Optical spectral remote sensing images can be used to extract corn canopy structure information rapidly in a large area. However, it cannot provide vertical canopy structure information, which leads to underestimated leaf area index (LAI) result. Terrestrial laser scanning can provide high precision 3D structure information of corn canopy, but only in the limited sampling area. Therefore, these two technologies are combined to extract high precision canopy structure through canopy analysis by using terrestrial laser scanning data voxelization method. Reflectance of large area of corn canopy using Landsat8 optical images is obtained, and accurate corn canopy LAI results are got through regression analysis of canopy structure information of voxel-based canopy profiling. The results show that LAI has the strongest correlation with the normalized difference vegetation index (NDVI), the correlation coefficient R2=0.8086, the root mean square error (RMSE) is 0.1230, and the correlation between LAI and ratio vegetation index (RVI) is the worst, R2=0.7079, RMSE is 0.1520. Based on the validation analysis of the measured values, the average relative error of the three models is lower than 10%, and the credibility of the three models is relatively high.