Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Jindong Wu; Dong Wang; Marvin E. Bauer

doi:10.1016/j.fcr.2007.01.003

Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Jindong Wu, Dong Wang, Marvin E. Bauer

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Abstract

Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI-LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI-LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI-LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI-LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.

Original language	English (US)
Pages (from-to)	33-42
Number of pages	10
Journal	Field Crops Research
Volume	102
Issue number	1
DOIs	https://doi.org/10.1016/j.fcr.2007.01.003
State	Published - Apr 30 2007

Bibliographical note

Funding Information:
The authors wish to acknowledge the grant from USDA/NASA 2001-52103-11321 for providing QuickBird images and the grant from the University of Minnesota Graduate School Grant-In-Aid for supporting ground measurement. We greatly appreciate the constructive suggestions of two anonymous reviewers. We also thank Dr. Carl Rosen and Mr. Frank Kasowski for providing field sites, Dr. Yi Zhang, Dr. Kurt Spokas, and Mr. Matt McNearney for general assistance with ground measurements.

Keywords

Leaf area index
QuickBird
Remote sensing
Sensitivity
Stability
Vegetation index

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title = "Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies",

abstract = "Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI-LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI-LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI-LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI-LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.",

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author = "Jindong Wu and Dong Wang and Bauer, {Marvin E.}",

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AU - Wu, Jindong

AU - Wang, Dong

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PY - 2007/4/30

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N2 - Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI-LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI-LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI-LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI-LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.

AB - Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI-LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI-LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI-LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI-LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.

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KW - Stability

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JO - Field Crops Research

JF - Field Crops Research

IS - 1

ER -

Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Abstract

Bibliographical note

Keywords

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