Abstract: Objectives: Net ecosystem productivity (NEP) is a key indicator of carbon sinks in terrestrial ecosystems. To accurately estimate NEP, it is essential to use high-resolution images and account for topographic effects, especially in mountainous areas, which have important implications for regional carbon budget and land use structure. Methods: We calculates NEP based on CASA and GSMSR models for Hubei Province. And then we optimize the results by applying two types of topographic correction: meteorological factor correction and surface area correction. We compare the NEP values before and after correction and analyze their spatiotemporal pattern. Results: The results show that: (1) Topographic correction can effectively improve the accuracy of NEP calculation. Compared with the data of national flux stations, the mean absolute error (MAE) decreased from 153.69 gC·m^-2·a^-1 before correction to 150.73 gC·m^-2·a^-1 after correction, and the total NEP of Hubei province was 21.21 million tons. (2) Previous studies underestimated the carbon sequestration of forest land. After topographic correction, the NEP of forest land increased by about 22% compared to before correction, followed by grassland, while other land use types showed less increase after topographic correction. (3) The topographic correction significantly improves the accuracy of NEP in areas with high altitude and large terrain variations. The spatial autocorrelation trend also decreases after correction, as indicated by the reduction of Moran’s I index from 1.24 to 1.18. Moreover, the topographic correction causes a noticeable westward shift of the center of gravity in spring and autumn. Conclusions: Compared with before topographic correction, the NEP results after correction have higher accuracy, and the carbon sink distribution of different land use types also differs. But because the influence of terrain on carbon sink estimation is reflected in multiple aspects. Hence, in future work, we will continue to explore the influence of topography on vegetation factors, meteorology, and remote sensing images, to improve the simulation accuracy of the model.