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Regional crop production prediction is a significant component of national food security assessment. Crop growth models are successfully applicable for yield estimation in simple point scale, however, they are hampered by the deriving of regional crop key input parameters. The World Food Studies (WOFOST) model had been used to express the characteristic of time series LAI in crop growth season in the study area. To solve the system errors of coarse resolution data extracted LAI due to the mixed pixels effect, the corrected LAI was implemented by combining the field measured LAI data and the HJ-LAI temporal trend information. Time-series LAI was assimilated through combined corrected HJ-LAI and WOFOST simulated LAI during the whole growth stage with the ensemble Kalman filter (EnKF) algorithm. The assimilated optimal LAI was used to drive the WOFOST model per-pixel to estimate the regional yield. Scheduling the assimilation of different step length observed quantities, comparing the accuracy and the efficiency of the assimilation at different time scale, we selected the proper time scale of the assimilation. The results indicated that selecting the time scale of the step length between 10 days and 16 days was more appropriate. Compared with the statistical yield, the coefficient of determination was 0.66 and RMSE was 1.61 ton/hm. The results showed that assimilation of the remotely sensed data into crop growth model with EnKF can provide a reliable approach for estimate regional crop yield and had great potential in agricultural applications.
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