Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R-2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.