1117 
CONCLUSION AND PERSPECTIVES 
A regional sahel ian grassland ecosystem model had been developed within a methodology to integrate remote 
sensing data into an explicit formulation of the main processes occuring in the ecosystem. A validation of the 
model, irrespective of remote sensing data, had been carried out with biomass measurements acquired on two 
different regions of the Sahel. In order to assess how sensitive vegetation indices are to varying vegetation 
conditions, a canopy reflectance and a soil reflectance model fed with structural and other parameters simulated 
by the ecosystem model were used to compute reflectances of the green and dry vegetation, and of the soil. A 
simple linear mixture model using the simulated vegetation cover fractions were applied to weight the 
contributions of the different components of the landscape reflectances. The latter were then processed through 
an atmosphere model to yield observable TOA reflectances. Confrontation with real satellite data showed that 
observed NDVI profiles could be well reproduced by model simulations, in phase as well as in amplitude. 
A major advantage offered by a methodology centered on an ecosystem processes model is the framework 
within which satellite data from different parts of the electromagnetic spectrum may be used complementarity. 
Whereas the present study has focused on optical Vis alone, another complementary source of satellite data can 
be used to supplement the Vis when the latter perform unsatisfactorily. Microwaves are known to be less prone 
to atmospheric perturbations, and still be sensible to changes in vegetation characteristics. Therefore, a study 
similar to the present one, using the appropriate physical models, must be carried out on passive and active 
microwave products to determine whether their complementarity with optical Vis is feasible and meaningful. 
The model up to now runs with standard meteorological data, the most important of which is the daily rainfall. 
At term, this point input has to be replaced with a spatialized field derived from remote sensing data. Recent 
methods to derive précipitable water from geostationnary meteorological satellite data give reliable estimates 
for a time step not shorter than a decade. Here also, a study is prescribed to quantify the consequences of the 
rainfall estimation and timing errors on the processes simulations. If the errors are not prohibitive, the model 
simulations can be reajusted and constrained via control variables also derived from remote sensing data (Vis, 
microwave indices,..). The combined use of different sources of remote sensing data prefigures new problems 
related to the spatial scale, time step and nature proper to each of them, and their assimilation in the ecosystem 
model. The basis of an assimilation algorithm capable of addressing such problems must therefore be set 
ACKNOWLEDGEMENTS 
We are indebted to Dr. W. van Leeuwen and Dr. A. Bégué for making available to us parameters to run the soil 
reflectance model on sahelian soils. We would also like to thank Dr. G. Dedieu for the SMAC model, Dr. F. 
Baret and Dr. S. Jacquemoud for the PROSPECT model. The AVHRR GAC data set was kindly provided by 
the Institute for Remote Sensing Applications, JRC, Ispra, Italy. 
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