Abstract: Lingqiu County, Jiexiu County, Xi County and Yanhu County of Shanxi Province in the eastern part of the Loess Plateau were selected as the study area. Field observation data from 2005 to 2012 were used to analyze the sensitivity of model parameters by using EFAST method, and the growth parameters of maize were adjusted by trial and error method. On this basis, MCD15A3H remote sensing data was fused, leaf area index （LAI） data, which was taken as the coupling variable was assimilated into the calibrated WOFOST model using SUBPLEX algorithm, and the growth and development process of maize in each region was simulated again. The results showed that the calibrated WOFOST model had better simulation results for growth period and yield. The average error between simulated value and measured value in the growth period was less than 3 days, the correlation coefficient （r） between simulated value and measured value of yield was 0.80, and the root mean square error （RMSE） was 956 kg/hm². After assimilating remote sensing data with WOFOST model, the r of simulated and measured yield increased from 0.80 to 0.91, and the RMSE decreased from 956 kg/hm² to 660 kg/hm².

Key words: data assimilation, WOFOST model, remote sensing data, dryland corn, precision evaluation