Abstract: 【Objective】 Cotton is an important economic crop in Xinjiang, so obtaining cotton chlorophyll content (SPAD value) quickly and accurately on the field scale is of great significance for accurate monitoring of cotton growth status and improving cotton yield and quality prediction. In this study, multi-spectral remote sensing technology combined with machine learning method was used to retrieve the SPAD value of cotton in Aksu area.A feasible method for large area estimation of SPAD value of cotton in the field, and provides an important reference for non-destructive and real-time monitoring of crop growth index. 【Methods】 The split zone design was used in the experiment, three nitrogen application levels and three irrigation quotas were selected. Firstly, the response law of SPAD value of cotton under different water and nitrogen treatments was analyzed. Then the spectral characteristics of cotton multispectral images in different periods were further analyzed and the vegetation index was constructed. The correlation between vegetation index and SPAD value was analyzed, and the vegetation index with high correlation was selected. Four machine learning algorithms were used to model and analyze the SPAD value and multi-spectral index of the whole growth period of experiment 1 and experiment 2, and the optimal monitoring model was selected. The SPAD value of cotton in different periods were predicted and inversed, and the model was verified by different field data. 【Results】 The SPAD value of cotton was estimated by UAV multispectral images and machine learning algorithm, and it was found that different growth periods were significantly affected by irrigation and fertilization conditions. The better estimation accuracy was obtained by screening the appropriate spectral index and modeling with the random forest model, and the estimation result of the model was the best at the flowering and boll stage, and the estimation progress R² of the model was between 0.68 and 0.73. The RF model had good stability in estimating the SPAD value of leaves among different fields. 【Conclusion】 The estimation of SPAD value of cotton leaves by RF algorithm based on UAV multispectral image calculation has good accuracy and stability.