An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring growth and estimating yield. However, the choice of assimilated variables mismatch in scale between remotely sensed observations model-simulated state have various effects on performance yield estimation. This study aims to examine accuracy estimation through joint leaf area index (LAI) soil moisture (SM) effect simulations. To address these issues, we proposed improved data-model (CDMA) framework, which integrates LAI SM, retrieved from data, into World Food Studies (WOFOST) using ensemble Kalman filter (EnKF) winter wheat results showed that at a 10 m grid size outperformed 500 size, same strategy. Additionally, was higher when bivariate method (R2 = 0.46, RMSE 756 kg/ha) compared univariate method. In conclusion, our highlights advantages assimilating SM emphasizes importance finer spatial resolution CDMA framework. would help develop high-accuracy system optical SAR parameters.