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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)

This data set contains information on the agricultural land use in Germany for the year 2020. The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>. The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>. Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.   Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022. <table> <tbody> <tr> <td> New class code (V2) </td> <td> Class name (V2) </td> <td> Class code (V1) </td> <td> Class name (V1) </td> </tr> <tr> <td> 1101 </td> <td> Winter wheat </td> <td> 31 </td> <td> Winter wheat </td> </tr> <tr> <td>   </td> <td>   </td> <td> 34 </td> <td> Other winter cereals </td> </tr> <tr> <td> 1102 </td> <td> Winter barley </td> <td> 33 </td> <td> Winter barley </td> </tr> <tr> <td> 1103 </td> <td> Winter rye </td> <td> 32 </td> <td> Winter rye </td> </tr> <tr> <td> 1201 </td> <td> Spring barley </td> <td> 41 </td> <td> Spring barley </td> </tr> <tr> <td> </td> <td> </td> <td> 43 </td> <td> Other spring cereals </td> </tr> <tr> <td> 1202 </td> <td> Oat </td> <td> 42 </td> <td> Spring oat </td> </tr> <tr> <td> 1300 </td> <td> Maize </td> <td> 91 </td> <td> Maize (silage) </td> </tr> <tr> <td> </td> <td> </td> <td> 92 </td> <td> Maize (grain) </td> </tr> <tr> <td> 1401 </td> <td> Potatoe </td> <td> 100 </td> <td> Potatoe </td> </tr> <tr> <td> 1402 </td> <td> Sugar beet </td> <td> 80 </td> <td> Sugar beet </td> </tr> <tr> <td> 1501 </td> <td> Rapeseed </td> <td> 50 </td> <td> Winter rapeseed </td> </tr> <tr> <td> 1502 </td> <td> Sunflower </td> <td> 70 </td> <td> Sunflower </td> </tr> <tr> <td> 1611 </td> <td> Peas </td> <td> 60 </td> <td> Legume </td> </tr> <tr> <td> 1612 </td> <td> Broad beans </td> <td> </td> <td> </td> </tr> <tr> <td> 1613 </td> <td> Lupine </td> <td> </td> <td> </td> </tr> <tr> <td> 1614 </td> <td> Soy </td> <td> </td> <td> </td> </tr> <tr> <td> 1603 </td> <td> Vegetables </td> <td> 120 </td> <td> Strawberry </td> </tr> <tr> <td> </td> <td> </td> <td> 130 </td> <td> Asparagus </td> </tr> <tr> <td> </td> <td> </td> <td> 140 </td> <td> Onion </td> </tr> <tr> <td> </td> <td> </td> <td> 181 </td> <td> Carrot </td> </tr> <tr> <td> </td> <td> </td> <td> 182 </td> <td> Other leafy vegetables </td> </tr> <tr> <td> 1602 </td> <td> Cultivated grassland </td> <td> 10 </td> <td> Grassland </td> </tr> <tr> <td> 200 </td> <td> Permanent grassland </td> <td> </td> <td> </td> </tr> <tr> <td> 3003 </td> <td> Fallow land </td> <td> </td> <td> </td> </tr> <tr> <td> 3001 </td> <td> Small woody features </td> <td> 555 </td> <td> Small woody features </td> </tr> <tr> <td> 3002 </td> <td> Other areas </td> <td> 999 </td> <td> Other agricultural areas </td> </tr> <tr> <td> 4001 </td> <td> Grapevine </td> <td> 110 </td> <td> Grapevine </td> </tr> <tr> <td> 4002 </td> <td> Hops </td> <td> 150 </td> <td> Hops </td> </tr> <tr> <td> 4003 </td> <td> Orchard </td> <td> 160 </td> <td> Orchards </td> </tr> </tbody> </table>   All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube.  The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map).    The maps are provided as GeoTiff files together with QGIS legend files for visualization.    References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831 BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).  BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.  https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.   <a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a>

ShareScore

12/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 0
Reuse readiness: 0
Engagement: 0

Topics

Remote sensing Agriculture Crop type Land use Land cover Germany Crops Biodiversity