Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venμs sensors

Anthony L. Nguy-Robertson, Anatoly A. Gitelson

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This study developed a set of algorithms for satellite mapping of green leaf area index (LAI) in C3 and C4 crops. In situ hyperspectral reflectance and green LAI data, collected across eight years (2001-2008) at three AmeriFlux sites in Nebraska USA over irrigated and rain-fed maize and soybean, were used for algorithm development. The hyperspectral reflectance was resampled to simulate the spectral bands of sensors aboard operational satellites (Aqua and Terra: MODIS, Landsat: TM/ETM+), a legacy satellite (Envisat: MERIS), and future satellites (Sentinel-2, Sentinel-3, and Venμs). Among 15 vegetation indices (VIs) examined, five VIs - wide dynamic range vegetation index (WDRVI), green WDRVI, red edge WDRVI, and green and red edge chlorophyll indices - had a minimal noise equivalent for estimating maize and soybean green LAI ranging from 0 to 6.5 m<sup>2</sup> m<sup>-2</sup>. The algorithms were validated using MODIS, TM/ETM+, and MERIS satellite data. The root mean square error of green LAI prediction in both crops from all sensors examined in this study ranged from 0.73 to 0.95 m<sup>2</sup> m<sup>-2</sup> and coefficient of variation ranged between 17.0 and 29.3%. The algorithms using the red edge bands of MERIS and future space systems Sentinel-2, Sentinel-3, and Venμs allowed accurate green LAI estimation over areas containing maize and soybean with no re-parameterization.

Original languageEnglish (US)
Pages (from-to)360-369
Number of pages10
JournalRemote Sensing Letters
Volume6
Issue number5
DOIs
StatePublished - May 4 2015

Fingerprint

MERIS
vegetation index
Landsat thematic mapper
leaf area index
MODIS
Crops
Satellites
sensor
crop
Sensors
soybean
maize
reflectance
Aqua (satellite)
Terra (satellite)
Chlorophyll
Parameterization
Mean square error
Rain
satellite data

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Electrical and Electronic Engineering

Cite this

Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venμs sensors. / Nguy-Robertson, Anthony L.; Gitelson, Anatoly A.

In: Remote Sensing Letters, Vol. 6, No. 5, 04.05.2015, p. 360-369.

Research output: Contribution to journalArticle

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