Abstract: Remote sensing technology combined with sample plot data allows for robust and efficient estimation of growing stock volume, which greatly reduces survey time and costs and facilitates regular monitoring. Taking Wangyedian Forest Farm in Chifeng City, Inner Mongolia Autonomous Region as the study area and a typical temperate coniferous plantation forest as the research object, the forest stock volume of the plantation forest in the whole study area was estimated by the two-stage extrapolation method using the random forest algorithm in combination with the measured sample data, the UAV-LiDAR data and the Sentinel-2 multispectral data. The measured sample plot data were extrapolated to the UAV-LiDAR sample plots to build the Field-UAV-LiDAR model, and the UAV-LiDAR estimated forest stock volume generated in the first stage was extrapolated to the full-coverage Sentinel-2 satellite image to build the Field-UAV-LiDAR-S-2 model. The direct prediction model（Field-S-2） was compared by combining with the measured sample plot data and the feature variables extracted from Sentinel-2. The results showed that the highest accuracy of growing stock volume estimation（R~2=0.79, RMSE=42.34 m~3/hm~2, rRMSE=19.24%） was based on the Field-UAV-LiDAR model, followed by the accuracy of growing stock volume estimation based on the Field-UAV-LiDAR-S-2 model（R~2=0.68, RMSE=50.19 m~3/hm~2, rRMSE=29.43%）, and the lowest accuracy of growing stock volume estimation based on Field-S-2 model（R~2=0.49, RMSE=63.25 m~3/hm~2, rRMSE=33.08%）. The results of the study verified the validity of combining UAV-LiDAR and Sentinel-2 data to estimate forest stock volume, which provides a reference idea for estimating forest stock volume in large areas.