Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval
Chen, Yuwen ; Sun, Jia LU
; Wang, Lunche
; Shi, Shuo
; Qiu, Feng
; Gong, Wei
; Wang, Shaoqiang
and Tagesson, Torbern
LU
(2023)
In GIScience and Remote Sensing
60(1).
- Abstract
Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As... (More)
Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (Cab), leaf carotenoids (Car), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of Car, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to Car, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating Cab. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.
(Less)
- author
- Chen, Yuwen
; Sun, Jia
LU
; Wang, Lunche
; Shi, Shuo
; Qiu, Feng
; Gong, Wei
; Wang, Shaoqiang
and Tagesson, Torbern
LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Leaf transmittance, remote sensing, sensitivity analysis, wavelength selection
- in
- GIScience and Remote Sensing
- volume
- 60
- issue
- 1
- article number
- 2168410
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85146676095
- ISSN
- 1548-1603
- DOI
- 10.1080/15481603.2023.2168410
- language
- English
- LU publication?
- yes
- id
- 943fb10d-c049-4ab9-9010-6ddc902e85ec
- date added to LUP
- 2023-02-13 14:46:58
- date last changed
- 2023-02-20 17:09:45
@article{943fb10d-c049-4ab9-9010-6ddc902e85ec,
abstract = {{<p>Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (C<sub>ab</sub>), leaf carotenoids (C<sub>ar</sub>), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of C<sub>ar</sub>, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to C<sub>ar</sub>, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating C<sub>ab</sub>. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.</p>}},
author = {{Chen, Yuwen and Sun, Jia and Wang, Lunche and Shi, Shuo and Qiu, Feng and Gong, Wei and Wang, Shaoqiang and Tagesson, Torbern}},
issn = {{1548-1603}},
keywords = {{Leaf transmittance; remote sensing; sensitivity analysis; wavelength selection}},
language = {{eng}},
number = {{1}},
publisher = {{Taylor & Francis}},
series = {{GIScience and Remote Sensing}},
title = {{Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval}},
url = {{http://dx.doi.org/10.1080/15481603.2023.2168410}},
doi = {{10.1080/15481603.2023.2168410}},
volume = {{60}},
year = {{2023}},
}