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ESTIMATION OF LEAF AREA INDEX AND CHLOROPHYLL FOR A
MEDITERRANEAN GRASSLAND USING HYPERSPECTRAL DATA
R. Darvishzadeh a,b \ A. Skidmore a , M. Schlerf a , C. Atzberger c , F. Corsi d , M. Cho L
a International Institute for Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, P.O. Box 6,
7500 AA Enschede, The Netherlands - (darvish, skidmore, schlerf)@itc.nl
b Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran -
darvishzade@yahoo.com
c Joint Research Centre of the European Commission, TP 266, Via Enrico Fermi 1, 21027 Ispra (VA), Italy -
clement.atzberger@jrc.it
d 712 Locksley Rd, Yorktown Heights, NY, 10598, USA - fabio@st-fc.org
e Ecological Remote Sensing, Natural Resources and the Environment, Earth Observation Group, Building 33 CSIR, Pretoria, SA -
Mcho@csir.co.za
Commission VII, WG VII/3
KEY WORDS: Hyperspectral, Vegetation, Estimation, Leaf Area Index, Chlorophyll
ABSTRACT:
The study shows that leaf area index (LAI) and canopy chlorophyll content can be mapped in a heterogeneous Mediterranean
grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700
spectroradiometer, along with concomitant in situ measurements of LAI and chlorophyll content. We tested the utility of univariate
techniques, involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques,
such as partial least squares regression. Among the various investigated models, canopy chlorophyll content was estimated with the
highest accuracy (R 2 CV = 0.74, relative RMSE CV = 0.35) and LAI was estimated with intermediate accuracy (R 2 CV = 0.67). Compared
with narrow band indices and red edge inflection point, partial least squares regression generally improved the estimation accuracies.
The results of the study highlight the significance of using multivariate techniques such as partial least squares regression rather than
univariate methods such as vegetation indices for providing enhanced estimates of heterogeneous grass canopy characteristics. To
date, partial least squares regression has seldom been applied for studying heterogeneous grassland canopies. However, it can
provide a useful exploratory and predictive tool for mapping and monitoring heterogeneous grasslands.
1. INTRODUCTION
Owing to its fast, non-destructive and relatively cheap
characterization of land surfaces, remote sensing has been
recognized as a reliable method for estimating various
biophysical and biochemical vegetation variables (Curran et al.,
2001; Hansen and Schjoerring, 2003; Weiss and Baret, 1999).
Hyperspectral remote sensing with narrow and continuous
spectral bands that provide an almost continuous spectrum is
considered more sensitive to specific vegetation variables such
as leaf area index (LAI) (Hansen and Schjoerring, 2003).
Because of the role of green leaves in controlling many
biological and physical processes of plant canopies, LAI (the
total one-sided leaf area per ground surface area) is a key
structural characteristic of vegetation and thus widely used as
an indicator of vegetation status.
LAI has been estimated in numerous studies by using remote
sensing in either statistical approaches or physically based
(canopy reflectance) models. Many of the previous studies,
however, are based on simulated data (Atzberger, 2004; Broge
and Leblanc, 2001; Haboudane et al., 2004), on agricultural
crops (Atzberger, 1995; Atzberger, 1997; Baret et al., 1987;
Broge and Mortensen, 2002; Jacquemoud et al., 2000; Waiter-
Shea et al., 1997; Weiss et al., 2001) or on forest (Chen et al.,
1997; Fang et al., 2003; Kalacska et al., 2004; Running et al.,
1986; Schlerf and Atzberger, 2006; White et al., 1997), where
single species was investigated. Therefore, investigation is
required to assess the capability of remote sensing models when
it comes to natural heterogeneous canopies with a combination
of different plant species in varying proportions. Mediterranean
grasslands are characterized by highly heterogeneous canopies,
and present a challenge for remote sensing applications because
the reflectance is often a mixture of different surface materials
(Fisher, 1997; Roder et al., 2007).
The aim of this study was to examine the utility of
hyperspectral remote sensing in predicting canopy
characteristics such as LAI and canopy chlorophyll content in a
heterogeneous Mediterranean grassland by means of different
univariate and multivariate methods. We compared narrow
band vegetation indices, including red edge inflection point
(REIP), with partial least squares regression. The suitability of
these different methods will be analyzed in terms of their
prediction accuracy. Naturally, the significance of the results is
valid only for Mediterranean grasslands and the biophysical
variables considered. The study is based on canopy spectral
reflectance measured in a heterogeneous grassland during a
field campaign in the summer of 2005 in Majella National Park,
Italy.
