Studies on wetland vegetation productivity and their carbon sequestration potential are becoming an important focus of the global carbon cycle and global climate change research. The photosynthetic capacity of wetland vegetation can indicate the health status of its growth. In addition, an accurate estimate of maximum carboxylation rate (V_cmax) is important for accurately simulating wetland vegetation photosynthesis and carbon sequestration processes with a gas exchange model. Here, the wetland of Wuliangsuhai (Inner Mongolia) was chosen as the study area, and the photosynthetic parameters and spectral reflectance of reed leaves were measured. Based on the Farquhar model of photosynthesis, reed leaf V_cmax values were calculated from A-C_i curves, and subsequently standardized to 25℃. Estimation models of V_cmax for reed leaves in the wetland were constructed with a bootstrap PLSR model and single band and hyperspectral vegetation indices (e.g., simple ratio index (SR) and normalized difference index (ND)). Based on hyperspectral remote sensing images from HJ-1A HSI, the bands of 702 and 756 nm, which had a higher estimation accuracy for V_cmax, were selected from the ND hyperspectral indices. Subsequently, a spatial distribution map of V_cmax for wetland reed was acquired for the study area. The results showed that the spectral characteristics of wetland vegetation, combined with hyperspectral vegetation indices, could be used effectively to accurately estimate reed V_cmax in the wetland. The highest accuracy was produced from the modeling method based on a bootstrap PLSR model (R²=0.87,RMSECV=3.90,RPD=2.72). Furthermore, the accuracy of V_cmax estimations from the ND hyperspectral indices was higher than that from the SR hyperspectral indices. Overall, the estimated values extracted from the spatial distribution map of V_cmax had a good correlation with the measured values (R²=0.80,RMSE=4.74).