In order to realize rapid, non-destructive and real-time monitoring of the leaf area index of cotton plants under different irrigation treatments, the canopy reflectance of cotton plants in four growth periods was obtained with the help of hyperspectral remote sensing technology, and the leaf area index of each cotton plant was obtained at the same time. The spectral preprocessing methods such as first-order derivation, second-order derivation, standard normal variate, multiple scattering correction and wavelet analysis were used to extract characteristic bands through continuous projection algorithm, PLS was used to establish hyperspectral estimation models for four growth periods and each growth period. Comparing the modeling accuracy of six pretreatment in four growth stages and each growth stage, it was shown that the wavelet decomposition scales of four growth stages, bud stage, flower stage and flower boll stage were 4, 2, 8 and 2, respectively, and the models were CWT-SPA-PLS, CWT-FD-SPA-PLS, CWT-SPA-PLS and CWT-FD-SPA-PLS respectively, which can achieve better accuracy;after SD treatment, better results were obtained in boll stage, R2 and RPD were 0.973 and 5.3295 respectively, which were better than other pretreatment results. The experimental results showed that the spectral information obtained by the preprocessing algorithm, especially the wavelet analysis method, can effectively estimate the leaf area index of cotton in four growth stages and each growth stage.