOCTIMELINEAVHRRL3Timeseries for global change analysisLSTDLand Surface Temperature (Day)Monthly Standard Deviation of Land Surface Temperatureland__TIMELINE_AVHRR_L3_P1M_LSTD_STDRectifiedGridCoverage-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004TIMELINE AVHRR L3 Monthly LSTD - Valid Pixel CountTIMELINE Monthly Composite of Count of valid pixels in the respective periodDLREOCTIMELINEAVHRRL3Timeseries for global change analysisLSTDLand Surface Temperature (Day)Count of valid pixelsland__TIMELINE_AVHRR_L3_P1M_LSTD_COUNTRectifiedGridCoverage-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004-56.51695840749218 24.28644126178609572.90771505554093 58.95876067653004TimeScan - Landsat - Global, 2015TimeScan Global 2015 (Landsat)DLREOCLandsatGlobalTimeScanland__TS_LANDSAT_2015RectifiedGridCoverage-180.0 -89.99999999999883179.99999999999767 90.0-180.0 -89.9999999999988179.999999999998 90.0Traffic Area Map (TAM) - Brunswick, GermanyThe dataset depicts the inventory of traffic areas in the city of Brunswick, Germany.DLREOCArial ImageryImage SegmentationTraffic Area MapParking Space DetectionMultitemporal Fusionland__URMO_TAM_3K_BRUNSWICKRectifiedGridCoverage4354069.632744781 3236847.0971854994359795.014559064 3245926.20557075410.484114223312364 52.2399210824410110.568968686619046 52.32188460260454Tree Canopy Cover Loss Germany 2018-2021The products depicts per-pixel tree canopy cover loss at 10 m spatial resolution from January 2018 till April 2021.ForestCanopy Cover LossDroughtLandsat-8Disturbance IndexTime SeriesSentinel-2Deutschlandland__TREE_CANOPY_COVER_LOSS_P1MRectifiedGridCoverage3999960.3867 2672927.56794710800.386700001 3572947.56794.960589178227888 47.0484474285764716.114268243041412 55.26042551315777Tree Species - Sentinel-1/2 - Germany, 2022The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model.DLREOCforesttree speciesmachine learningtime-seriesSentinel-1Sentinel-2Germanyland__TREE_SPECIES_DE_2022RectifiedGridCoverage4024850.0 2684040.04677550.0 3566260.05.357117533816744 47.1680748891055215.586501370011273 55.20059302945526Tree Species - Sentinel-2 - Germany, 2016The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2016 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data.DLREOCforesttree speciesmachine learningtime-seriesSentinel-2Germany eoc-mid-0079-vm:land__TREE_SPECIES_DE_2016RectifiedGridCoverage4024850.0 2686260.04677550.0 3566260.05.357117533816744 47.1880133317940315.586501370011273 55.20059302945526World Settlement Footprint (WSF) 2015World Settlement Footprint 2015landurbanizationglobalmappingsettlementpatternslandsatsentinel2copernicusland__WSF_2015RectifiedGridCoverage-180.01007601815968 -60.01006609352275180.01007601819816 78.0100585990529-180.01007601815968 -60.01006609352275180.01007601819816 78.0100585990529World Settlement Footprint (WSF) 2019World Settlement Footprint 2019landurbanizationglobal settlement extentmappingsentinel-1sentinel-2copernicus2019dlrland__WSF_2019RectifiedGridCoverage-180.01007601815968 -60.01006609352275180.01007601819816 78.0100585990529-180.01007601815968 -60.01006609352275180.01007601819816 78.0100585990529World Settlement Footprint (WSF) 3D - Building Area - Global, 90mThe WSF 3D Building Area product depicts detailed quantification of total area in m² at 90 m resolution at a global scale.building heightbuilding volumebuilding areabuilding densityurbanization3DglobalSentinel-1Sentinel-2TanDEM-XDLREOCland__WSF3D_V02_BUILDINGAREARectifiedGridCoverage-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557World Settlement Footprint (WSF) 3D - Building Fraction - Global, 90mThe WSF 3D Building Fraction product depicts detailed quantification of fraction in % at 90 m resolution at a global scale.building heightbuilding volumebuilding areabuilding densityurbanization3DglobalSentinel-1Sentinel-2TanDEM-XDLREOCland__WSF3D_V02_BUILDINGFRACTIONRectifiedGridCoverage-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557World Settlement Footprint (WSF) 3D - Building Height - Global, 90mThe WSF 3D Building Height product depicts detailed quantification of average height in m at 90 m resolution at a global scale.building heightbuilding volumebuilding areabuilding densityurbanization3DglobalSentinel-1Sentinel-2TanDEM-XDLREOCland__WSF3D_V02_BUILDINGHEIGHTRectifiedGridCoverage-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557World Settlement Footprint (WSF) 3D - Building Volume - Global, 90mThe WSF 3D Building Fraction product depicts detailed quantification of total volume in m³ at 90 m resolution at a global scale.building heightbuilding volumebuilding areabuilding densityurbanization3DglobalSentinel-1Sentinel-2TanDEM-XDLREOCland__WSF3D_V02_BUILDINGVOLUMERectifiedGridCoverage-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557-180.00149996666667 -56.000277822222216180.00227781111116 84.00049995555557World Settlement Footprint (WSF) EvolutionWorld Settlement Footprint Evolutionlandurbanizationglobalmappingsettlementevolutionsettlement extent growthlandsatbig dataland__WSF_EvolutionRectifiedGridCoverage-180.0102556812165 -60.01024575657463180.01025568124214 78.0100585990529-180.0102556812165 -60.01024575657463180.01025568124214 78.0100585990529