A novel red‐edge spectral index for retrieving the leaf chlorophyll content. Issue 12 (18th October 2022)

• Record Type: Journal Article
• Authors: Zhang, Hu; Li, Jing; Liu, Qinhuo; Lin, Shangrong; Huete, Alfredo; Liu, Liangyun; Croft, Holly; Clevers, Jan G. P. W.; Zeng, Yelu; Wang, Xiaohan; Gu, Chenpeng; Zhang, Zhaoxing; Zhao, Jing; Dong, Yadong; Mumtaz, Faisal; Yu, Wentao
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 12(2022)
• Page Start: 2771
• Page End: 2787
• Publication Date: 2022-10-18
• Subjects: chlorophyll content estimation -- chlorophyll product -- PROSAIL model -- remote sensing -- spectral vegetation index; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13994
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: The leaf chlorophyll content (Chlleaf ) 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 Chlleaf. An increasing number of sensors are capable of sampling red‐edge bands, providing opportunities to estimate Chlleaf . However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)‐based Chlleaf inversions. This study aims to propose a new red‐edge chlorophyll index to decouple the effects of the canopy and soil background from the Chlleaf estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chlleaf 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 Chlleaf 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 Chlleaf conditions. Based on the CSI regression method, a Chlleaf product with a 30‐m/10‐day resolution across China was generated. The CSI is sensitive to Chlleaf 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. 摘要: 从区域到全球尺度，叶片叶绿素含量(Chlleaf )在碳循环建模和农业监测中都有重要作用。目前越来越多的传感器涵盖了对叶片叶绿素含量变化信息高度敏感的红边波段，为Chlleaf 的估算带来了新机遇。 然而，冠层、叶片及土壤等其他信息会和Chlleaf 信息一起耦合在反射信号中，这限制了基于单一植被指数(vegetation index, VI)反演Chlleaf 的普适性。 本研究旨在提出一个新的红边叶绿素指数，以消除Chlleaf 估算中冠层、土壤背景的影响。 本文提出了叶绿素敏感指数(CSI)，并利用PROSAIL(PROSPECT + SAIL)和4‐Scale‐PROSPECT模型得到了CSI和Chlleaf 之间的回归关系。 敏感性分析表明，CSI 对冠层结构和土壤背景的变化不敏感。全球 9 个站点、308 个地面测量样本的验证结果表明:与现有表现最好的MTCI指数相比，CSI 提高了Chlleaf 反演精度(CSI: RMSE = 9.39 μg cm −2 ，MTCI: RMSE = 13.00 μg cm −2 )。此外，CSI方法在不同LAI和Chlleaf 条件下反演精度高且稳定性强。 本文基于 CSI 回归方法，生产了中国地区30 米/10 天分辨率的Chlleaf 产品。 CSI 对 Chlleaf 敏感，但对冠层结构和土壤水分参数不敏感，能够准确反演叶片叶绿素含量这一叶片尺度生物化学信息，在生态系统建模和生态应用等方面具有潜力。