To improve the precision of crop yield estimation by integrating the remote sensing data into the crop model, two methods were applied, the four-dimensional variational (4DVAR) and the ensemble Kalman filter (EnKF), to assimilate the leaf area index (LAI) and the soil moisture (θ) derived from Sentinel multi-source data with the CERES-Wheat model. The two algorithms were assessed on the performance of assimilation of LAI and θ and estimated the yield of winter wheat across three counties located in the south of Shanxi Province in China. It was found that both assimilation algorithms can combine the advantages of remote sensing observations and crop model simulations. Compared with the crop model simulation values, the accuracy of assimilated LAI and θ were improved. Compared with EnKF, the 4DVAR algorithm can reduce the RMSEs of the assimilated LAI and θ by 0.1490m2/m2 and 0.0091cm3/cm3, respectively. And 4DVAR-LAI could accurately identify the phenological period of winter wheat according to the remote sensing observations, which was more consistent with the growth and development of the actual phenological period of winter wheat. Therefore, 4DVAR showed a better performance in the assimilation of Sentinel multi-source data with CERES-Wheat model. The accuracy of the yield estimation model based on assimilated LAI and θ by 4DVAR (RMSE was 449.77kg/hm2, MRE was 7.85%) was higher than the yield accuracy based on simulated values by the CERES-Wheat model (RMSE was 641.55kg/hm2, MRE was 10.23%). The 4DVAR assimilation algorithm effectively improved the yield estimation accuracy of winter wheat at a regional scale.