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To establish a comprehensive index for monitoring the crop growth and estimating the crop yields accurately, the leaf area index (LAI), aboveground biomass and soil moisture (0~20cm) simulated by the CERES-Wheat model were assimilated with the state variables retrieved from Landsat data using the particle filter algorithm, for obtaining daily assimilated LAI, aboveground biomass and soil moisture values. Then linear regression analyses were performed to examine the relationships between the assimilated LAI, aboveground biomass or soil moisture and field-measured yields respectively, which were combined with the combination forecasting of entropy method, for determining the weights of different variables at the main growth stages of winter wheat. The comprehensive index was established based on the weights of variables, and the linear correlations between comprehensive index and measured yields were used for establishing wheat yield estimation model. The results showed that the root mean square errors (RMSEs) and mean relative errors (MREs) between the assimilated state variables and the field-measured ones were lower than the RMSEs and MREs between the simulations and the field-measurements, respectively. Thus the accuracies of the assimilated LAI, aboveground biomass and soil moisture time series were improved through the assimilation process. In addition, the correlation coefficients between the comprehensive index and the yields were higher than those between the individual variables and the yields at each wheat growth stage. And the accuracy of the yield estimation model established based on the comprehensive index (R2 was 0.78 and RMSE was 330kg/hm2) was significantly higher than those of the models established based on the LAI (R2 was 0.62 and RMSE was 448kg/hm2), aboveground biomass (R2 was 0.64 and RMSE was 431kg/hm2) and soil moisture (R2 was 0.67 and RMSE was 442kg/hm2) respectively. Therefore, the established comprehensive index fully integrated the advantages of the different variables in estimating crop yields, which can be used for estimating wheat yields accurately.