Improving Maize Yield Estimation Accuracy by Integrating Satellite Remote Sensing Data and the WOFOST Model Through Data Assimilation

Under intensified climate change, improving yield estimation accuracy is essential for food security and precision agricultural management. This research aimed to enhance summer maize yield estimation accuracy. GF-1 imagery was used to derive vegetation indices for leaf area index (LAI) modeling; vegetation index–LAI models used partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), convolutional neural network (CNN), and categorical feature-enhanced gradient boosting (CatBoost). Retrieved summer maize LAI and measured soil moisture (SM) were assimilated into a calibrated World Food Studies (WOFOST) model using ensemble Kalman filtering (EnKF) and four-dimensional variational assimilation (4D-Var). The influence of ensemble size and assimilation period on the accuracy of yield simulations was analyzed. Results showed that all machine learning models effectively retrieved LAI, with CNN achieving the highest accuracy under the evaluation conditions of this study. Compared with the non-assimilation scenario, data assimilation significantly improved WOFOST-based maize yield estimation; EnKF performed best for yield estimation, whereas 4D-Var yielded slightly lower LAI simulation errors than EnKF under the experimental conditions of this study. Among the ensemble sizes evaluated, an ensemble size of 100 produced the lowest yield prediction error, while assimilation during the tasseling-to-grain-filling period resulted in lower yield prediction errors than continuous whole-growth-period assimilation under the conditions examined. Overall, data assimilation effectively improved WOFOST-based maize yield estimation and supported regional crop monitoring and precision agricultural management.