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The observation data of LAI-2000 and vegetation index was generated by satellite remote sensing data of HJ, combining three kinds of commonly used regression model. LAI (Leaf area index) inversion model was constructed according to growth period, growth stage and threshold boundaries of summer maize, respectively. The optimal LAI inversion model was acquired based on the above three modes. The summer corn LAI scatter grams of the vegetative growth period, the tasseling stage as well as the reproductive stage were generated after verification and evaluation of the model reliability. The productions of MODIS LAI (MOD15A2) were verified by LAIHJ based on the inversion of model HJ image. According to the survey, except HJVI, during the whole growth period of summer maize, a linear model of RVI with LAI was regarded as the best fitting model (R2 = 0.662); during the vegetative growth period, a linear model of OSAVI with LAI was regarded as the best fitting model (R2=0.724); at the tasseling stage, index model of OSAVI with LAI was regarded as the best fitting model(R2=0.749); at the reproductive stage, a linear model of NDVI with LAI was regarded as the best fitting model(R2=0.700). The correlation of HJVI and LAI at the growth period achieved to 0875, and the correlation at different growth stages with LAI is higher than the other vegetation indexes (during the vegetative period, R2=0.769; at the tasseling stage, R2=0.783; at the reproductive stage, R2=0.703). EVI is the best index when LAI is less than 3 (R2=0.358), while OSAVI is the best when LAI is more than 3(R2=0.515). During the three reproductive periods, R2 of LAIM and LAIHJ is 0.732, 0.761 and 0.661. Conclusions were drawn: the inversion method of LAI at different stages is optimal. HJVI shows obvious advantage for LAI inversion ability. The production of MODIS LAI could be used for crop monitoring in special situation. The study not only broadens the mode of inversion LAI using vegetation index, but also confirms the importance of HJ data in agricultural field.