## 1 Introduction  
Forests are important terrestrial ecosystems, and their primary productivity has become a hot topic in the study of global climate change [1]. Forest ecosystem regulates the carbon balance among land, atmosphere and biosphere by fixing or releasing carbon dioxide through physical, chemical and biological processes [2], accounting for 45-60% of the carbon reserves of terrestrial ecosystem [3]. Forests provide basic organic compounds for ecosystems through photosynthesis [4]. Plants assimilate carbon dioxide in the atmosphere and absorb it into biomass through photosynthesis. Part of the assimilated carbon is discharged into the atmosphere through plant respiration (autotrophic respiration) [5]. As an important datum to quantify the carbon sink capacity of vegetation [6], net primary productivity (NPP) is defined as the difference between the cumulative photosynthesis and cumulative autotrophic respiration of green plants in unit time and space [7]; that is, the increase of vegetation biomass after autotrophic respiration is considered. The accurate simulation of NPP is very important for understanding the carbon dynamics in atmosphere, vegetation and soil and the response of forest ecosystem to future climate change [8, 9]. Given its central role in the global carbon budget and the growing need to understand the role of forest ecosystem in the global carbon cycle, it is necessary to clearly understand the spatio-temporal pattern of NPP [10, 11]. It also provides information needed for ecosystem monitoring, simulation and management of natural resources to achieve sustainable development [12, 13, 14]. Therefore, the quantification of NPP is becoming one of the main topics of global climate change research [15]. The simulation of forest NPP can be realized by carbon cycle models [16]. Forest ecosystem carbon cycle models can be divided into patch-scale models and regional-scalemodels from the spatial scale. The patch-scale models are based on single tree or stand, and usually use the growth, death and regeneration of trees in a certain gap area to simulate and predict the change process of the forest land, so as to realize the dynamic simulation of the whole stand. Regional-scale models include biogeographic models, land biophysical models and biogeochemical models. The biogeographic models compare the ecosystem to the correlation function of climate and soil, which are mainly used to predict the spatial distribution structure and dominance of different vegetation under different environmental conditions. They are suitable for studying the distribution of global vegetation on a large scale. Representative models include Holdridge model [17], DOLY model [18] and MAPPS model [19]. The land biophysical models are two-way coupling models of vegetation and climate [20; 21], which simulate the reflection, absorption, scattering, transmission and other processes of vegetation canopy on different wavelength spectra and canopy evapotranspiration under different conditions [22], emphasizing the interaction between climate and vegetation. Representative models include IBIS model [23] and CASA model [24]. Biogeochemical models can simulate nutrient cycling among vegetation, ground litter and soil organic matter; calculate NPP, carbon budget and nutrient utilization of vegetation; and predict the impact of carbon cycle on climate change. Representative models include CENTURY model [25] and BEPS model [14]. BEPS model is an ecosystem remote sensing coupling model based on the forest biogeochemical cycles model (forest BGC), which involves biochemical, vegetation physiological and physical mechanisms. The model uses the radiative transfer theory and photosynthesis module to simulate the photosynthesis process of sunny and shady leaves. At the same time, the Farquhar model [26] is used for conversion, which solves the compatibility problem of data sources with different spatial-temporal resolutions.