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The remote sensing of unmanned aerial vehicle (UAV) is accurate, flexible and fast. It is of great significance for large-scale agricultural management and water efficiency evaluation to establish yield estimation model of summer maize based on drone remote sensing. It was reported such an effort for summer maize in Inner Mongolia by using UAV multi-spectral platform. Six kinds of linear models for the measured summer maize yield maize as function of various vegetation indices derived at various growth stages were constructed by using Newton-trapezoidal integral and least squares method. And the threshold filtering method was used to reduce the influence of soil noise on the accuracy of the model. The results showed that there were significant differences in the accuracy of the models at different growth stages. In single growth period, the model precision from high to low was ordered as tasseling silking, wax maturity, and jointing, and the optimal vegetation index was EVI2 (R2=0.72,RMSE was 485.46kg/hm2).For most growth periods the superior vegetation index was GNDVI (R2=0.89,RMSE was 299.35kg/hm2). After soil filtration, the increase of R2 in jointing stage and multiple growth stages was significant. The correlation coefficient R2 was increased to above 0.87 for the multifertility estimation model based on vegetation indices GNDVI, MASVI2 and EVI2. In summary, the UAV yield estimation model can quickly and effectively diagnose and assess crop growth and yield. The estimation accuracy of the model in multiple growth periods was better than that in a single one, and GNDVI was the optimal model parameter. The threshold filtering method can effectively improve the estimation accuracy.