Deriving leaf-scale chlorophyll index (CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory

作者：Chenpeng Gu, Jing Li, Qinhuo Liu, Hu Zhang, Alfredo Huete, Hongliang Fang, Liangyun Liu, Faisal Mumtaz, Shangrong Lin, Xiaohan Wang, Yadong Dong, Jing Zhao, Junhua Bai, Wentao Yu, Chang Liu, Li Guan · 发表于：Remote Sensing of Environment · 年份：2025 · DOI：10.1016/j.rse.2025.114692 · 被引用次数：6 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Remote Sensing and Land Use

Leaf chlorophyll content (LCC) is a crucial biochemical parameter for monitoring the plant's nutritional status and photosynthetic capacity . However, retrieving LCC from canopy reflectance is challenging due to the coupling influence of LCC and canopy structure, particularly leaf area index (LAI). The isolation of leaf-scale information from canopy signals is therefore essential to improve the LCC estimation. This study proposed an approach for deriving the leaf-scale chlorophyll index (CI leaf ) from the canopy bidirectional reflectance factor (BRF) based on the spectral invariant theory ( p -theory). Six widely used canopy-scale chlorophyll indices (CI canopy ) were selected to derive the corresponding CI leaf . The CI leaf is expressed as the product of its original CI canopy and a scale conversion factor (SCF) (CI leaf = CI canopy × SCF). The SCF is determined by two spectral invariants of p -theory (recollision probability p and directional area scattering factor DASF), as well as canopy BRFs at specific wavelengths, and it corrects for the contribution of canopy multiple scattering to CI canopy . The analysis through radiative transfer model simulations showed that CI leaf exhibited more unified relationships with LCC across LAI conditions than the original CI canopy and substantially eliminated the influence of LAI on the CI-based model. Validation results demonstrated that CI leaf improved the accuracy of LCC estimation compared to CI canopy . The leaf-scale MERIS te...