Abstract: Forest stock volume is an important indicator for assessing the productivity of ecosystems, and also the basis for analysis of substance circulation in forest ecosystem. Forest stock volume of the different scales area can be estimated based on remote secsing technique, so the spatial distribution and dynamic monitoring of the forest stock volume are significant by using remote sensing techniques. Compared with the traditional optical remote sensing image, the fully polarimetric synthetic aperture radar(PALSAR)is almost unaffected by atmosphere and has the observation capabilities of the whole day and the ability to penetrate clouds, rain and snow. The fully polarimetric SAR image contains more abundant information. Since the fully PALSAR image has a great advantage of being able to obtain the fully polarized scattering attributes of the target object, it is widely used in estimation of forest parameters. In this paper, Zijinshan National Forest Park in Nanjing was chosen as the case study area, while PALSAR image in 2011 and 120 field plots in 2012 were collected as the main information source to estimate scenic forest stock volume. 13 factors including characteristic values extracted from Pauli and Cloude target decomposition, backscattering coefficients of HH and HV, ratio vegetation index, terrain and human disturbances were used to estimate forest parameters of unit stock volume using six remote sensing based models namely multivariate linear regression(MLR), artificial neural network(ANN), K nearest neighbor classification algorithm(KNN), classification and regression tree(CART), bagging(Bagging)and random forest(RF). Research results showed that: ① among the six models, the performance of random forest was the best, followed by bagging method, and multivariate linear regression was the worst; ② terrain factors of DEM(digital elevation model)and aspect, backscattering features of polarization radar echo were important environmental variables which affect unit stock volume; ③ spatial distribution of unit stock volume showed a zonal pattern descending from center to the periphery.