## 1 Introduction  
Forests are an important part of terrestrial ecosystems and play an important role in the global carbon cycle [1, 2]. The forest ecosystem is the main carbon pool on land, and its carbon content accounts for more than half of the entire terrestrial ecosystem [3]. However, due to the influence of climate change, human activities, and the natural growth and degradation of forest vegetation, it shows a dynamic change law [4, 5] and leads to uncertainties in the simulation of carbon fluxes in different regions and forest types, so it is always a challenge to model forest carbon fluxes accurately [6, 7]. Vegetation productivity is an important indicator for evaluating carbon flux including gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem exchange (NEE) [8, 9]. The current methods for quantitative estimation of forest ecosystem productivity can be summarized as: inventory method, eddy correlation method, and model method. The forest inventory method usually estimates the carbon storage in the forest by measuring the change in biomass, and then obtains the net primary productivity through the conversion factor [10]. The advantage of the inventory method is to directly measure the carbon storage of vegetation and soil at the sample scale, but it has a long inventory cycle and suffers great uncertainty from sample to area. The eddy covariance method has been proven to be accurate in detecting changes in ecosystem carbon fluxes on fine time scales [11; 12], but since the sites are scarce and costly that makes it difficult to extend to regional-scale applications. With the development of remote sensing technology, people can take advantage of the macroscopic and dynamic characterizations of remote sensing and its unique application potential in large-scale surveys that are spatially continuous. However, remote sensing can only obtain instantaneous observation data and cannot provide continuous temporal characterization; moreover, pure remote sensing information is also difficult for reflecting the interaction between the forest ecosystem and the outside world [13; 14]. For vegetation productivity simulation, current widely used models include two types: light use efficiency (LUE) models and ecological process models. The LUE model based on remote sensing can obtain regional-scale and time-continuous information. Representative models include CASA [15], GLO-PEM [16], MODIS MOD17 model [17], C-Fix [18], etc. However, such models cannot reflect the internal mechanism of the forest ecosystem or the interaction between the forest ecosystem and the surrounding environment [13]. The ecological process model can describe most of the physiological and ecological processes of vegetation on fine time scales [19], such as photosynthesis, respiration and transpiration. Representative models include TEM [20], BEPS [21], Biome-BGC [22], etc. However, the ecological process models require a large amount of detailed ground information as input data, and a series of related initialization states and vegetation physiological ecology; these will result in great uncertainties especially in the regional application. Many scholars in previous studies have used the incorporation of LUE model and ecological process model to reduce the uncertainty of simulations of carbon fluxes. [PERSON] et al. [23] proposed a method for parameter calibration of the process model based on the incorporation of the remote sensing model (C-Fix)-process model (Biome-BGC); the method suppressed the uncertainty of the simulation results, but the mothed was only applied to a single ecological gradient in the Mediterranean region and its generalizability remains unknown. The optimized MOD_17 model was used to couple the process model Biome-BGC in the region, and the simulation results were demonstrated more accurately and reliably [24; 25; 26], but this method was only suitable for single dominant tree species (_Picea crassifolia_) in this region. [PERSON] et al.