## 1 Introduction  
Forests comprise the main part of terrestrial ecosystems and play an important role in the terrestrial carbon cycle and in global climate change [1]. Gross primary production (GPP) is defined as the total amount of carbon dioxide (CO\({}_{2}\)) absorbed by plants through photosynthesis [2]. Accurate estimation of forest GPP is of great significance for evaluating the health status of ecosystems, understanding the carbon cycle mechanisms of terrestrial ecosystems, and revealing the response of terrestrial ecosystems to climate change [3, 4]. The light use efficiency (LUE) model have been widely used to estimate GPP from local to global scales because of its theoretical soundness and practical convenience [5; 6; 7]. LUE models estimate GPP by multiplying the energy absorbed by plants by the actual LUE, which converts energy to the amount of carbon fixed during photosynthesis [8]. Currently, researchers have developed more than 20 LUE models [7]. Although these models have been developed based on the LUE logic, they have different model structures; that is, different quantification methods have been used for the fraction of incident photosynthetically active radiation absorbed by vegetation canopies (FPAR), water stress scalars (Ws), and temperature stress scalars (Ts), and different methods have been used to integrate multiple environmental stress scalars [9; 10]. The usage of different quantification methods of the water stress scalar is the main reason for the performance divergence in LUE models [9; 11]. Currently, the water stress indicators commonly used to define the water stress scalar in the LUE model include (classification based on the three interfaces along the soil-plant-atmosphere continuum): (1) atmospheric drought indicators (e.g., vapor pressure deficit (VPD)), (2) soil moisture indicators (e.g., soil water content (SWC)), and (3) plant moisture indicators (e.g., evaporative fraction (EF)) [7; 12]. However, it has been reported that on a monthly scale, the above representative water stress indicators can only explain 20%, 6%, and 36% of the LUE variation when combining eight biomes [11]. Moreover, many studies have found that the performance of LUE models can be significantly degraded by a drought event in different ecosystem types due to the fact that the existing water stress indicators do not accurately indicate the complex relationship between water availability and actual LUE [9; 13; 14; 15]. These studies suggest that a reliable water limitation equation should be developed to improve the accuracy of the LUE models. The plant water stress index (PWSI), based on canopy temperature, has long been regarded as a sensitive indicator for characterizing plant water status on a regional scale [16; 17; 18; 19]. The principle of PWSI is that canopy temperature increases with stomatal closure and the transpiration rate decreases in response to water deficit in plants. PWSI can accurately detect the water status of crops, grasslands, and forests [20; 21; 22]. Meanwhile, PWSI is closely related to CO\({}_{2}\) flux in wheat and maize [23]; [PERSON] et al. [24] also found that PWSI can explain 88% of the variation in net ecosystem productivity in warm-temperate mixed plantations on sunny days. With the development of thermal infrared remote sensing technology, PWSI has been successfully applied to analyze the temporal and spatial distribution of plant water status on a scale from ecosystems (unmanned aerial vehicle platforms) to regions (satellite platforms) [25; 26; 27]. However, no study has evaluated the performance of GPP simulation using the LUE model, in which the water stress scalar is defined by the PWSI. Weather conditions have a significant impact on the performance of the LUE model. [PERSON] et al. [9] pointed out that the LUE model underestimated GPP on cloudy days, because the effect of diffuse radiation on LUE was not considered.