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To validate the feasibility of particle filter assimilation algorithm for crop yield estimation, and improve accuracy of summer maize yield estimation in the central plain of Hebei Province, the leaf area index (LAI) and vegetation temperature condition index(VTCI) simulated by the CERES-Maize model were assimilated with the state variables retrieved from MODIS data. The random forest method was used for determining the weights of different variables at the growth stages of summer maize. The maize yield estimation model was established based on the weights of variables and measured yield. The results showed that no matter at the sampling sites or at regional scale, the assimilated LAI and VTCI were better able to respond the monitored LAI and VTCI, the assimilated LAI decreased the sharp changing points which LAIs were simulated by CERES-Maize, the assimilated VTCI was in good agreement with those of both the remotely sensed VTCI and the simulated VTCI, and the assimilated VTCI was a good index for indicating crop water stress of summer maize. The optimal model was selected for summer maize yield and estimation accuracy of the year 2015 in the central plain of Hebei Province, normalized root mean square error (NRMSE) between simulated and observed summer maize yields before and after performing PF assimilation scheme was decreased from 12.71% to 10.50%, and relative error (RE) was decreased from 12.57% to 8.43%. Therefore, the established yield model based on the assimilated LAI and VTCI fully integrated the advantages of remote sensing information and crop model, and can be used for estimating summer maize yield accurately.