Abstract: Abstract: As the core of photosynthesis, the accurate prediction of maximum carboxylation rate (Vcmax) is crucial to photosynthetic rate and vegetation productivity. At present, there are many studies which have predicted Vcmax by physiological parameters and spectral data. However, the studies mainly focus on forest, and study on leaf Vcmax of cotton has not been reported. In this experiment, "Guoxinmian 9" was selected as the experimental material, and four nitrogen application levels were set to determine the physiological parameters and leaf reflection spectra of cotton leaves in different growth periods. The relationships were explored between leaf Vcmax and physiological parameters, spectral indexes, and the hyperspectral indexes that could accurately retrieve the leaf Vcmax of cotton were screened out. Then leaf Vcmax estimation models were constructed. The results showed that the leaf Vcmax of cotton was not affected by the level of nitrogen application from the seedling stage to the bud stage due to the nitrogen in the soil without nitrogen fertilizer is enough to ensure that the leaf Vcmax of cotton maintain a high level from the seedling stage to the bud stage. As the growth period progressed, nitrogen became one of the main factors affecting the Vcmax of the leaves, and the correlation between leaf Vcmax and nitrogen content (R2 = 0.717) was higher than that of chlorophyll and leaf mass area. The Vcmax of the leaves had strong correlations with the combination of blue light and red-edge wave band through regression analysis of spectral indexes. The Normalized Difference Vegetation Index (NDVI) using reflectance at 697 and 445 nm, as well as Ratio Vegetation Index (RVI) using reflectance at 445 and 694 nm had the best fitting effects, and their values of R2 all exceed 0.75. In addition, from the 27 predecessor vegetation indexes, three vegetation indexes with a higher degree of correlation with leaf Vcmax were obtained: Photochemical Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index(MCARI), Modified Normalized Difference Vegetation Index(mND705), and the absolute value of the correlation coefficient of them were greater than 0.6. Finally, based on physiological parameters and spectral index, the estimation models of leaf Vcmax were established by general linear regression and multiple stepwise regression. The accuracy of the multiple stepwise regression model established by RVI445,694, PRI and mND705 was highest (R2=0.809, RMSE=16.93 μmol/(m2·s)), followed by the multiple stepwise regression model established by nitrogen and chlorophyll content in leaves (R2=0.801, RMSE=17.01 μmol/(m2·s)). In summary, leaf Vcmax of cotton is more sensitive to leaf nitrogen compared other physiological parameters.The accuracy of the leaf Vcmax estimation model based on leaf spectrum established in this study is higher than that using leaf nitrogen content, chlorophyll and leaf mass area as independent variables, indicating that it is feasible to invert leaf Vcmax through leaf spectrum. It is shown that hyperspectral index can effectively estimate the Vcmax of cotton leaves, and the results can provide support for accurate inversion of leaf Vcmax and assessment of photosynthetic capacity.