Nationwide annual agricultural land-use maps of Germany from 1990 to 2023 derived from satellite imagery

Abstract

We present German-wide annual agricultural land-use maps from 1990 to 2023, created from Landsat and Sentinel-2 images using a deep learning approach. Based on farmers’ declarations from 2006 to 2022, we extracted annual training samples for 13 crop classes and one grassland class. These samples were used to train a multi-year one-dimensional convolutional neural network, which was subsequently applied to generate the annual land-use maps. Between 2010 and 2022, overall map accuracies ranged between 85% and 93%. When averaged over time, dominant classes like grassland, rapeseed, winter cereals, sugar beet, and maize were detected with high accuracy (≥90%). Conversely, minor classes such as fallow land and plantation were predicted with low accuracy (≤52%). Comparison of map areas with agricultural statistics over time revealed high correlations for most classes, particularly maize (r = 0.978). The presented maps provide an essential basis for analyzing long-term trends in agricultural land-use. They can be used to fill temporal gaps in national agricultural statistics and to disaggregate those statistics to higher spatial units.