Winter Wheat Yield Estimation at the Field Scale By Assimilating Sentinel-2 LAI into Crop Growth Model

Crop yield estimation at the field scale is essential for farmers, crop insurance companies to make informed decisions. Methodologies based on assimilating remote sensing LAI into crop growth models have shown advantages in crop yield estimates. Compared with MODIS and Landsat, Sentinel-2 satellites provide higher spatial and temporal resolution data, which brings revolutionary opportunities for crop monitoring. This study is to evaluate the performance of assimilating Sentinel-2 LAI into the WOFOST model for winter wheat yield estimation using the Ensemble Kalman Filter algorithm. The results showed that assimilating Sentinel-2 LAI improved the yield estimation (R
²
= 0.45; RMSE = 512 kg/ha) compared to the situation without data assimilation (R
²
= 0.27; RMSE = 818 kg/ha), which demonstrated the potential usage of the Sentinel-2 LAI for yield estimation at the field scale.