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, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. 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 (in the meantime slightly updated) methodology can be found in Asam et al. 2022.

The timing of the first mowing event per year strongly impacts grassland functions and ecology, such as the provision of habitats and species composition. As grasslands in Germany are managed on small-scale units and grass grows back quickly, satellite information with high spatial and temporal resolution is necessary to capture grassland mowing dynamics. Based on Sentinel-2 data time series, mowing events are detected throughout Germany and the date of the first mowing event per year is extracted. The grassland mowing detection approach operates per pixel, including preprocessing of the Enhanced Vegetation Index (EVI) time series and a calibrated rule-based grassland mowing detection which is specified in more detail in Reinermann et al. 2022, 2023.

Grassland mowing dynamics (i.e. the timing and frequency of mowing events) have a strong impact on grassland functions and yields. As grasslands in Germany are managed on small-scale units and grass grows back quickly, satellite information with high spatial and temporal resolution is necessary to capture grassland mowing dynamics. Based on Sentinel-2 data time series, mowing events are detected throughout Germany and annual maps of the grassland mowing frequency generated. The grassland mowing detection approach operates per pixel, including preprocessing of the Enhanced Vegetation Index (EVI) time series and a calibrated rule-based grassland mowing detection which is specified in more detail in Reinermann et al. 2022, 2023.

Hedgerows play an important role in maintaining biodiversity, carbon sequestration, soil stability and the ecological integrity of agricultural landscapes. In this dataset, hedgerows are mapped for the whole of Bavaria. Orthophotos with a spatial resolution of 20 cm, taken in the period from 2019 to 2021, were used in a deep learning approach. Hedgerow polygons of the Bavarian in-situ biotope mapping from 5 districts (Miltenberg, Hassberge, Dillingen a.d. Donau, Freyung-Grafenau, Weilheim-Schongau) as well as other manually digitized polygons were used for training and testing as input into a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 backbone and was optimized with the Dice loss as a cost function. The generated hedgerow probability tiles were post-processed by merging and averaging the overlapping tile boundaries, shape simplification and filtering. For more details, see Huber Garcia et al. (2025). The dataset has been created within the project FPCUP (https://www.copernicus-user-uptake.eu/) in close cooperation with Bayerisches Landesamt für Umwelt (LfU).