Aboveground biomass (AGB) is an important indicator to guide the management of livestock industry, and it is the basis of comprehensive analysis of the balance between grassland and livestock. To date, only few studies have studied the spatial distribution of grassland AGB in Qilian Mountains, and the scale differences of multi-sources data have not been well solved. Therefore, in order to understand the spatial distribution of AGB in Qilian Mountains, we used the space-air-ground integrated method based on Sentinel-2 multispectral data, Unmanned Aerial Vehicle (UAV) data and the measured AGB data during the growth period of vegetation in 2021. In addition, we analyzed the applicability of Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT) and eXtreme Gradient Boosting (XGBoost) algorithms for AGB inversion. Finally, we mapped the spatial distribution of AGB in the study area using the optimal model among all the models. The results verified that the effectiveness of vegetation indices varied in study area. Generally, the results indicated that the spatial distribution had an increasing trend from northwest to southeast, with an average AGB density of 925.43 kg/hm². A significantly positive correlation was found between 6 vegetation indices and measured AGB, and both of the indices was suitable for inversion of grassland AGB in the Qilian Mountains. Moreover, compared with other models, the performance of XGBoost model was the best, with the highest R² of 0.78 and accuracy of 74.75%, the lowest Root Mean Squared Error (RMSE) of 99.74 kg/hm²and Mean Absolute Deviation (MAE) of 71.60 kg/hm². In addition, UAV data provided spatial characteristics in detail, which reduced the scale difference between Sentinel-2 and the measured data. Therefore, on the basis of the correlation between 6 vegetation indices and AGB, it is of practical significance to construct the XGBoost model to invert the spatial distribution of AGB in grassland of Qilian Mountains. The results can provide a reference value and data support for guiding the development of livestock industry and maintaining the balance of grassland ecosystem.