A regional sahelian grassland model to be coupled with multispectral satellite data. II. Toward the control of its simulations by remotely sensed indices
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Remote Sensing of Environment;52(3): 194-206
Permanent link to this item: http://hdl.handle.net/10568/29554
An approach for combining remote sensing spectral measurements with an ecosystem model was presented in an accompanying article. The sahelian grassland ecosystem STEP model developed for that purpose was also described and validated. In order to fulfill a prerequisite for using coarse resolution optical satellite data with the STEP model, the present paper presents i) a modelling of the reflectance 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 modelling of the landscape reflectance is based on existing soil and canopy reflectance models, and considers area-weighted contributions from green and dry vegetation, and bare soil components. The ecosystem model provides the landscape reflectance models with inputs like vegetation cover fraction (fv) and leaf area index (LAI) to characterize the vegetation present. Atmospheric effects are also accounted for using an existing simplified radiative transfer model. Simulated top of the atmosphere reflectances confronted to real satellite data during a growing season indicate that the modelling is adequate to reproduce temporal profiles of vegetation indices when atmospheric conditions are not prohibitive. Simulated vegetation indices (NDVI, SAVI, GEMI, SR) compared to vegetation characteristics show that a good tracking of the evolution of LAI and fv during the growing season is possible before maturation. A sensitivity study of the four VIs to green biomass, soil brightness, and atmospheric water vapour is carried out for the specific case of the Sahel. The SAVI and NDVI are both found to be adequate if atmospheric effects are minimized. NDVI integrated over the growing season is compared to net primary productivity (NPP) for different sites, regions, and growing seasons. A near-linear relationship is found, but the same relationship may not be applicable to different regions or growing 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 informations difficult. The possibility of using satellite data to drive the STEP model, or control its simulations, will be assessed in a forthcoming article.
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