Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Anthony L. Nguy-Robertson; Yi Peng; Anatoly A. Gitelson; Timothy J. Arkebauer; Agustin Pimstein; Ittai Herrmann; Arnon Karnieli; Donald C. Rundquist; David J. Bonfil

doi:10.1016/j.agrformet.2014.03.004

Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Anthony L. Nguy-Robertson, Yi Peng, Anatoly A. Gitelson^*, Timothy J. Arkebauer, Agustin Pimstein, Ittai Herrmann, Arnon Karnieli, Donald C. Rundquist, David J. Bonfil

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

157 Scopus citations

Abstract

Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530-570. nm) and red edge (700-725. nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 - OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.

Original language	English
Pages (from-to)	140-148
Number of pages	9
Journal	Agricultural and Forest Meteorology
Volume	192-193
DOIs	https://doi.org/10.1016/j.agrformet.2014.03.004
State	Published - 15 Jul 2014
Externally published	Yes

Bibliographical note

Funding Information:
This study was supported partially by the NASA NACP program, the U.S. Department of Energy EPSCoR program, Grant Office of Science Biological and Environmental Research , and NASA EPSCoR “Aerial” grant and the Nebraska Space Grant program and by BARD Senior Research Fellowship FR-29-2012 to AAG and DJB and Lady Davis Fellowship to AAG. We acknowledge the support and the use of facilities and equipment provided by the Center for Advanced Land Management Information Technologies (CALMIT), and the Carbon Sequestration Program, University of Nebraska-Lincoln. This research was also supported in part by funds provided through the Hatch Act. We are also thankful for the multitude of staff, graduate and undergraduate students involved in collecting the data used in the study.

Keywords

LAI
Landsat
MERIS
MODIS
Sentinel-2
VENμS
Vegetation index

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.agrformet.2014.03.004

Cite this

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title = "Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm",

abstract = "Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530-570. nm) and red edge (700-725. nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 - OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.",

keywords = "LAI, Landsat, MERIS, MODIS, Sentinel-2, VENμS, Vegetation index",

author = "Nguy-Robertson, {Anthony L.} and Yi Peng and Gitelson, {Anatoly A.} and Arkebauer, {Timothy J.} and Agustin Pimstein and Ittai Herrmann and Arnon Karnieli and Rundquist, {Donald C.} and Bonfil, {David J.}",

note = "Funding Information: This study was supported partially by the NASA NACP program, the U.S. Department of Energy EPSCoR program, Grant Office of Science Biological and Environmental Research , and NASA EPSCoR “Aerial” grant and the Nebraska Space Grant program and by BARD Senior Research Fellowship FR-29-2012 to AAG and DJB and Lady Davis Fellowship to AAG. We acknowledge the support and the use of facilities and equipment provided by the Center for Advanced Land Management Information Technologies (CALMIT), and the Carbon Sequestration Program, University of Nebraska-Lincoln. This research was also supported in part by funds provided through the Hatch Act. We are also thankful for the multitude of staff, graduate and undergraduate students involved in collecting the data used in the study. ",

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doi = "10.1016/j.agrformet.2014.03.004",

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T2 - Potential of determining optimal spectral bands for a universal algorithm

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AU - Peng, Yi

AU - Gitelson, Anatoly A.

AU - Arkebauer, Timothy J.

AU - Pimstein, Agustin

AU - Herrmann, Ittai

AU - Karnieli, Arnon

AU - Rundquist, Donald C.

AU - Bonfil, David J.

N1 - Funding Information: This study was supported partially by the NASA NACP program, the U.S. Department of Energy EPSCoR program, Grant Office of Science Biological and Environmental Research , and NASA EPSCoR “Aerial” grant and the Nebraska Space Grant program and by BARD Senior Research Fellowship FR-29-2012 to AAG and DJB and Lady Davis Fellowship to AAG. We acknowledge the support and the use of facilities and equipment provided by the Center for Advanced Land Management Information Technologies (CALMIT), and the Carbon Sequestration Program, University of Nebraska-Lincoln. This research was also supported in part by funds provided through the Hatch Act. We are also thankful for the multitude of staff, graduate and undergraduate students involved in collecting the data used in the study.

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N2 - Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530-570. nm) and red edge (700-725. nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 - OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.

AB - Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530-570. nm) and red edge (700-725. nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 - OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.

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KW - MERIS

KW - MODIS

KW - Sentinel-2

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Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm

Abstract

Bibliographical note

Keywords

UN SDGs

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