No abstract

Observing System for a range of applications, as well as to provide an empirical basis for understanding past, current, and possible future climate variability and change.

Abstract-Aerosols are believed to play a direct role in the radiation budget of earth, but their net radiative effect is not well established, particularly on regional scales. Whether aerosols heat or cool a given location depends on their composition and column amount and on the surface albedo, information that is not routinely available, especially over land. Obtaining global information on aerosol and surface radiative characteristics, over both ocean and land, is a task of the Multi-angle Imaging SpectroRadiometer (MISR), an instrument to be launched in 1998 on the Earth Observing System (EOS)-AM1 platform. Three algorithms are described that will be implemented to retrieve aerosol properties globally using MISR data. Because of the large volume of data to be processed on a daily basis, these algorithms rely on lookup tables of atmospheric radiative parameters and predetermined aerosol mixture models to expedite the radiative transfer (RT) calculations. Over oceans, the "dark water" algorithm is used, taking full advantage of the nature of the MISR data. Over land, a choice of algorithms is made, depending on the surface types within a scene-dark water bodies, heavily vegetated areas, or high-contrast terrain. The retrieval algorithms are tested on simulated MISR data, computed using realistic aerosol and surface reflectance models. The results indicate that aerosol optical depth can be retrieved with an accuracy of 0.05 or 10%, whichever is greater, and some information can be obtained about the aerosol chemical and physical properties.

[1] Remote sensing products, such as the fraction of reflected solar radiation flux, as well as the amount of radiation absorbed in the photosynthetically active spectral region and the Leaf Area Index (LAI), are operationally available from Space Agencies. Climate models may benefit from these products provided their one dimensional (1-D) radiation transfer schemes effectively represent the three dimensional (3-D) effects implied by the internal spatial variability of vegetation canopies, e.g., the leaf area density, at all scales and resolutions involved (say from 1 to 100 kilometers). Failing to do so leads to inherent inconsistencies between the domain-averaged reflected and absorbed fluxes, and the implied Leaf Area Index. We propose a comprehensive approach which introduces a parameterization of the internal variability of the LAI in the 1-D representation of the radiation scheme, called a domain-averaged structure factor, and provides a description of the radiant fluxes fully consistent with the LAI specified by remote sensing. We take this opportunity to revisit and update the two-stream formulations implemented in climate models to accurately estimate the fractions of radiation absorbed separately by the vegetation canopy and the underlying surface. This is achieved by isolating the contributions of the vegetation canopy alone, the background as seen through the canopy gaps and the multiple scattering between the vegetation layer and the background. The performance of this formulation is evaluated against results from Monte Carlo simulations relative to explicit realistic 3-D canopies to show that the proposed scheme correctly simulates both the amplitude and the angular variations of all radiant fluxes with respect to the solar zenith angle.Citation: Pinty, B., T. Lavergne, R. E. Dickinson, J.-L. Widlowski, N. Gobron, and M. M. Verstraete (2006), Simplifying the interaction of land surfaces with radiation for relating remote sensing products to climate models,

Knowledge about the state, spatial distribution and temporal evolution of the vegetation cover is of great scientific and economic value. Satellite platforms provide a most convenient tool to observe the biosphere globally and repetitively, but the quantitative interpretation of the observations may be difficult. Reflectance measurements in the visible and near-infrared regions have been analyzed with simple but powerful indices designed to enhance the contrast between the vegetation and other surface types, however, these indices are rather sensitive to atmospheric effects. The 'correction' of satellite data for atmospheric effects is possible but requires large data sets on the composition of the atmosphere. Instead, we propose a new vegetation index which has been designed specifically to reduce the relative effects of these undesirable atmospheric perturbations, while maintaining the information about the vegetation cover.Terrestrial vegetation, which constitutes the bulk of the continental biomass (Ajtay et al. 1979), affects the climate system over a wide range of space and time scales by modifying the surface energy balance, and by influencing the exchanges of water and carbon between the surface and the atmosphere. The identification of green plant material is of great interest since leaves and needles are the site of photosynthesis and the prime link between the biosphere and the atmosphere.