Integrating Landsat, Sentinel-2 and Sentinel-1 time series for mapping intermediate crops

Intermediate crops are grown between main crops to protect soils and nutrients when fields would otherwise be bare. Despite being an essential constituent of cropping systems, spatial information on intermediate crops is scarce. Here, we propose a classification algorithm that combines field data, satellite imagery from multiple optical sensors and synthetic-aperture radar (SAR) data to map intermediate crops across Brandenburg, Germany. We trained random forest models using different sets of input features, including spectral-temporal metrics from optical data, metrics derived from SAR data and information on the scheduled main crop. The best classification was based on a combination of all input features and achieved an overall accuracy of 92.9%. Intermediate crops were overestimated, which can be partly attributed to misclassification of volunteers and weeds as intermediate crops. The overestimation was mitigated by aggregating results to the field level. Our results highlight the need for good optical data coverage during autumn and winter to accurately map intermediate crops while demonstrating the ability of SAR data to enhance classification accuracy. Overall, our study shows the potential of remote sensing methods to capture the characteristics of intermediate crops and derive spatially explicit data for monitoring sustainable agricultural practices.