4aeac9d6-935c-4fc4-a657-3c3296589b5f English Dataseries German Aerospace Center (DLR) geoservice@dlr.de 2026-04-30T09:31:28 ISO 19115-1:2014/19139 2003/Cor.1:2006 2 69845 10 100954 10 false EPSG:32632 CropTypes - Crop Type Maps for Germany - Yearly, 10m 2024-11-11T00:00:00 2.0 https://geoservice.dlr.de/catalogue/srv/metadata/4aeac9d6-935c-4fc4-a657-3c3296589b5f This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 21 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2017. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying methodology can be found in Gessner et al. 2025 and Asam et al. 2022. These data sets enable a comparison of arable land use between years and the derivation of crop rotations and biodiversity. Such information is important for many environmental and agricultural policy issues. The data set fits to DLR's mission to address societal relevant information and to provide related products to decision makers and a broader public. German Aerospace Center (DLR) ursula.gessner@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/4aeac9d6-935c-4fc4-a657-3c3296589b5f/attachments/croptypes_ger_p1y_ql.jpg large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/4aeac9d6-935c-4fc4-a657-3c3296589b5f/attachments/croptypes_ger_p1y_ql_s.jpg thumbnail DLR EOC Germany Crops Agriculture Random Forest Classification Multispectral Radar Spectral Statistics Temporal Statistics Sentinel-1 Sentinel-2 IACS opendata National Spatial scope 2024-05-23 geonetwork.thesaurus.external.place.SpatialScope.en Agricultural and aquaculture facilities GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.local.theme.inspire-theme Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use. Es gelten keine Zugriffsbeschränkungen Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / Terms of use: License, https://creativecommons.org/licenses/by/4.0 {"id": "cc-by-4.0", "name": "Creative Commons Namensnennung - 4.0 International (CC BY 4.0)", "url": "http://dcat-ap.de/def/licenses/cc-by/4.0", "quelle": "Copyright DLR (year of production)"} 20000 English farming 5.87 15.04 47.27 55.06 2017-03-01T00:00:00Z 2024-10-31T23:59:59Z GeoTIFF https://geoservice.dlr.de/eoc/land/wms? OGC:WMS CROPTYPES_DE_P1Y_V02 EOC Land Map Service - Crop Type Maps for Germany https://download.geoservice.dlr.de/CROPTYPES/files/ WWW:LINK-1.0-http--link HTTP Download Service https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/CROPTYPES_DE_P1Y_V02 WWW:LINK-1.0-http--link EOC EO Products Service EOC EO Products Service - STAC Collection: CROPTYPES_DE_P1Y_V02 https://geoservice.dlr.de/web/datasets/croptypes_de WWW:LINK-1.0-http--link EOC Geoservice Dataset EOC Geoservice Dataset https://geoservice.dlr.de/web/maps/eoc:croptypes WWW:LINK-1.0-http--link EOC Geoservice Map Context EOC Geoservice Map Context (eoc:croptypes) Conformity_001 INSPIRE Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification. true Input data are all available Sentinel-1 and Sentinel-2 data of each respective year for the whole of Germany. LPIS data from several German Federal States are used. Processing: For details of processing see Gessner et al. 2025 and Asam et al. 2022. Temporal features of the employed remote sensing data and LPIS data were used in a Random Forest model to predict crop types. The classification result was post-processed using temporal and spatial filtering techniques and rule-based corrections. Quality Assurance: Data quality is assured through a state of the art accuracy assessment. Annual overall accuracies range between 0.75-0.80.