Accurate estimation of leaf or canopy chlorophyll content is essential for monitoring crop growth. Crop chlorophyll monitoring using remote sensing is a non-destructive， large-area，real-time monitoring method， which requires reliable inversion models and satellite data. Focusing on summer maize， parameter settings of PROSAIL model were determined through local and global sensitivity analysis. In combination with measured ground data and related literature， canopy reflectance of summer maize based on PROSAIL model was simulated. Then， based on spectral response function of Sentinel-2A image， equivalent reflectance data of Sentinel-2A image were obtained. Typical hyperspectral vegetation indices and vegetation indices of improved band combination mode based on Sentinel-2A image data were calculated and analyzed to determine the best estimation model of Leaf Chlorophyll Content （LCC） and Canopy Chlorophyll Content （CCC）. Based on PROSAIL simulation data， Sentinel-2A image data and ground measurement data， the modeling and validation analysis of summer maize LCC and CCC were carried out. The results showed that the R² of vegetation indices inversion based on PROSAIL model and Sentinel-2A image were 0.61 and 0.65， RMSE were 7.54 μg/cm2 and 8.46 μg/cm2， respectively. The inversion accuracy of LCC using vegetation indices based on PROSAIL model and Sentinel-2A image was consistent， and the inversion accuracy met the requirements of summer maize growth monitoring. The R² of CCC retrieved by the two methods were 0.75 and 0.77， RMSE were 1.03 g/m² and 0.02 g/m²， respectively. This study provides an effective method for retrieving chlorophyll content in the region where there are few ground measured data， which is helpful for the growth and pest control monitoring of summer maize.