A REGIONAL SAHELIAN GRASSLAND MODEL TO BE COUPLED WITH MULTISPECTRAL SATELLITE DATA .2. TOWARD THE CONTROL OF ITS SIMULATIONS BY REMOTELY-SENSED INDEXES

An approach for combining remote sensing spectral measurements with an ecosystem model was presented in an accompanying article (Mougin et a l., 1995). The sahelian grassland ecosystem STEP model developed for t hat purpose was also described and validated. In order to fulfill a pr erequisite for using coarse resolution optical satellite data with the STEP model, the present paper presents i) a modeling of the reflectan ce which is adapted to the sahelian landscape and ii) a study based on the coupled ecosystem-reflectance modeling to assess the potential of vegetation indices for inferring vegetation. parameters. The modeling of the landscape reflectance is based on existing soil and canopy ref lectance models, and considers area-weighted contributions from green and dry vegetation, and bare soil components. The ecosystem model prov ides the landscape reflectance models with inputs like vegetation cove r fraction. (f(v)) and leaf area index (LAI) to characterize the veget ation present. Atmospheric effects are also accounted for using an exi sting simplified radiative transfer model. Simulated top of the atmosp here reflectances confronted to real satellite data during a growing s eason indicate that the modeling is adequate to reproduce temporal pro files of vegetation indices when atmospheric conditions are not prohib itive. Simulated vegetation indices (NDVI, SAVI, GEMI, SR) compared to vegetation characteristics show that a good tracking of the evolution of LAI and f(v) during the growing season is possible before maturati on. A sensitivity study of the four VIs to green biomass, soil brightn ess, and atmospheric water vapor is carried out for the specific case of the Sahel. The SAVI and NDVI are both found to be adequate if atmos pheric effects are minimized. NDVI integrated over the growing season is compared to net primary productivity (NPP) for different sites, reg ions, and growing seasons. A near-linear relationship is found, but th e same relationship may not be applicable to different regions or grow ing seasons, On the whole, the results suggest that vegetation indices contain information which are useful for the ecosystem model, despite the fact that perturbating factors make the retrieval of these inform ations difficult. The possibility of using satellite data to drive the STEP model, or control its simulations will be assessed in a forthcom ing article.