A Condition-Aware Shading Domain-Adaptive Framework for Robust Chlorophyll Inversion Across Shade Managements in...

Shade management, which is widely adopted in cultivation and understory regeneration, alters plant light environments, thereby degrading the trait inversion performance and posing a key challenge in plant phenotyping. To address this issue, this study reframed chlorophyll retrieval of Hopea hainanensis under shade management as an illumination-regime-dependent conditional domain shift problem, and developed a condition-aware domain adaptation framework (CAI-DAI) tailored to this setting. The results showed that chlorophyll content increased with shading intensity, accompanied by clear differences in canopy spectral distributions among shading levels, supporting the presence of condition-dependent variation under shade management. Model comparisons showed that CA-IE and CAI-DAI, which integrate conditional encoding and conditional alignment, performed better than the comparative models across fine-tuning ratios from 30% to 70%. Among them, CAI-DAI achieved the best and most stable performance, with test MAE ranging from 4.355 to 4.774 μg·cm^-2 and nRMSE ranging from 16.4% to 18.2%, and R² ranging from 0.456 to 0.585. Further evaluation at individual shading levels (S1-S4) showed that CAI-DAI produced narrower error ranges than CA-IE. It also showed smaller error fluctuations under most fine-tuning ratios. These results demonstrate that the proposed framework effectively improves robustness under heterogeneous shading conditions and limited labeled samples, providing methodological support for chlorophyll monitoring and decision-making related to shade management.