Forest stand volume estimation is an important research field of forestry remote sensing.Factors such as cloud and fog weather and spectral saturation have restricted the accuracy of optical remote sensing image estimation.Synthetic Aperture Radar(SAR) images have the characteristics of strong penetrability and are less likely to be affected by cloud and fog,which make up for the deficiencies of optical remote sensing.This study uses the coniferous forest in Longnan county,Jiangxi Province as the study area,combines Landsat 8 and PALSAR-2 dual-polarization SAR image data to extract a total of 245 remote sensing factors such as spectral information,vegetation index,texture information and backscattering coefficient based on remote sensing data preprocessing.Based on the Pearson correlation coefficient method and the multiple stepwise regression method,65 remote sensing factors are selected for the estimation of stand stock.Taking forest stand canopy closure as a stratification factor,the study adopts five models of linear,KNN,support vector machine(SVM),multi-perceptron(MLP) and random forest(RF) to estimate the forest stand volume,and tests the accuracy of the estimated results.The experimental results show that:1) Compared with using the spectrum and texture information of Landsat8 alone,the backscatter information of PALSAR-2 based on the canopy closure classification and fusion significantly improves the inversion accuracy of accumulation 2) In low canopy closure forest stand,the linear model has the highest accuracy(rRMSE=21.16%),in medium canopy closed forest stand,the MLP model has the best estimation effect(rRMSE=30.61%),in high canopy closure forest stand,the MLP model has the best estimation effect(rRMSE=27.53%).Based on the backscattering coefficient of PALSAR-2,the canopy closure stratification can effectively improve the inversion accuracy of the medium and high accumulation areas.