## 4 Conclusions  
This study examined the impact of biophysical factors on \(G_{\rm s}\) across various vegetation types. We observed that the influence of biophysical conditions on \(G_{\rm s}\) varies across different  
Figure 3: Model accuracy of before (Benchmark) and after adding LAI (LAI). The darker colors in the panels correspond to higher values. (**a**) presents R\({}^{2}\) results for the training set, while (**b**) displays R\({}^{2}\) results for the test set. (**c**) shows RMSE outcomes for the training set, and (**d**) outlines RMSE results for the test set.  
biomes. However, air temperature, relative air humidity, soil water content, and net radiation consistently emerge as significant factors in most cases. In contrast, wind speed consistently exhibits the least influence. Moreover, the effects of environmental factors differ during different stages of vegetation growth. Including LAI enhances the predictive accuracy of \(G_{\rm s}\), with LAI emerging as the most influential feature. Additionally, when estimating \(T\), the Perez-Priego method generally demonstrates a higher upper limit of prediction accuracy than the other two methods.  
There remained some limitations of our work. Firstly, the importance of homogeneous vegetation characteristics among different sites under the same climatic conditions must be further verified. In this study, only US-AR1 and US-AR2 sites, both belonging to Dsa, were selected, and the effects of environmental factors on \(G_{\rm s}\) under homogeneous PFT showed partial consistency but not complete consistency, with some randomness. Meanwhile, even if the climatic conditions are the same, factors such as soil type, water source, production management practices, different species of vegetation, and ecotypes between sites may affect vegetation growth and development, leading to variations in \(G_{\rm s}\) results. Therefore, further research needs to be extended to determine the extent of the influence of these factors. Secondly, while our inclusion of LAI aimed to capture phenological effects on \(G_{\rm s}\), we acknowledge that the improved model performance may also be attributed to photosynthetic changes preceding LAI variations, indicating the need for further research to disentangle these intertwined mechanisms.  
In conclusion, \(G_{\rm s}\) is a critical variable in numerous terrestrial ecohydrology and biophysical models of land surfaces. Our research provides essential guidance on selecting factors for \(G_{\rm s}\) modeling. In practical modeling and applications, one can optimize the selection of feature combinations based on data availability, vegetation type, and climate conditions. This approach enhances the accuracy and efficiency of models, enabling tailored strategies that align with specific environmental scenarios, thereby improving predictions and the management of water and carbon dynamics across diverse ecosystems.  
Conceptualization, R.X. and X.Z.; Data curation, R.X. and W.Z.; Formal analysis, R.X. and J.Q.; Funding acquisition, X.Z. and Y.Z. ([PERSON]); Investigation, R.X. and Q.H.; Methodology, R.X. and W.Z.; Project administration, W.C.; Resources, X.Z. and J.Q.; Software, R.X. and Z.Z.; Supervision, Y.Z. ([PERSON]), J.Q. and S.W.; Validation, Q.H.; Visualization, Z.Z. and J.S.; Writing--original draft, R.X. and W.Z.; Writing--review and editing, X.Z. and J.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grants from the National Key R&D Program of China (No. 2022 YFD2001004-1).  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.