Combining Machine Learning and Spatiotemporal Filtering to Map Crop Types of Germany for Seven Years

Knowledge of the distribution of crop types and their sequence over the years is essential not only for scientific applications but also for supporting informed planning for food security, climate adaptation and mitigation, agroecology, and landscape diversity. For Germany, crop type information is collected as part of the subsidy management, but data access is restricted for certain years and Federal States. Remote sensing time series of sensors, such as Sentinel-1 and Sentinel-2, allows national mapping of crop types at field scale. This has been demonstrated in previous literature, which describe crop type mapping of Germany for one to three years. Here, we demonstrate a two-phase crop type classification methodology based on Sentinel-1 and Sentinel-2 data. The approach combines machine learning (ML)-based classification with spatial and temporal analyses. The methodology was used to create a novel and most recent seven-year time series (2018–2024) for Germany at 10-m spatial resolution separating 18 classes. The two-phase approach led to annual overall accuracies (OAs) of 0.81–0.83 with average class-specific ${F}1$ -scores ranging from around 0.56–0.99.