## 4 Discussion
### Environmental Restrictions on the SIF-Based and LUE-Based Models  
The parameters of these three models all showed fluctuations to some extent under different ECI values of which contained the information of the air temperature, APAR, and VPD. However, the CV values of \(\varepsilon/SIF_{\text{yield}}\) in the SIF model were relatively small under different ambient conditions in each independent temporal aggregation compared with those values in the SL-LUE and TL-LUE models (Figure 5; Table 3). It was also found by [3] that the GPP-SIF relationship in grassland tended to remain linear at the canopy scale with different meteorological conditions. Physiological stress caused by excessive light is known to affect the photochemical yield and fluorescence [73; 74]. For the SIF model, the values of \(\varepsilon/SIF_{\text{yield}}\) showed the greatest change with a CV of 16.7% and 22.5% at the mid-day and monthly timescales, which is possibly attributable to the dramatic changes in the slope as the ECI values change (Figure 7a,c). Moreover, at the mid-day and daily timescales, the linear relationship between \(\varepsilon\) and \(SIF_{\text{yield}}\) changed from a strong correlation (average R\({}^{2}\) value of 0.70) to a weak correlation (average R\({}^{2}\) value of 0.30) as the ECI values increased (Figure 7a,b). Therefore, the weakening of covariation between the \(\varepsilon\) and \(SIF_{\text{yield}}\) is likely the cause of the small fluctuations in the slope in the SIF model. Furthermore, fluorescence, photochemical quenching (PQ), and non-photochemical quenching (NPQ) are mutually exclusive and competing pathways. Changes in the efficiency of one affects the efficiency of the other two pathways [75]. The stress-induced closing down of photochemical traps in photosystem-II increases the fluorescence yield as the lifespan of an excited electron lengthens, while stress-induced opening up of extra NPQ pathways causes the fluorescence to decrease [76]. Nevertheless, both the shutdown of photosynthetic reaction centers and the opening of NPQ pathways cause the photochemical yield to drop. The influences of an increasing NPQ on \(\varepsilon\) and \(SIF_{\text{yield}}\) are not necessarily proportional [3]. That is, the nonlinear effects of NPQ on \(\varepsilon\) and \(SIF_{\text{yield}}\) are complex, which may also be a reason for the distinct response of these models to the different meteorological conditions.  
Additionally, sunlit leaves receive both direct and diffuse radiation and should have higher temperatures than shaded leaves, resulting in greater temperature gradients between the surrounding air and sunlit leaves. A former study has suggested that the different temperatures of sunlit and shaded leaves should be considered in the parameter optimization procedure in the TL-LUE model [31]. Moreover, it is also noted that the differences in leaf temperature can also cause disparities in the VPD of sunlit and shaded leaves. In the LUE-based models, the application of the same VPD and air temperature scalars to the whole canopy may also be a source of uncertainty in the optimized parameters and estimated GPP [30]. Therefore, although we partitioned the efficiency (\(\varepsilon\)) term of the LUE-based models into fixed parts (\(\varepsilon_{\text{max}}\), \(\varepsilon_{\text{msu}}\), and \(\varepsilon_{\text{msh}}\)) that depict the optimal efficiency and variable components that represent the effects of light, temperature, and water stress (e.g., VPD), the sensitivity of the LUE models to the ECI may be the main cause of the larger fluctuations in the parameter values.