Assessment of AquaCrop for winter wheat using satellite derived fCover data

The integration of crop growth models with remote sensing has presented great potential in (regional) crop yield forecasting; although so far few field-level applications exist. Based on crowd/farm-sourced observations (phenological stages and yield measurements) and a basic assimilation procedure using satellite (DMC) and digital hemispherical pictures (DHP) derived green fractional cover data (fCover), the AquaCrop plug-in model was assessed for winter wheat fields in Belgium. A semi-automated R-environment was developed to simultaneously run, assess and evaluate the ensemble of field-level simulations. The root mean square error (RMSE) was 0.8 ton/ha. It was concluded that the presented approach might be promising for large scale field-level yield forecasting.