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In view of the low efficiency and high cost of traditional agricultural yield estimation methods, taking winter wheat in Shandong Province as an example, and the cumulative enhanced vegetation index (EVI), cumulative crop water stress indicator (CWSI) and trend yield were used to build a statistical yield estimation model in Shandong Province with least square method. Cumulative EVI was calculated from 8-day surface reflectance products (MOD09A1), cumulative CWSI was calculated from 8-day global terrestrial evapotranspiration products (MOD16A2), and trend yield was calculated using historical yield data calculated by method of time trend analysis. The yield estimation model was operated and verified in monitoring mode and forecasting mode respectively. In the monitoring mode, the provincial yield estimation accuracy was 96.91%, and the estimation accuracy of each city was above 89.64%. Heze City had the highest monitoring accuracy, which was 99.31%, and Jining City had the lowest value, which was 89.64%. In the forecasting mode, the model was operated and verified in threetime points of growth period: the end of the rejuvenated period (the 89th day), the end of the jointing period (the 121st day) and the end of the milk ripening period (the 145th day). The prediction accuracy of wheat was over 96.44% at provincial level and over 89.41% at municipal level during three-time points. The forecast accuracy of Qingdao City was the highest, with an average of 99.07%, and that of Jining City was the lowest, with an average of 89.81%. The yield estimation model had a high applicability to the estimation of crop yield at municipal and provincial levels, which can realize the constantly yield prediction. The method of monitoring and forecasting yield was conducive to timely understanding the growth condition and changes of winter wheat, and it had a certain reference value for the government departments to make scientific and effective agricultural production decisions.