The chlorophyll within crop leaves and crop canopy produces energy and participates in photosynthesis process by absorbing sunlight. Therefore, it is important to estimate the crop canopy chlorophyll content timely and accurately for crop growth monitoring, nutrient content monitoring and crop quality evaluation. Sentinel-2 has a wide-swath sensor with 5-days revisit period, so the Sentinel-2 image is produced with high spatial resolution (10 m) and 13 spectral bands. Specially, there are three red edge bands in Sentinel-2 image, which are sensitive to crop canopy chlorophyll content and its change. So the Sentinel-2 image is an ideal remote sensing data source for chlorophyll content estimation. Vegetation indexes depict the difference for the crop between different growth conditions and different chlorophyll contents, through the band combinations based on the reflection characteristics of crops at different spectral bands. So the vegetation indexes from Sentinel-2 image can be used to estimate the corn canopy chlorophyll content timely and accurately in a regional area. Therefore, this study is focusing on estimating the corn canopy chlorophyll content using 10 kinds of vegetation indexes computing from Sentinel-2A remote sensing images. And the study area is located in three counties of Baoding City, Hebei Province, ranging from 115°29′E to 116°14′E, 39°5′N to 39°35′N. We measured the corn plant chlorophyll content in 24 sampling areas distributed randomly in the whole study area from 6 August to 11 August, 2016. And each sampling area was located using Huace i80 real-time kinematic (RTK) GPS receiver (Huace Ltd., Shanghai, China). The Sentinel-2A image was preprocessed including geometric correction, radiometric calibration and atmospheric correction, and Sen2Cor model and SNAP were used to do atmospheric correction. 10 vegetation indexes were computed including CIgreen(Green Chlorophyll Index), CIred-edge(Red-edge Chlorophyll Index), DVI(Difference Vegetation Index), LCI(Leaf Chlorophyll Index), MTCI(MERIS Terrestrial Chlorophyll Index), NAVI(Normalized Area Vegetation Index), NDRE(Normalized Difference Red-Edge), NDVI(Normalized Difference Vegetation Index), RVI(Ratio Vegetation Index), SIPI(Structure Insensitive Pigment Index). Secondly, the statistical correlativity was analyzed between these 10 vegetation indexes and measured chlorophyll content value for every sampling area. So the corn canopy chlorophyll content estimating was developed using this correlation analysis results. Lastly, the optimal chlorophyll content estimation model was selected to estimate the chlorophyll content in the whole study area. This study was focusing on (1) developing the estimation model for corn canopy chlorophyll content in the study area, and the accuracy was assessed using R2, RMSE and RE; (2) deciding the optimal band combination; (3)deciding the optimal amount of red edge band participating in vegetation indexes calculation. The accuracy assessment results indicated that (1) there was polynomial correlation between measured chlorophyll content and the selected 10 vegetation indexes in this study, and the accuracy of estimated chlorophyll content using the vegetation indexes considering the red edge bands is better than the ones without red edge bands. The CIgreen(560, 705)and DVI which were all considering red edge bands improved the chlorophyll content estimation accuracy, and the R2 improved 0.516 for CIgreen(560, 705). The statistical relationship between the measured chlorophyll content and the vegetation index in the field work was established, and the relationship was extended to the whole study area. This study was about the estimation of corn canopy LAI and chlorophyll content using these ten vegetation indexes, which was focusing on the following four parts. Firstly, we compared if the vegetation with or without red-edge band could get accurate LAI and chlorophyll content estimated result. Secondly, we added two red-edge bands to the vegetation indexes without red-edge band originally. Thirdly, we added two red-edge bands to the vegetation indexes with one red-edge band originally only. Fourthly, we set up the vegetation index with two red-edge bands. The results showed that there are polynomial regression between the selection of multi-VI and the field survey of canopy chlorophyll content. Because the introductions of the red edge band, the fitting accuracy improved more than 0.3 between the vegetation index and corn canopy chlorophyll content, and the CIgreen (560, 705) (Green Chlorophyll Index) improved 0.516 that is the highest. The index calculating between the visible light band and the first red edge band (705 nm), the near infrared band with the second red edge band (740 nm), both of which established the regression model with the field survey of corn canopy chlorophyll content, and promoted the best fitting precision. The MTCI (MERIS Terrestrial Chlorophyll Index) has the highest fitting precision in which the R2 is 0.803, RMSE is 3.185, RE is 4.819%. It is shown that adding the red edge band will improve the fitting precision and it is suitable for crop growth monitoring.