## 1 Introduction
The data required for NPP calculation by BEPS model mainly include meteorological data, land cover type and leaf area index (LAI). LAI is an important variable connecting remote sensing images and ecological process models. It can be calculated from vegetation index, which can be directly obtained from remote sensing images. Meteorological data including temperature, radiation, precipitation, wind speed and relative humidity are the main factors affecting carbon and water transport. Land cover types determine the initial values of the model. Using the above gridded data, BEPS model can extend the simulation of stand level to large-scale simulation. [PERSON] et al. [27] combined remote sensing data with an ecosystem model to estimate East Asian NPP, produced the distribution map of East Asian NPP in 1998, and calculated that the average NPP of that year was 634 g C m\({}^{-2}\). [PERSON] et al. [28] used the modified version of BEPS to simulate CO\({}_{2}\) flux, obtained the gross primary productivity (GPP) of the broad-leaved deciduous secondary forest in Japan from 1998 to 2002, and obtained the seasonal and interannual changes of GPP. The results showed that the interannual changes of GPP observed in alpine areas were mainly affected by LAI changes. [PERSON] et al. [29] improved the BEPS model in terms of water cycle, making it suitable for arid and semi-arid areas. They simulated the NPP of forests, grasslands and crops in Gansu Province in 2003. The results showed that the NPP of three vegetation types simulated by the improved BEPS model were more consistent with the ground measurement of Qilian Mountains than the original BEPS model. The highest difference rate of forests was 9.21%, and the lowest difference rate of crops was 4.29%. In September 2020, China clearly proposed to achieve \"carbon peak\" in 2030 and \"carbon neutrality\" in 2060. As the \"main force\" of carbon neutrality, forest ecosystem plays a very important role in accurately assessing the carbon budget of forests and quantifying the contribution of forests in realizing the vision of carbon neutrality [30]. Saihanba mechanized forest farm, located in Chengde City, Hebei Province, was established in 1962. It is a National Nature Reserve and National Forest Park. Historically, Saihanba was densely forested, but since the 19 th century, the primitive ecology has been seriously damaged and the forest has disappeared. For nearly half a century, Saihanba has been the site of efforts to build the world's largest artificial forest farm. In 2017 and 2021, respectively, the forest farm won the \"Earth Guardian Award\", the United Nations' highest award for environmental protection, and the \"Land Life Award\", the highest honor in the field of combating desertification. Estimating NPP of Saihanba mechanized forest farm can provide scientific reference for carbon budget prediction of forest ecosystem. This study simulated and analyzed NPP, latent heat flux, sensible heat flux and canopy temperature at Saihanba flux tower site by using BEPS model, and the sensitivity of the main influencing factors of NPP was analyzed. Also, the NPP of different stand types in the study area was simulated, and the influence of topographic factors on NPP was analyzed. This paper aimed at (1) obtaining the NPP of the study area and different stand types; (2) analyzing the impact of meteorological and topographic factors on NPP.