Abstract: Taking the relationship between vegetation cover change and its influencing factors in Xinjiang as the research object, by comparing multiple linear regression, random forest regression, XGBoost and support vector machine regression, the model with the highest accuracy was selected, and 15 influencing factors were reorganized and analyzed according to the attribute importance degree calculated by the optimal model. The effects of 15 factors including air temperature, precipitation, radiation amount, potential evaporation, longitude, dimension, elevation, landform type, slope, slope orientation, human impact index, runoff, soil type, soil moisture, and vegetation type on vegetation cover change were explored. The results showed that XGBoost model had the highest prediction accuracy for normalized vegetation index （NDVI）, followed by random forest regression. In the study area, the most influential factors on NDVI were soil moisture, runoff, vegetation type, longitude, potential evaporation, air temperature, radiation amount, landform type and precipitation. In terms of the types of influencing factors, climatic conditions had the greatest influence on NDVI, followed by soil characteristics, and the topographic and geomorphic factors were the lowest compared with the first two.

Key words: machine learning, vegetation cover, XGBoost, prediction model, influence factor, normalized vegetation index （NDVI）, Xinjiang