ArticleFrontiers in plant science2026
Robust retrieval of biophysical traits under shade gradients in young
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Introduction: Shading regulation is crucial for cultivating and restoring endangered Methods: We developed an inversion framework that integrates multiple spectral scale-normalization methods with a hybrid RTM-deep learning modeling approach under controlled shading gradients. This framework was designed to improve cross-domain spectral consistency under shading-induced variability and to introduce a new carbon-based constituent index, DLD Results: Inter-group shading differences in original reflectance were easily obscured by noise, whereas scale normalization substantially enhanced the shading response in the green peak and red-edge regions and better-preserved physical consistency between RTM-simulated and field-measured data. The hybrid modeling achieved the highest accuracy for chlorophyll (R Discussion: This study provides a practical VNIR-based pathway for nondestructive and robust retrieval of key biophysical traits of
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