## 1 Introduction  
The total atmospheric carbon dioxide (CO\({}_{2}\)) assimilated by vegetation is known as gross primary productivity (GPP) and is generally considered the most significant carbon flux in the carbon cycle of terrestrial ecosystems, which plays an important role in global carbon and the climate system [1, 2]. Especially in the background of rapid global climate change, accurate estimation of GPP is particularly essential and urgent [3]. The flux observation network based on the eddy covariance technique supplies lots of observational data (such as FLUXNET, Ameriflux, and Chinaflux), which have been used by a large number of studies as standard ground data [4; 5; 6]. However, due to the finite number and non-uniform distribution of sites, it is still unable to meet the needs of vegetation-productivity monitoring and evaluation at the larger scales [7]. In recent years, many studies have developed multifarious models including meteorological-based models [8; 9], process-based models [10; 11; 12; 13], light use efficiency (LUE) models [3; 14; 15; 16; 17; 18; 19; 20], and data-driven models [21; 22; 23; 24] to quantify GPP at different scales based on remote sensing data. Among these models, the LUE model has wide utilization due to its high accuracy, simple form and easy access to the input data [3; 25; 26]. However, the interannual variability of GPP estimated by the LUE models still deviates considerably from in situ observations [26; 27]. [PERSON] et al. [28] suggested that one of the main reasons for the uncertainty in the GPP estimation is that the effects of environmental factors on photosynthesis are not fully incorporated into the LUE model. Typically, GPP can be calculated using the LUE model as described in Equation (1):  
\[GPP=\varepsilon_{max}\times APAR\times f(stress) \tag{1}\]  
where the \(\varepsilon_{max}\) and APAR are the maximum LUE and the absorbed photosynthetically active radiation, respectively. The \(f\) (_stress_) represents the environmental factor used to adjust the \(\varepsilon_{max}\). Among all environmental factors, temperature has an important influence on enzyme activity and electron transport rate, and a sufficient water supply ensures stomatal openness and physiochemical reactions in plants, which are the most common environmental stress factors in the LUE models [25; 29]. However, most LUE models set the temperature parameter as a constant for each vegetation type, which ignores the spatial differences in the influence of temperature [30; 31; 32]. In recent years, several studies have proposed methods to calculate optimal temperatures [30; 33; 34], and [PERSON] et al. [34] have published the first global map of optimal ecosystem temperatures (\(T_{opt}^{co}\)), which may help to reduce the large uncertainty in GPP estimates due to temperature parameters. In situ GPP allows the estimation of site optimum temperatures, while at the regional scale, satellite-retrieved solar-induced chlorophyll fluorescence (SIF) capable of observing vegetation photosynthesis may be a better option than the traditional vegetation indices used in previous studies. Moreover, atmospheric CO\({}_{2}\) can diffuse through stomata into leaves and is a crucial driver of photosynthesis. Since the 1980s, global atmospheric CO\({}_{2}\) concentrations have increased by approximately 23%, contributing to increased photosynthesis and GPP. Although many studies have recognized the importance of the CO\({}_{2}\) fertilization effect, it has not been integrated into most LUE models, except for CFix [35], P-model [36], revised EC-LUE [26], PRELES model [37], and the Dry Matter Productivity (DMP) of the Copernicus global land service, which could result in the insensitively of the GPP estimates to the increasing CO\({}_{2}\) concentrations. In addition, sunlit leaves are more likely to reach light saturation due to the simultaneous reception of direct and diffuse radiation [3; 14; 38]. So far, the impact of the radiation component on LUE has been agreed in many studies [3; 14; 19; 26; 39].