Optimized estimation of leaf mass per area with a 3d matrix of vegetation indices
Chen, Yuwen ; Sun, Jia LU
; Wang, Lunche
; Shi, Shuo
; Gong, Wei
; Wang, Shaoqiang
and Tagesson, Torbern
LU
(2021)
In Remote Sensing
13(18).
- Abstract
Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then,... (More)
Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, re-spectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
(Less)
- author
- Chen, Yuwen
; Sun, Jia
LU
; Wang, Lunche
; Shi, Shuo
; Gong, Wei
; Wang, Shaoqiang
and Tagesson, Torbern
LU
- organization
- publishing date
- 2021-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- 3D matrix, leaf mass per area, PROSPECT-D model, Vegetation index
- in
- Remote Sensing
- volume
- 13
- issue
- 18
- article number
- 3761
- publisher
- MDPI AG
- external identifiers
-
- scopus:85115319605
- ISSN
- 2072-4292
- DOI
- 10.3390/rs13183761
- project
- Carbon Sequestration and greenhouse gas emissions in (agro) Sylvopastoral Ecosystems in the Sahelian CILSS States
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This research was funded by the National Key R&D Program of China (2018YFB0504500); National Natural Science Foundation of China (42001314); Open Research Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University (grant number 20R02), and Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan (grant number 111-G1323520290). T.T. was funded by SNSA (Dnr 96/16) and the EU-Aid-funded CASSECS project. Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
- id
- de69c60e-cb19-4a87-acb0-96ae4e325c4d
- date added to LUP
- 2021-09-28 12:52:19
- date last changed
- 2023-03-08 01:03:22
@article{de69c60e-cb19-4a87-acb0-96ae4e325c4d,
abstract = {{<p>Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R<sup>2</sup> = 0.76 and 0.78, RMSE = 0.0016 g/cm<sup>2</sup> and 0.0017 g/cm<sup>2</sup>, re-spectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.</p>}},
author = {{Chen, Yuwen and Sun, Jia and Wang, Lunche and Shi, Shuo and Gong, Wei and Wang, Shaoqiang and Tagesson, Torbern}},
issn = {{2072-4292}},
keywords = {{3D matrix; leaf mass per area; PROSPECT-D model; Vegetation index}},
language = {{eng}},
number = {{18}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Optimized estimation of leaf mass per area with a 3d matrix of vegetation indices}},
url = {{http://dx.doi.org/10.3390/rs13183761}},
doi = {{10.3390/rs13183761}},
volume = {{13}},
year = {{2021}},
}