Taking the Wangyedian Forest Farm in Inner Mongolia as the research area,combined with ground surveys,and based on the preprocessing of the GF-2 remote sensing data,48 remote sensing factors such as spectral information,vegetation index and texture information were extracted,and 8 remote sensing factors were selected for modeling by Pearson correlation coefficient method.Using multiple linear regression,multi-layer perceptron,K-nearest neighbor,support vector machine,and random forest model to estimate the forest volume,the forest volume inversion map in the study area was obtained.The results showed that:1) Among the remote sensing factors extracted from GF-2,mean of texture features based on the second-order matrix had a higher correlation with the forest volume;2) Random Forest had better estimation accuracy of forest volume than methods such as multiple linear regression,multi-layer perceptron,K-nearest neighbor and support vector machine,and its relative root mean square error (rRMSE) was 25.40%;3) The areas with high forest volume in the study area were mainly distributed in the west and southeast;the areas with low forest volume were mainly distributed in the northwest,central and northern parts,which were consistent with the actual investigation.The domestic GF-2 image and random forest algorithm had certain potential in the inversion of forest volume.