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

文献类型：期刊论文

作者Gu, Chenpeng6,7,8,9; Li, Jing6,8,9; Liu, Qinhuo6,7,8,9; Zhang, Hu8,9; Huete, Alfredo5; Fang, Hongliang4; Liu, Liangyun2,3,6; Mumtaz, Faisal6,8,9; Lin, Shangrong(); Wang, Xiaohan6,8,9
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2025-05-15
卷号322页码:114692
关键词Leaf chlorophyll content Leaf-scale chlorophyll index Canopy structure Spectral invariants Recollision probability
ISSN号0034-4257
DOI10.1016/j.rse.2025.114692
产权排序6
文献子类Article
英文摘要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 (CIleaf) from the canopy bidirectional reflectance factor (BRF) based on the spectral invariant theory (p-theory). Six widely used canopy-scale chlorophyll indices (CIcanopy) were selected to derive the corresponding CIleaf. The CIleaf is expressed as the product of its original CIcanopy and a scale conversion factor (SCF) (CIleaf = CIcanopy x 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 CIcanopy. The analysis through radiative transfer model simulations showed that CIleaf exhibited more unified relationships with LCC across LAI conditions than the original CIcanopy and substantially eliminated the influence of LAI on the CI-based model. Validation results demonstrated that CIleaf improved the accuracy of LCC estimation compared to CIcanopy. The leaf-scale MERIS terrestrial chlorophyll index (MTCIleaf) exhibited the most prominent improvements, reducing the root-mean-square error (RMSE) by 6.68 mu g/cm2 for ground spectra and 2.33-4.21 mu g/cm2 for Sentinel-2 images with multi-ecosystem datasets. Additionally, the influence of vegetation types on the CI-based model was mitigated by CIleaf. MTCIleaf reduced the RMSE values by 3.8 %-34.0 % for different plant functional types, giving more consistent accuracies across species than MTCIcanopy. Our results show that the proposed CIleaf combines the robustness of the physically-based method with the simplicity of the CI-based method, thus providing a practical approach for large-scale high-resolution LCC mapping. Moreover, the method holds promise for designing leaf-scale vegetation indices sensitive to various leaf biochemical parameters beyond LCC, extending its utility to broader leaf-scale remote sensing retrieval (e.g., leaf carotenoid content and leaf dry mass).
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WOS关键词PHOTON RECOLLISION PROBABILITY ; AREA INDEX ; RETRIEVAL ; PHOTOSYNTHESIS ; FLUORESCENCE ; VARIABLES ; DYNAMICS
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001442817000001
出版者ELSEVIER SCIENCE INC
源URL[http://ir.igsnrr.ac.cn/handle/311030/213265]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Li, Jing; Liu, Qinhuo
作者单位1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China;
3.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth, Beijing 100094, Peoples R China;
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Beijing 100101, Peoples R China;
5.Univ Technol Sydney, Sch Life Sci, Broadway, NSW, Australia;
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
7.Chinese Acad Sci, Natl Engn Res Ctr Satellite Remote Sensing Applica, Beijing 100101, Peoples R China;
8.Beijing Normal Univ, Beijing 100101, Peoples R China;
9.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Gu, Chenpeng,Li, Jing,Liu, Qinhuo,et al. Deriving leaf-scale chlorophyll index (CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory[J]. REMOTE SENSING OF ENVIRONMENT,2025,322:114692.
APA Gu, Chenpeng.,Li, Jing.,Liu, Qinhuo.,Zhang, Hu.,Huete, Alfredo.,...&Guan, Li.(2025).Deriving leaf-scale chlorophyll index (CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory.REMOTE SENSING OF ENVIRONMENT,322,114692.
MLA Gu, Chenpeng,et al."Deriving leaf-scale chlorophyll index (CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory".REMOTE SENSING OF ENVIRONMENT 322(2025):114692.

入库方式： OAI收割

来源：地理科学与资源研究所

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