A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

Xing, N; Huang, W; Xie, Q; Shi, Y; Ye, H; Dong, Y; Wu, M; Sun, G; Jiao, Q

A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

Xing, N Huang, W Xie, Q

Shi, Y Ye, H Dong, Y Wu, M Sun, G Jiao, Q

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Remote Sensing, 2020, 12, (1), pp. 16-16
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Xing, N
dc.contributor.author	Huang, W
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.contributor.author	Shi, Y
dc.contributor.author	Ye, H
dc.contributor.author	Dong, Y
dc.contributor.author	Wu, M
dc.contributor.author	Sun, G
dc.contributor.author	Jiao, Q
dc.date.accessioned	2020-10-22T03:05:50Z
dc.date.available	2020-10-22T03:05:50Z
dc.date.issued	2020
dc.identifier.citation	Remote Sensing, 2020, 12, (1), pp. 16-16
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/143460
dc.description.abstract	<jats:p>Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation	University of Technology Sydney
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs12010016
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-10-22T03:05:19Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	1

Abstract:

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

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http://hdl.handle.net/10453/143460