Regional rice yield estimation based on assimilation of remote sensing data and crop growth model with Ensemble Kalman method

摘要

Regional crop production prediction is a significant component of national food security assessment. Remote sensing has the advantage of acquiring soil surface and crop canopy radiation information, however it is hard to reveal the inherence mechanism of crop growth and yield formation. Crop growth models based on the crop photosynthesis, transpiration, respiration, nutrition are successfully applicable for yield estimation in simple point scale, however, they are hampered by the deriving of regional crop key input parameters. Data assimilation method which combines crop growth model and remotely sensed data has been proved the most potential approach in regional yield estimation. Deqing County was taken as the study area. Based on the calibration and regional of the World Food Studies (WOFOST) model, WOFOST had been used to express the characteristic of time series LAI in crop growth season. 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 indicate that selecting the time scale of the step length between 10 days and 16 days about the assimilation of the remote sensing information and WOFOST model is 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. The research can provide an important reference value for the regional crop production estimation.