Introduction

Satellite-borne instruments constitute, a priori, a unique tool for monitoring surface albedo values at the global scale and at spatial and temporal resolutions adequate for meteorological and climate studies. However, the

Remote Sensing and Climate Modeling: Synergies and Limitations, 51-67. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

effectiveness of this approach hinges on the availability of tools and models that can accurately account for the radiative contributions to the measured radiances from the atmospheric and surface components of the observed system, including the spectral and directional variations resulting from the anisotropy of terrestrial surfaces. The accurate representation of the upward radiance field at the land surface (bottom of the atmosphere), taking into account the convolution of surface and atmospheric scattering properties, is a major scientific problem to be solved.

Atmospheric aerosols of diverse origins exhibit significant spatial and temporal variations and strongly impact radiation transfer processes at solar wavelengths, but their properties have never been made available as an operational product. This lack of reliable information on aerosol load and properties reinforces the need to invert coupled surface-atmosphere radiation transfer models against space remote sensing data. As is usual with inverse problems, a minimum number of input data of sufficient quality and a small set of critical state variables are required to guarantee a reliable assessment of the retrieved properties. Martonchik et al. (1998a) demonstrated the possibility of retrieving surface radiative properties from an analysis of quasi-instantaneous multi-angular spectral measurements of the radiance fields emerging at the Top Of the Atmosphere (TOA). Martonchik et al. (1998b), Kahn et al. (1997) and Kahn et al. (1998) showed that aerosol properties can similarly be estimated. The design of the Meteosat VIS band does not yield a comprehensive spectral and directional sampling of the radiance fields scattered by the Earth. However, thanks to its geostationary orbit, this sensor is able to sample the radiance field emerging at TOA every thirty minutes during the course of the day, i.e., for different solar illumination conditions. In other words, assuming that the geophysical system under observation does not change drastically during the daily period of solar illumination, Meteosat data provide a useful angular sampling of the radiance field scattered by the Earth system. Whenever and wherever this assumption is acceptable, the Meteosat temporal sampling of the radiance field for a given location can thus be interpreted as an angular sampling; this approach constitutes the cornerstone of our strategy to estimate surface albedo values.

This paper summarizes the methodology developed to address various issues related to the actual application of a multi-angular approach for estimating surface properties from the Meteosat data set. These issues include

1. the optimal modeling of the radiation transfer for clear sky conditions as measured by the Meteosat instrument for finding solutions to an inverse problem in an operational context,

2. the selection, for each pixel (location), of those time observations during the day which are not contaminated by cloud radiative effects, and

3. the identification of the optimal solution, i.e., the joint characterization of the surface and the atmosphere for each pixel and each day through the set of potential solutions.

This paper also discusses the results of this approach when applied to one year (1996) of Meteosat data. It will be seen that our results point to anthropogenic effects, and in particular biomass burning, as the likely process of surface albedo changes in savannas and woodlands.

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