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A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio : minimizing the effect of their correlation

He, Chunmei ; Sun, Jia LU orcid ; Chen, Yuwen ; Wang, Lunche ; Shi, Shuo ; Qiu, Feng ; Wang, Shaoqiang and Tagesson, Torbern LU (2023) In International Journal of Digital Earth 16(1). p.272-288
Abstract

The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with... (More)

The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration:Clinical Trials Registry India identifier: CTRI/.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Leaf carotenoids content, leaf chlorophyll content, PROSPECT model, ratio of Car to Chl, vegetation index
in
International Journal of Digital Earth
volume
16
issue
1
pages
17 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85148543337
ISSN
1753-8947
DOI
10.1080/17538947.2023.2168772
language
English
LU publication?
yes
id
95dfbc95-1939-43bd-acfd-03ec4390a1e3
date added to LUP
2023-03-07 13:15:43
date last changed
2023-09-24 12:18:28
@article{95dfbc95-1939-43bd-acfd-03ec4390a1e3,
  abstract     = {{<p>The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CI<sub>red-edge</sub>) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CI<sub>red-edge</sub> also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration:Clinical Trials Registry India identifier: CTRI/.</p>}},
  author       = {{He, Chunmei and Sun, Jia and Chen, Yuwen and Wang, Lunche and Shi, Shuo and Qiu, Feng and Wang, Shaoqiang and Tagesson, Torbern}},
  issn         = {{1753-8947}},
  keywords     = {{Leaf carotenoids content; leaf chlorophyll content; PROSPECT model; ratio of Car to Chl; vegetation index}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{272--288}},
  publisher    = {{Taylor & Francis}},
  series       = {{International Journal of Digital Earth}},
  title        = {{A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio : minimizing the effect of their correlation}},
  url          = {{http://dx.doi.org/10.1080/17538947.2023.2168772}},
  doi          = {{10.1080/17538947.2023.2168772}},
  volume       = {{16}},
  year         = {{2023}},
}