Abstract: Taking Tarbagatay Prefecture of Xinjiang as the study area, using vegetation index, meteorological data and terrain data as independent variables, combined with the measured biomass data of sample plots in the study area, five machine learning models including k-nearest neighbors regression （KNN）, multiple linear regression （MLR）, gradient boosting decision tree （GBDT）, random forest regression （RF） and Gradient Boosting Decision Tree （GBDT） were analyzed and compared, as well as two ensemble learning models constructed using voting regressor and stacking methods. The results showed that the stacking ensemble learning model had the best performance, with R² of 0.764, RMSE and MAE of 23.29 g/m² and 16.8 g/m², respectively. The optimal model was then used to invert and map above-ground biomass （AGB） of grassland.

Key words: above-ground biomass （AGB） of grassland, Landsat 8, remote sensing image, machine learning, estimation model, Tarbagatay Prefecture, Xinjiang