Combined use of Sentinel-2 and Sentinel-1 data for wheat crop yield forecasting with machine learning algorithms
Vegetation indices derived from remotely sensed optical data are commonly used for crop monitoring in precision agriculture, but their information capability can be hindered by the presence of clouds. Consequently, areas with frequent cloud cover like the southwest of Sweden may have limited access to usable optical data. Nonetheless, the addition of synthetic aperture radar (SAR) data can provide
