Bare Soil Composite for Germany (10 m) based on Sentinel-2 data from 2015 to 2024

This dataset contains the soil reflectance composite for Germany, which is described in Broeg et. al (2026).

The open-source software FORCE (Frantz, 2019) was used to download and preprocess all available Sentinel-2A/B scenes from February to November (2015 to 2024). A data fusion algorithm was applied to increase the spatial resolution of the 20-meter Sentinel-2 bands (B5, B6, B7, B8a, B11, B12) to a common resolution of 10 meters (Frantz et al., 2016). Clouds, cloud shadows, and hazy transition zones were detected and removed using a modified version of the Fmask algorithm (Frantz, 2019).

Bare soil observations were filtered from the preprocessed time series using the Normalized Burn Ratio 2 (NBR2 < 0.16) and a modified vegetation index (PV+IR2 < 0.24), proposed by Heiden et. al (2022). The soil reflectance was derived for the ten Sentinel-2 bands (B02, B03, B04, B05, B06, B07, B08, B08a, B11, B12) by calculating band-wise mean values across the filtered observations.

The map extent covers all areas in Germany that are defined as agricultural land, according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2025). 

Raster maps are provided as Cloud Optimized GeoTIFF (COG).

References

BKG, Bundesamt für Kartographie und Geodäsie (2025). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 15. May 2025).

Broeg, T., Don, A., Scholten, T., Erasmi, S., 2026. Reducing bias in cropland soil organic carbon and clay predictions using Sentinel-2 composites and data balancing. Remote Sensing of Environment 333, 115109. https://doi.org/10.1016/j.rse.2025.115109

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11(9), 1124. https://doi.org/10.3390/rs11091124

Frantz, D., Stellmes, M., Roder, A., Udelhoven, T., Mader, S., & Hill, J. (2016). Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs. IEEE Transactions on Geoscience and Remote Sensing, 54(7), 4153–4164. https://doi.org/10.1109/TGRS.2016.2537929

Heiden, U., d’Angelo, P., Schwind, P., Karlshöfer, P., Müller, R., Zepp, S., Wiesmeier, M., & Reinartz, P. (2022). Soil Reflectance Composites—Improved Thresholding and Performance Evaluation. Remote Sensing, 14(18), 4526. https://doi.org/10.3390/rs14184526