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A novel red‐edge spectral index for retrieving the leaf chlorophyll content

Hu Zhang, Jing Li, Qinhuo Liu, Shangrong Lin, Alfredo Huete, Liangyun Liu, Holly Croft, J.G.P.W. Clevers, Yelu Zeng, Xiaohan Wang, Chenpeng Gu, Zhaoxing Zhang, Jing Zhao, Yadong Dong, Faisal Mumtaz, Wentao Yu

2022Methods in Ecology and Evolution95 citationsDOIOpen Access PDF

Abstract

Abstract The leaf chlorophyll content (Chl leaf ) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red‐edge spectral region is sensitive to variations in Chl leaf. An increasing number of sensors are capable of sampling red‐edge bands, providing opportunities to estimate Chl leaf . However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)‐based Chl leaf inversions. This study aims to propose a new red‐edge chlorophyll index to decouple the effects of the canopy and soil background from the Chl leaf estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chl leaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4‐Scale‐PROSPECT model. Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground‐measured samples over nine sites world‐wide revealed that CSI improves the Chl leaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm −2 ) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm −2 ). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chl leaf conditions. Based on the CSI regression method, a Chl leaf product with a 30‐m/10‐day resolution across China was generated. The CSI is sensitive to Chl leaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf‐scale biochemistry in ecosystem modelling and ecological applications.

Topics & Concepts

Red edgeLeaf area indexCanopyChlorophyllMean squared errorRemote sensingEnvironmental scienceChlorophyll aMathematicsBotanyBiologyStatisticsGeographyRemote Sensing in AgricultureLeaf Properties and Growth MeasurementSpecies Distribution and Climate Change