## 3 Results and Discussion
### Effect of LAI on Stomatal Conductance Prediction Results  
As some of the sites showed slight underfitting, especially BR-Sa3 (Figure 3--Benchmark), this suggests that the model may be relatively simple and fail to effectively learn and capture the complexity and key patterns in the training data. Therefore, more meaningful features need to be added to optimize the model [56; 65]. Differences in the main controller of \(G_{\text{s}}\) among different growing stages of the same vegetation type demonstrate the different physiological characteristics of plants (Table 2). Studies have shown that stomatal development has a crucial effect on \(G_{\text{s}}\)[66], so \(G_{\text{s}}\) is highly correlated with the plant growth process. Hence, the importance of the feature varies considerably at different stages of vegetation (Table 2). The classification of vegetation phenological periods is challenging due to variations among plants and the impact of climate change [67]. However, LAI can serve as an indicator of leaf phenology [50]. Moreover, some model studies have attempted to assess the \(G_{\text{s}}\)-LAI relationships [68; 69]. Therefore, we added LAI to the data samples on a daily scale to see their effect on the accuracy of the results. Since data on the partitioning of ET based on EC differ, the range of values of \(G_{\text{s}}\) inversion results obtained from each method is not consistent (generally TEA > uWUE > Perez-Priego), which can affect the RMSE results. The RMSE was only used to compare the differences in the results within the methods. The incorporation of LAI led to enhanced prediction accuracy, as evidenced by the average \(R^{2}\) score on training and test data increasing from 0.796 (0.663) to 0.816 (0.799) (Figure 3). Also, LAI consistently ranked highest regarding feature importance (Appendix B, Figure A1). The enhanced accuracy of models incorporating LAI underscores the pivotal role of plant physiological parameters in \(G_{\text{s}}\) modeling. This enhancement corroborates the findings of [PERSON] and [PERSON] [39], who demonstrated that models integrating physiological predictors (e.g., plant photosynthesis rates) with environmental variables significantly outperformed those that did not. Meanwhile, based on the \(R^{2}\) results, the Perez-Priego method exhibited superior performance compared to the other two methods in various biomes, except for evergreen broadleaf forests BR-Sa3. This discrepancy could be attributed to the relatively insignificant variation in LAI across EBF vegetation types or potentially due to the adverse effects of climate impacts on the quality of LAI data [70]. Furthermore, the persistent slight underfitting at certain sites may suggest inherent limitations in the applicability of RF, which requires further in-depth study. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Period** & **Method** & **SWC1** & **SWC2** & **VPD** & **TA** & **RH** & **WS** & **CO2** & **Rn** & **Rg** \\ \hline \multirow{3}{*}{Growing} & uWUE & 0.8116 & 1.1922 & 1.0090 & 1.1558 & 1.2629 & 0.8127 & 0.4957 & 1.1381 & 0.9938 \\  & Perez-Priego & 0.9903 & 0.8785 & 0.8077 & 1.2641 & 0.5956 & 0.1992 & 0.4172 & 1.0697 & 0.8140 \\  & TEA & 1.1261 & 1.0491 & 1.2467 & 1.1335 & 0.9460 & 0.8015 & 0.5262 & 1.4220 & 1.0293 \\ \hline \multirow{3}{*}{Overwintering} & uWUE & 0.1521 & 0.2328 & 0.3372 & 0.2324 & 0.2226 & 0.6757 & 0.4982 & \(-\)0.0184 & \(-\)0.0024 \\  & Perez-Priego & 0.3937 & 0.2208 & 0.5302 & 0.5693 & 0.1855 & \(-\)0.0222 & 0.1035 & 0.0186 & 0.1759 \\ \cline{1-1}  & TEA & 0.2525 & 0.3437 & 0.5123 & 0.5632 & 0.3119 & 0.7706 & 0.3355 & 0.2498 & 0.4221 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature importance of winter wheat growth period and overwintering period.