Abstract: Using remote sensing methods to accurately estimate aboveground biomass carbon stock（ABGCS） in forest canopy layers and light saturation value of carbon storage, aiming to replace the cumbersome procedures of traditional large-area surveys, providing references and basis for carbon storage estimation, and improving the efficiency of sustainable forest management. In this study, the ABGCS in Jiayin County, Yichun City, Heilongjiang Province in 2017 was selected as the research object. Landsat 8 OLI remote sensing images and forest resource two-class survey data were used to construct parameter models of stepwise multiple regression model（SMR）, non-parameter models of BP neural network model（BP-NN）, random forest model（RF）, support vector regression model（SVR） to estimate and reverse the spatial distribution of ABGCS in Jiayin County. The research results showed that the estimation accuracy of non-parameter models was significantly higher than that of parameter models. Among them, the fitting accuracy of the three non-parameter models（BP-NN, RF, SVR） was increased by 25. 0%, 12. 2%, and 7. 3%, respectively, compared with the parameter model（SMR）. By comprehensive comparison of the evaluation indexes of the four models in ten-fold cross-validation, the performance of the models was analyzed: BP-NN＞RF＞SVR＞SMR, among which the BP-NN model fitted the largest R~2（0. 785） and the smallest RMSE（3. 572 t/hm~2）, MSE（12. 757 t/hm~2）, MAE（2. 687 t/hm~2）. From the perspective of carbon storage residual segmentation test results, all four models exhibited varying degrees of overestimation and underestimation of carbon storage. The BPNN model had the smallest ME and MRE values in each carbon storage segment, indicating strong generalization ability. The light saturation value of ABGCS was determined to be 63. 056 t/hm~2 using a cubic model, which was close to the predicted ABGCS light saturation value by BP-NN（64. 232 t/hm~2）. Therefore, the BP-NN model has a relatively ideal effect in estimating ABGCS in Jiayin County, providing important basis for dynamic monitoring and research of forest carbon storage.