Abstract: 【Objective】 This project aims to explore the ability of UAV multi feature to construct D. tianschanicum aboveground biomass (AGB) estimation model.The finding has provided a reference for the grading basis for the classification of grassland degradation degree. 【Methods】 Diarthron tianschanicum is one of the degradation indicator plants, and its growth status can reflect the degree of grassland degradation. and to extract the spectral features, texture features and D. tianschanicum coverage from visible high spatial resolution remote sensing images, and the three were used as inputs to establish a univariate linear model. The three types of features were fused to construct multiple stepwise regression and artificial neural network models, and the effect of multi feature fusion to estimate AGB was analyzed. 【Results】 (1) The best coverage extraction window period of D. tianschanicum was in full bloom, and the effect of D. tianschanicum extraction model constructed by RF algorithm was ideal, and the overall accuracy was more than 81%. (2) Spectral features, texture features and coverage were all correlated with AGB, and the texture feature G had the highest correlation, which was 0.784. (3) Compared with single vegetation index, texture feature, coverage and any two feature combinations as input amount, the accuracy of AGB was the highest, with R² and RMSE of 0.870 and 15.383, respectively. 【Conclusion】 It is verified by artificial neural network mode that the fusion of spectral features, texture index and coverage can effectively improve the accuracy of AGB estimation.