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 (Ｒ 2 =0. 72，ＲMSE was 485. 46 kg/hm 2 )． For
most growth periods the superior vegetation index was GNDVI (Ｒ 2 =0. 89，ＲMSE was 299. 35 kg/hm 2 )．
After soil filtration，the increase of Ｒ 2 in jointing stage and multiple growth stages was significant． The
correlation coefficient Ｒ 2 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．
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