## 1 Introduction  
Gross primary production (GPP), defined as the sum of photosynthetic carbon uptake by vegetation, is the first step in the input of atmospheric CO\({}_{2}\) to terrestrial ecosystems [1; 2]. An accurate estimation of GPP is essential for a better understanding of the ecosystem carbon cycle [3; 4; 5]. The eddy covariance (EC) method is an important tool for measuring land-atmosphere CO\({}_{2}\) exchange and thus the GPP of terrestrial ecosystems [6]. However, the applications of such a method are largely impeded by the limited number of flux tower sites and the limited spatial representativeness of their GPP estimates (i.e., footprint area) [7]. Models therefore constitute the primary approaches for predicting terrestrial ecosystem GPP over large areas [8; 9]. Numerous models have been developed to estimate GPP from local to global scales [10; 11]. Overall, these can be grouped into two broad types: process-based biogeochemical models and satellite-based models. The first simulates the full biogeochemical fluxes (e.g., carbon, water, and nitrogen cycles) on the basis of calculating key ecological processes, such as canopy interception and evaporation, transpiration, photosynthesis, growth and maintenance respiration, carbon allocation above and below-ground, litterfall, decomposition, and nitrogen mineralization [12]. They are usually driven by data, such as climate, soil, and land cover/use or vegetation specific parameters [13]. They have been widely used to simulate and predict carbon dynamics at different spatial scales [14]. However, such models may be subject to large errors, primarily due to (1) missing biogeochemical processes, (2) complex model structures, and (3) the uncertainties associated with the input data and parameters [15; 16]. In contrast, satellite-based models simulate GPP based on simple algorithms [13; 17]. They are mainly driven by satellite observations, which are only available historically and thus cannot predict future GPP [14; 17]. Such models require few model inputs and parameters and can effectively address the spatial and temporal dynamics of GPP over large areas [17; 18]. With advancements in remote sensing and spatial science, satellite-based models have been increasingly used to estimate GPP at various spatial scales in recent decades [9; 17; 18]. Many satellite-based GPP models have been developed and can be generally classified into two categories based on GPP algorithms: light-use efficiency (LUE) and statistical models. The LUE models were developed based on [PERSON]'s production efficiency concept [19; 20] and include the Carnegie-Ames-Stanford Approach (CASA) [21], the global production efficiency model (GLO-PEM) [22], the moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP) [23; 24], the vegetation photosynthesis model (VPM) [25; 26], and the eddy covariance-light use efficiency (EC-LUE) model [27]. The modeled GPP corresponds to the product of the incident photosynthetically active radiation (PAR), the fraction of PAR absorbed by vegetation canopy (fPAR), the potential LUE without environmental stress (\(\varepsilon_{\text{max}}\)), and the reduction in LUE relative to \(\varepsilon_{\text{max}}\) due to environmental stresses (e.g., air temperature stress and water availability) (_f_). Different \(f\) scalars are usually used by different LUE models [9]. In contrast, statistical models estimate the GPP by its statistical relationship with greenness indices and/or environmental variables, and include the temperature and freenness (TG) model [28], the greenness and radiation (GR) model [29], and the vegetation index (VI) model [8]. These models often differ according to the environmental variables used. The contrasting model structures and/or parameters result in large uncertainties in GPP estimates across the models and ecosystems [17; 18; 30; 31].