In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
171 
RETRIEVAL OF VEGETATION BIOCHEMICALS USING A 
RADIATIVE TRANSFER MODEL AND HYPERSPECTRAL DATA 
R. Darvishzadeh a ’ *, Clement Atzberger b , Andrew Skidmore c , Martin Schlerf c 
a RS & GIS Department, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran- (r_darvish@sbu.ac.ir) 
b Joint Research Centre of the European Commission, TP 266, Via Enrico Fermi 1, 21027 Ispra (VA), Italy - 
(clement. atzberger@j re. it) 
c NRS Department, ITC Faculty, University of Twente, Enschede, The Netherlands - (skidmore@itc.nl;schlerf@itc.nl) 
Commission VII 
KEY WORDS: Hyper spectral, Vegetation, Estimation, Model, Spectral 
ABSTRACT: 
Accurate quantitative estimation of vegetation biochemical characteristics is necessary for a large variety of agricultural and 
ecological applications. The advent of hyperspectral remote sensing has offered possibilities for measuring specific vegetation 
variables that were difficult to measure using conventional multi-spectral sensors. In this study, the potential of biophysical 
modelling to predict leaf and canopy chlorophyll contents in a heterogeneous grassland is investigated. The well-known PROSAIL 
model was inverted with HyMap measurements by means of a look-up table (LUT). HyMap images along with simultaneous in situ 
measurements of chlorophyll content were acquired over a National Park. We tested the impact of using multiple solutions and 
spectral sub-setting on parameter retrieval. To assess the performance of the model inversion, the RMSE and R 2 between 
independent in situ measurements and estimated parameters were used. The results of the study demonstrated that inversion of the 
PROSAIL model yield higher accuracies for Canopy chlorophyll content, in comparison to Leaf chlorophyll content (R 2 =0.84, 
RMSE=0.24). Further a careful selection of spectral subset, which comprised the development of a new method to subset the spectral 
data, proved to contain sufficient information for a successful model inversion. Consequently, it increased the estimation accuracy of 
investigated parameters (R 2 =0.87, RMSE=0.22). Our results confirm the potential of model inversion for estimating vegetation 
biochemical parameters using hyperspectral measurements. 
1. INTRODUCTION 
The spatial and temporal distribution of vegetation biochemical 
and biophysical variables are important inputs into models 
quantifying the exchange of energy and matter between the land 
surface and the atmosphere. Among the many vegetation 
characteristics, leaf chlorophyll content (LCC) and canopy 
chlorophyll content (CCC) are of prime importance. Leaf 
chlorophyll content and canopy chlorophyll content (the latter 
defined here as the product of LAI and leaf chlorophyll content) 
contribute to verifying vegetation physiological status and 
health, and have been found useful for detecting vegetation 
stress, photosynthetic capacity, and productivity (Boegh et al., 
2002; Carter, 1994). 
The physical approach for estimating vegetation parameters 
from remotely sensed data, involves using radiative transfer 
models. This approach assumes that the radiative transfer model 
accurately describes the spectral variation of canopy reflectance, 
as a function of canopy, leaf and soil background 
characteristics, using physical laws (Goel, 1989; Meroni et al., 
2004). As radiative transfer models are able to explain the 
transfer and interaction of radiation inside the canopy based on 
physical laws, they offer an explicit connection between the 
vegetation biophysical and biochemical variables and the 
canopy reflectance (Houborg et al., 2007). To actually use 
physically based models for retrieving vegetation characteristics 
from observed reflectance data, they must be inverted (Kimes et 
al., 1998). A drawback in using physically based models is the 
ill-posed nature of model inversion (Atzberger, 2004; Combal 
et al., 2002), meaning that the inverse solution is not always 
unique as various combinations of canopy parameters may yield 
almost similar spectra (Weiss and Baret, 1999). To overcome 
this problem, some restriction of the inverse problem may be 
required to constrain the inversion process. This involves the 
use of prior knowledge about model parameters (Combal et al., 
2002; Lavergne et al., 2007). 
Significant efforts to estimate and quantify vegetation properties 
using radiative transfer models have been carried out in the last 
two decades. Despite these efforts, literature reveals that studies 
on heterogeneous grasslands with combinations of different 
grass species and the use of hyperspectral measurements are 
rare. The main objective of this paper is to estimate and predict 
canopy and leaf chlorophyll content by inverting the canopy 
radiative transfer model PROSAIL (Jacquemoud and Baret, 
1990; Verhoef, 1984; Verhoef, 1985). The aptness of the 
methods is analyzed in terms of prediction accuracy for 
estimating leaf and canopy chlorophyll content. 
Corresponding Author