## 3 Results
### Influence of Model Structure on Model Performance  
The influence of a single environmental constraint (\(T_{\rm s}\) or \(W_{\rm s}\)) and the combination of different environmental constraints on the performance of the six big-leaf _LUE-GPP_ models was explored. From Figure 3, the models that only considered \(T_{\rm s}\) (\(R^{2}\) = 0.672-0.743) or \(W_{\rm s}\) (\(R^{2}\) = 0.656-0.744) showed better performance than those without any environmental constraints (\(R^{2}\) = 0.651). Among the \(T_{\rm s}\) quantification methods, that calculated by the EF-LUE model (\(T_{\rm s\text{-EF}}\)) could better quantify the influence of \(T_{\rm s}\) on \(GPP\) estimation (\(R^{2}\) = 0.742 \(\pm\) 0.06), followed by those by the CASA (\(R^{2}\) = 0.678 \(\pm\) 0.06) and MODIS models (\(R^{2}\) = 0.673 \(\pm\) 0.08). Among the \(W_{\rm s}\) quantification methods, that calculated by the EF-LUE (\(W_{\rm s\text{-EF}}\)) model better simulated the effect of \(W_{\rm s}\) on \(GPP\) estimation (\(R^{2}\) = 0.742 \(\pm\) 0.06), followed by those simulated by the TEC (\(R^{2}\) = 0.708 \(\pm\) 0.06), CASA (\(R^{2}\) = 0.704 \(\pm\) 0.07), VPM (\(R^{2}\) = 0.702 \(\pm\) 0.08), MVPM (\(R^{2}\) = 0.676 \(\pm\) 0.09), and MODIS (\(R^{2}\) = 0.655 \(\pm\) 0.06) models. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Year** & \(R^{2}\) & _MAE_ **(g C m\({}^{-2}\) d\({}^{-1}\))** & _RMSE_ **(g C m\({}^{-2}\) d\({}^{-1}\))** & _\(\eta\)_ & _d\({}_{1}\)_ & **GPI** \\ \hline
2018 & 0.786 & 1.395 & 1.844 & 0.558 & 0.755 & 0.601 \\
2019 & 0.733 & 1.329 & 1.751 & 0.537 & 0.758 & 1.185 \\
2020 & 0.621 & 1.259 & 1.725 & 0.440 & 0.714 & 1.293 \\ \hline \end{tabular}
\end{table} 
Table 6: The compared performances of simulated gross primary productivity (GPP) by two-leaf light use efficiency-based GPP models against the GPP derived from the EC measurements by determination coefficients (\(R^{2}\)), mean absolute error (_MAE_), root mean squared errors (_RMSE_), fixed efficiency index (\(\eta\)), fixed sum index (\(d_{1}\)), and global performance indicator (_GPI_) during 2018–2020 in a humid region orchard ecosystem. Figure 2: Seasonal variations (**a**) and the relationship (**b**) between the gross primary productivity derived from EC measurement (\(GPP_{\rm EC}\)) and estimated values by the two-leaf light use efficiency-based models (\(GPP_{\rm TL}\)). Because \(W_{\text{s-EF}}\) could better simulate the effect of moisture constraints on \(GPP\), the model performances of different combinations of \(T_{\text{s}}\) and \(W_{\text{s-EF}}\) were superior to those of other combinations in simulating \(GPP\) (Figure 4). Among the combinations of joint constraints of \(T_{\text{s}}\) and \(W_{\text{s}}\) (\(T_{\text{s}}\times W_{\text{s}}\), Figure 4a), that of \(T_{\text{s}}\) quantification by the MODIS model and \(W_{\text{s}}\) in EC-LUE model (\(W_{\text{s-EF}}\)) had the highest \(R^{2}\) (0.751), followed by those of \(T_{\text{s}}\) quantification by the EF-LUE and \(W_{\text{s-EF}}\) (\(R^{2}\) = 0.748) and \(T_{\text{s}}\) of the CASA model (\(T_{\text{s-CASA}}\)) and \(W_{\text{s-EF}}\) (\(R^{2}\) = 0.745). Among the combinations of maximum constraints of \(T_{\text{s}}\) or \(W_{\text{s}}\) (Min (\(T_{\text{s}}\), \(W_{\text{s}}\)), Figure 4b), that of \(T_{\text{s-CASA}}\) and \(W_{\text{s-EF}}\) had the highest \(R^{2}\) (0.756), followed by those of \(T_{\text{s-MODs}}\) and \(W_{\text{s-EF}}\) (\(R^{2}\) = 0.747) and \(T_{\text{s-EF}}\) and \(W_{\text{s-EF}}\) (\(R^{2}\) = 0.745). The model performances of various combinations of \(T_{\text{s-CASA}}\) and any \(W_{\text{s}}\) were generally superior to those of other combinations, indicating that, although \(T_{\text{s-CASA}}\) may not be optimal, it can be combined with other \(W_{\text{s}}\) quantification methods for estimating \(GPP\). In addition, for the same quantification method for \(T_{\text{s}}\) and \(W_{\text{s}}\), the joint constraints combination of \(T_{\text{s}}\) and \(W_{\text{s}}\) had a higher \(R^{2}\) than the other maximum restriction combinations.