Abstract: Climate-change-driven drought intensification increasingly threatens forest ecosystems, highlighting an urgent need for accurate monitoring of forest water stress. Leaf water potential (Ψ_leaf) is a key integrative indicator, yet conventional measurements are destructive and unsuitable for large-scale or high-frequency monitoring. Hyperspectral remote sensing offers a promising alternative, but robust canopy-level Ψ_leaf estimation remains constrained by limited labeled data and heterogeneous environmental conditions. Here, we develop a cross-scale framework integrating supervised contrastive learning with deep transfer learning to translate robust leaf-scale pretraining into canopy-scale Ψ_leaf estimation from hyperspectral data in a Populus × euramericana ‘I-214’ plantation. Hyperspectral imagery was captured at the leaf scale under controlled laboratory conditions (n = 229) and at the canopy scale using a UAV-based platform (n = 200), together with paired Ψ_leaf measurements. Reflectance consistently increased with declining Ψ_leaf at both scales, supporting the feasibility of cross-scale modeling. At the leaf scale, physics-consistent spectral augmentation coupled with contrastive learning enhanced feature discrimination and predictive stability under small-sample conditions (R² = 0.8030). Transfer learning via progressive fine-tuning enabled efficient scaling of the leaf-level pretrained model to canopy-level prediction despite structural and environmental complexity and restricted field data ranges, achieving R² = 0.7605 and RMSE = 0.1056 MPa. Coupling with individual-tree crown segmentation further enabled spatially explicit mapping of canopy Ψ_leaf and plot-level forest water stress dynamics. These results demonstrate that combining contrastive representation learning with cross-scale transfer provides a practical pathway for physiological monitoring and scalable, climate-smart forest phenotyping in data-constrained forested environments.

Key words: cross-scale modeling, deep learning, forest drought monitoring, hyperspectral remote sensing, supervised contrastive learning, transfer learning