13 citations as recorded by crossref.

1. HESS Opinions: Towards a common vision for the future of hydrological observatories P. Nasta et al. https://doi.org/10.5194/hess-29-465-2025
2. A Modeling Approach to Stomatal Conductance under Different Vapor Pressure Conditions N. Martínez-Jeraldo et al. https://doi.org/10.1007/s11538-026-01676-6
3. Reconstruction of the dynamics of sap-flow timeseries of a beech forest using a machine learning approach J. Kabala et al. https://doi.org/10.1016/j.agrformet.2024.110379
4. Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions B. Liu et al. https://doi.org/10.1016/j.agwat.2025.109634
5. Improving latent heat flux prediction via enhanced cross-variable interaction and temporal dependence learning Q. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135388
6. Environmental Factors Driving the Transpiration of a Betula platyphylla Sukaczev Forest in a Semi-arid Region in North China during Different Hydrological Years Y. Wu et al. https://doi.org/10.3390/f13101729
7. Enhancing streamflow estimation by integrating a data-driven evapotranspiration submodel into process-based hydrological models X. Lian et al. https://doi.org/10.1016/j.jhydrol.2023.129603
8. Environmental drivers and machine-learning analysis of grapevine sap flow on hilly slopes in Southwest China M. Tian et al. https://doi.org/10.1016/j.agwat.2026.110743
9. Revealing seasonal plasticity of whole-plant hydraulic properties using sap-flow and stem water-potential monitoring Z. Zhang et al. https://doi.org/10.5194/hess-29-3975-2025
10. Hybrid residual deep learning models with physical knowledge for improving plant transpiration estimation B. Liu et al. https://doi.org/10.1016/j.compag.2023.108135
11. Estimating the transpiration of kiwifruit using an optimized canopy resistance model based on the synthesis of sunlit and shaded leaves Z. Li et al. https://doi.org/10.1016/j.agwat.2024.109193
12. Multi-factor Fire Susceptibility Index (FSI) in Hungary: a data-driven approach using remotely sensed data for peri-urban and forested landscapes A. Agustiyara et al. https://doi.org/10.1007/s40808-026-02785-w
13. Annual tree growth and transpiration records may not indicate drought avoidance: Working hypotheses to manage forest drought stress in mixed species stands J. Knighton & R. Fahey https://doi.org/10.1016/j.agrformet.2026.111251