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Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval

Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval
Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval
Leaf Area Index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional Vegetation Indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the Normalized Difference Vegetation Index (NDVI), are commonly used to estimate Leaf Area Index (LAI). However, these indices commonly saturate at moderate-to-dense canopies (e.g. NDVI saturates when LAI exceeds 3). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that, the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR) and the green chlorophyll index (CIgreen) formula. These improved indices are termed NDVIred&RE (red and red-edge normalized difference vegetation index), MSRred&RE (red and red-edge modified simple ratio index) and CIred&RE (red and red-edge chlorophyll index). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine vegetation indices for crop LAI estimation, including NDVI, MSR, CIgreen, the red-edge modified indices NDVIRed-edge, MSRRed-edge, CIRed-edge (generally represented by VIRed-edge) and the newly improved indices NDVIred&RE, MSRred&RE, and CIred&RE (generally represented by VIred&RE). The results show that VIred&RE improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to VIRed-edge. The newly improved indices prove to be powerful alternatives for LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.
1939-1404
Xie, Qiaoyun
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Dash, Jadu
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Huang, Wenjiang
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Peng, Dailiang
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Qin, Qiming
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Mortimer, Hugh
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Casa, Raffaele
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Pignatti, Stefano
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Laneve, Giovanni
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Pascucci, Simone
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Dong, Yingying
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Ye, Huichun
f42884ae-149b-44b7-af92-9efb6814f1d7
Xie, Qiaoyun
0c068452-9b31-41e7-a37d-878ae1036d6d
Dash, Jadu
51468afb-3d56-4d3a-aace-736b63e9fac8
Huang, Wenjiang
fdf66a2a-5ff5-4c1c-b732-4a9ed50997fa
Peng, Dailiang
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Qin, Qiming
8712698a-7734-4180-a11a-5ccc88cba326
Mortimer, Hugh
ee70cd0a-8427-4c5c-8091-1d142d0d5325
Casa, Raffaele
90c7c516-da08-4413-9820-62862fe7027e
Pignatti, Stefano
e07505aa-929a-41be-95a8-83db493b445c
Laneve, Giovanni
6c5baf06-5644-45d5-ad21-89a219c6ec63
Pascucci, Simone
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Dong, Yingying
21f3f2d5-21ad-4776-b57e-df48a002498f
Ye, Huichun
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Xie, Qiaoyun, Dash, Jadu, Huang, Wenjiang, Peng, Dailiang, Qin, Qiming, Mortimer, Hugh, Casa, Raffaele, Pignatti, Stefano, Laneve, Giovanni, Pascucci, Simone, Dong, Yingying and Ye, Huichun (2018) Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (doi:10.1109/JSTARS.2018.2813281).

Record type: Article

Abstract

Leaf Area Index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional Vegetation Indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the Normalized Difference Vegetation Index (NDVI), are commonly used to estimate Leaf Area Index (LAI). However, these indices commonly saturate at moderate-to-dense canopies (e.g. NDVI saturates when LAI exceeds 3). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that, the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR) and the green chlorophyll index (CIgreen) formula. These improved indices are termed NDVIred&RE (red and red-edge normalized difference vegetation index), MSRred&RE (red and red-edge modified simple ratio index) and CIred&RE (red and red-edge chlorophyll index). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine vegetation indices for crop LAI estimation, including NDVI, MSR, CIgreen, the red-edge modified indices NDVIRed-edge, MSRRed-edge, CIRed-edge (generally represented by VIRed-edge) and the newly improved indices NDVIred&RE, MSRred&RE, and CIred&RE (generally represented by VIred&RE). The results show that VIred&RE improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to VIRed-edge. The newly improved indices prove to be powerful alternatives for LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.

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Accepted/In Press date: 26 February 2018
e-pub ahead of print date: 29 March 2018

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Local EPrints ID: 418574
URI: http://eprints.soton.ac.uk/id/eprint/418574
ISSN: 1939-1404
PURE UUID: c1ae9b8a-3eb9-4eca-ac05-0304c922822b
ORCID for Jadu Dash: ORCID iD orcid.org/0000-0002-5444-2109

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Date deposited: 12 Mar 2018 17:30
Last modified: 18 Feb 2021 17:01

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Contributors

Author: Qiaoyun Xie
Author: Jadu Dash ORCID iD
Author: Wenjiang Huang
Author: Dailiang Peng
Author: Qiming Qin
Author: Hugh Mortimer
Author: Raffaele Casa
Author: Stefano Pignatti
Author: Giovanni Laneve
Author: Simone Pascucci
Author: Yingying Dong
Author: Huichun Ye

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