## 3 Results
### Accuracy Evaluation of Original Model  
The performance of the original models was evaluated using data from June-September 2020 and May-June 2021. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, with the RMSE and IA ranging from 1.4302 to 3.3002 g C m\({}^{-2}\) day\({}^{-1}\) and 0.4702 to 0.7871, respectively. Further, all models only explained 23-52% of the daily GPP variation in the cork oak plantation (Figure 6). Among the seven models, the GLO-PEM performed relatively well, with an R\({}^{2}\) of 0.5192, RMSE of 1.4302 g C m\({}^{-2}\) day\({}^{-1}\), and IA of 0.7577 (Figures 6k and A1, Appendix A). The MOD17 and MODTEM models had relatively high explanatory powers for daily GPP variations, followed by the GLO-PEM. However, when the environmental stress was severe (GPP values were low), some simulated values of the MOD17 and MODTEM models were smaller than the measured values, resulting in scattered points in the figure. Compared with the MOD17 model, the performance of the MODTEM model was better (Figures 6a,c and A1). This is because the actual LUE in the MOD17 model was only regulated by the water stress scalar, whereas it was regulated by both water and temperature stress scalars in the MODTEM model. The difference between the EC-LUE and ECM-LUE models was in the integration method of the environmental stress factors; the former adopts the minimum  
Figure 5: The ratio of gross primary production (GPP) that was simulated by considering only water (\(\mathrm{GPP_{wat}}\), (**a**)) or temperature (\(\mathrm{GPP_{tem}}\), (**b**)) stress scalars in light use efficiency (LUE) models to estimate potential GPP (PGPP). method based on Liebig's law, and the latter adopts the multiplication method. However, among the seven models, these two models were weaker in explaining daily GPP variations, reaching 23% and 34%, respectively, and the points in the figure were scattered. The simulated values of the EC-LUE and ECM-LUE models were higher than the measured values, reaching 36% and 14%, respectively. Particularly under severe stress conditions, the degree of deviation was significant, resulting in poor accuracy. The performance of the ECM-LUE model was better than that of the EC-LUE model (Figures 6e,g and A1). The accuracy of the SM-LUE model followed that of the MODTEM model; however, the explanatory power for daily GPP variations was only higher than that of the EC-LUE and ECM-LUE models (Figures 6i and A1). The explanatory power of the Wang model for daily GPP variations was relatively high, but the model accuracy was the lowest. The estimated GPP value was 70% lower than the measured value, which was related to the existence of many environmental constraints in the model (Figures 6m and A1). Figure 6: Comparison of daily gross primary production (GPP) simulated by MOD17 **(a,b)**, MODTEM **(c,d)**, EC-LUE **(e,f)**, ECM-LUE **(g,h)**, SM-LUE **(i,j)**, GLO-PEM **(k,l)**, and Wang **(m,n)** models versus observed GPP on sunny days and cloudy days. Solid dots represent cloudy days, and the open circle dots represent sunny days. Data from June–September 2020 and May–June 2021. The red line is the fitting curve. On cloudy days, except for the SM-LUE and Wang models, the performance of the other models for daily GPP simulations was relatively good. All models explained 16-52% of the daily GPP variation, with the RMSE and IA ranging from 1.1284 to 2.7853 g C m\({}^{-2}\) day\({}^{-1}\) and 0.4564 to 0.8292, respectively (Figure 6). The MOD17 and MODTEM models provide a high explanatory power for daily GPP variations. However, the simulated values of the two models were higher than the measured values, reaching 28% and 21%, respectively, and the performance of the MODTEM model was superior to that of the MOD17 model (Figures 6b,d and A1). The EC-LUE, ECM-LUE, and GLO-PEM models performed relatively well, and the accuracy of the three models was relatively high compared to the other models.