Abstract: Exploring the potential of domestic high-resolution data in the estimation of forest carbon storage estimation research provides a new approach for the construction of forest carbon storage estimation model. In this study, the Jiufengshan National Forest Park in Wuhan City was selected, GF-2 remote sensing image was used as the data source, and ground measured information was combined to estimate forest AGC storage in the Park. A total of 6 vegetation indices, 4 band values and 8 texture features were extracted, and 9 remote sensing variables that related to measured carbon storage were screened out. Linear and nonlinear equations were used to model a single highly correlated variable and multiple correlated variables, and subsequently the optimal model was therefore selected. In order to further improve the prediction accuracy, the model was carried into four texture Windows (3×3, 5×5, 7×7, 9×9). The results showed that the vegetation index extracted from remote sensing images had strong collinearity, and the accuracy of the single variable model was lower than that of the multiple regression model. The root-mean-square (RMSE) and the coefficient of determination R² were used to evaluate the prediction accuracy of the model under four Windows. We showed that the model had the best prediction power under the 5×5 window (R²=0.73, RMSE=0.5), and the prediction power was the lowest under the 3×3 window (R²=0.64, RMSE=0.8), compared with all the estimated models, the accuracy of the model is improved by 0.11 in the texture window. Therefore, the constructed multivariate model was used to estimate carbon storage with a 5×5 window. The total carbon storage in the Jiufengshan National Forest Park was 1.06×10⁴ t, the overall average carbon density was 84.59 t/hm², it has a certain carbon fixation effect. Using domestically produced high-resolution image of GF-2 satellite imagery data to invert Jiufengshan Forest Park in Wuhan, it can be well used in the field of quantitative carbon storage and growth status of forest vegetation. The research has important scientific significance for the monitoring and management of forest carbon sink under the “carbon peaking and carbon neutrality” target.