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COMPARISON OF SEVERAL OPTIMIZATION METHODS TO EXTRACT 
CANOPY BIOPHYSICAL PARAMETERS ■ APPLICATION TO CAESAR DATA 
S. JACQUEMOUD 1 *, S. FLASSE 2 , J. VERDEBOUT 1 , G. SCHMUCK 1 
Joint Research Centre 
Institute for Remote Sensing Applications 
(1) Advanced Techniques (2) Monitoring Tropical Vegetation 
21020 Ispra (Va), Italy 
ABSTRACT 
An improved version of the SAIL model which includes the hot spot effect and the spectral variation of 
vegetation reflectance is used to retrieve canopy biophysical parameters from visible and near infrared 
radiometric data. The leaf mesophyll structure, the chlorophyll a+b concentration, the leaf area index, the mean 
leaf inclination angle and the hot spot size parameter are determined by inversion of the coupled 
PROSPECT+SAIL model. Four different optimization methods (Quasi-Newton, Marquardt, Simplex, Genetic 
Algorithms+Quasi-Newton) are tested with several kinds of data (synthetic data and airborne data acquired 
with the CAESAR sensor) and compared in terms of accuracy and computation time. 
KEY WORDS: canopy reflectance, models, inversion 
INTRODUCTION 
The interpretation of optical remote sensing data for agricultural and ecological applications is still 
problematic. A classical approach involves vegetation indices built from reflectance values acquired in the red 
and near infrared by space borne sensors. The development of a new generation of instruments capable of 
measuring the spectral radiance at several viewing angles may be accompanied by new methods of 
interpretation. Among these, the inversion of physically-based reflectance models appears very promising 
because it allows to separate the influence of surface variables on the measured radiometric signal (Flasse, 
1993) . Estimating' properly canopy biophysical variables from reflectance measurements implies first an 
appropriate model and second an appropriate inversion procedure! 
In an inversion perspective, the choice of the model is governed by a certain number of rules. Remote 
sensing, as many scientific disciplines, uses modelling which consists in creating an abstract and reduced 
version of reality. If we use a sufficiently high number of parameters, it is clear that we can always construct a 
mathematical model describing any situation. But obviously that is not the real problem: the challenge consists 
in constructing a model which does not rely too heavily on mathematical hypotheses. Thus there is a conflict 
between a strict adherence to empirical data, commonly called a fit, and the quantity of parameters used in a 
model: a lot of parameters may provide a good fit but also imply a complicated model. When inverting them, 
best models are those which make a compromise between a few parameters and a good fit (Thom, 1983). 
However this condition is not sufficient since the description of canopy reflectance with mathematical model 
leaves aside the physical principles governing the reflectance. The model parameters must correspond to 
quantities measurable in the field and interpretable in terms of physical and biological properties. Finally, due 
to the great variability of plant canopies (homogeneous, row, sparse or mixed crops), it is perhaps futile to try to 
build a universal model applicable to complex media (Pinty and Verstraete, 1992). Different models have been 
inverted to extract information on vegetation from bidirectional (Goel and Thompson, 1984; Otterman, 1990; 
Pinty et al„ 1990; Kuusk, 1991a; Deering et al„ 1992), spectral (Schmuck et al„ 1993; Baret and Jacquemoud, 
1994) , or both bidirectional and spectral (Kuusk, 1994) reflectance measurements. 
According to the method of least squares, inverting a canopy reflectance model consists in 
determining simultaneously the values of the parameters of the model which minimize the distance between the 
measured and the simulated data. For this purpose, one defines a merit function A^LtRmes-RmodfP)] 2 where 
Rmes is the measured reflectance and Rmod(P) the reflectance modeled with the set of parameters P influencing 
* Permanent affiliation: LAMP/OPGC, Université Blaise Pascal, 63177 Aubière, France