Leaf area index is one of the important parameters to describe the canopy structure of corn, which determines the biophysical processes of corn canopy photosynthesis, respiration, transpiration and carbon cycle. Therefore, retrieval of leaf area index is of great significance to corn growth monitoring. The Sentinel-2 remote sensing image and LAI-2000 ground synchronous data were used to retrieve the leaf area index of corn canopy. Normalized difference spectral index (NDSI) and ratio spectral index (RSI) were extracted to build the univariate and multivariate empirical models. The best LAI retrieving models were identified based on the best combinations of coefficient of determination (R2) and root mean square error (RMSE). Finally, spatial distributions of LAI in the study area were mapped through the optimal retrieve model. Results showed that all spectral indices tested were significantly correlated with LAI of corn, and the correlation between spectral indices built with red-edge bands and LAI was higher than that built without red-edge bands. Validation analysis result indicated that although the accuracy of the multivariate empirical model was high, its ability to predict LAI was poor. Linear regression model of NDSI（783,705） most accurately explained retrieval of LAI of corn, with R2 of 0.5342 and RMSE of 0.2885. Therefore, linear regression model of NDSI（783,705） was recommended as the most legible model for estimating LAI of corn. The red-edge bands confirmed from Sentinel-2 remote sensing image improved the accuracy of retrieving the LAI of corn. Moreover, the results also provided a powerful evidence to develop the Sentinel-2 remote sensing image and red-edge bands application in retrieving the LAI of corn.