Interpreting Controls of Stomatal Conductance across Different Vegetation Types via Machine Learning  
[PERSON]  
1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.S.) [PERSON]  
1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.S.) [PERSON]  
1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.S.) [PERSON]  
1 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; [EMAIL_ADDRESS] (R.X. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS] (Q.S.) [PERSON]  
5 National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; [EMAIL_ADDRESS] (Q.H. ); [EMAIL_ADDRESS]. requires canopy transpiration (\(T\)) rate simulations and stand-scale water consumption or \(T\) calculations [12; 13]. \(T\) can be partitioned from ecosystem-scale evapotranspiration (ET) by several methods, including leaf gas exchange, plant-level sap flow, lysimeters, soil, photometers, soil heat pulse methods, and stable and radioisotopic techniques [14; 15; 16]. Among them, [PERSON] et al. [17] used three widely applicable methods, underlying water use efficiency (uWUE) [18], Perez-Priego [19], and the Transpiration Estimation Algorithm (TEA) [20], to partition the eddy covariance (EC)-based ET into evaporation (\(E\)) and \(T\) for 251 sites in FLUXNET [21]. All three methods are based on coupled water-carbon relationships, but their assumptions and parameterization methods differ. The uWUE method [22; 23] assumes an ecosystem has an actual and potential underlying water use efficiency and uses optimality assumptions to calculate \(T/\)ET. The Perez-Priego method [19] circumvents the assumption that \(T/\)ET approaches unity at some periods by estimating ecosystem conductance directly. The TEA method from [PERSON] et al. [20] utilizes a non-parametric model and thereby limits assumptions on how the ecosystem functions [14]. Compared to other conventional models, e.g., the Shuttleworth-Wallace-Hu [24] model and the Penman-Monteith [25] model, these three models reduce dependency on parameters and offer higher accuracy and adaptability in partitioning evapotranspiration. Estimates of ecosystem \(T\) based on EC data offer a data-driven perspective on the role of plant water use in global water and carbon cycles. Studies on \({}^{13}\)C leaf measurements suggest that stomatal behavior varies across biomes [26]. In addition to the vegetation's characteristics (e.g., stomatal characteristics, type, growth, and development), environmental factors significantly impact plant stomata. It has been found that environmental elements such as temperature, light, and moisture have a significant effect on the \(G_{\rm s}\) of plants [27; 28; 29; 30; 31]. Some progress has been made in the study of \(G_{\rm s}\) in different biomes, and the effects of environmental factors on \(G_{\rm s}\) have received widespread attention. However, there is a gap in research on the combined effects of different biomes and environmental factors on \(G_{\rm s}\). Machine learning (ML) frameworks have recently been acknowledged as powerful nonparametric tools for prediction across various scientific and engineering fields [32; 33; 34].