DOI: 10.5281/zenodo.17197871
title: Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025)

description: This preliminary version is based on all available satellite data until August 2025 (*_2025_08). The map will be updated when more data are available.

Note: This dataset holds the same information as 10.5281/zenodo.17182293 but with new pyramids for improved visualization.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2025. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas.

Data and methods used to create map versions v301/2 differ from those used in previous versions. The v301/2 maps were derived from a time series of Sentinel-2 and Landsat 8/9 images. Map production is based on the methods described in Pham et al. (2024).

All optical satellite data were managed, 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), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). 

Version v301:Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

Version v302:Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refinded. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.

Validation:The final maps were validated using all pixels of the publicly available IACS parcels from the federal states of Brandenburg, Lower Saxony, and North Rhine-Westphalia that were not used for model training. Classes that are underrepresented in these federal states could therefore not be adequately evaluated (e.g., hops and grapevines). We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. 

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

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References:Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867.

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 (2020). 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.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

 ___________________________________________________________________________National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) © 2025 by Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan;  licensed under CC BY 4.0. 

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).