Atmospheric general circulation models used for climate simulation and weather forecasting require the fluxes of radiation, heat, water vapor, and momentum across the land-atmosphere interface to be specified. These fluxes are calculated by submodels called land surface parameterizations. Over the last 20 years, these parameterizations have evolved from simple, unrealistic schemes into credible representations of the global soil-vegetation-atmosphere transfer system as advances in plant physiological and hydrological research, advances in satellite data interpretation, and the results of largescale field experiments have been exploited. Some modern schemes incorporate biogeochemical and ecological knowledge and, when coupled with advanced climate and ocean models, will be capable of modeling the biological and physical responses of the Earth system to global change, for example, increasing atmospheric carbon dioxide.Until the early 1980s, global atmospheric general circulation models (AGCMs) incorporated very simple land surface parameterizations (LSPs) to estimate the exchanges of energy, heat, and momentum between the land surface and the atmosphere. These have since evolved into a family of schemes that can realistically describe a comprehensive range of land-atmosphere interactions. These advanced schemes will be needed to understand the response of the biosphere and the climate system to global change, for example, increasing atmospheric CO 2 (1-3).Three generations of models have taken us from the early LSPs to where we stand now. The first, developed in the late 1960s and 1970s, was based on simple aerodynamic bulk transfer formulas and often uniform prescriptions of surface parameters (albedo, aerodynamic roughness, and soil moisture availability) over the continents (4). In the early 1980s, a second generation of models explicitly recognized the effects of vegetation in the calculation of the surface energy balance (5, 6). At the same time, global, spatially varying data of land surface properties were assembled from ecological and geographical surveys published in the scientific literature (7). The latest (third generation) models use modern theories relating photosynthesis and plant water relations to provide a consistent description of energy exchange, evapotranspiration, and carbon exchange by plants (8-10). Some are beginning to incorporate treatments of nutrient dynamics and biogeography, so that vegetation systems can move in response to climate shifts. A series of largescale field experiments have been executed to validate the process models and scaling assumptions involved in land-atmosphere schemes (3). These experiments have also accelerated the development of methods for translating satellite data into global surface parameter sets for the models. Theoretical Background and the First-Generation ModelsIt has been understood for nearly 200 years that the continents and the atmosphere exchange energy, water, and carbon with each other. However, it was not until the late 1960s with the construct...

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of reflectance-based biophysical products, and applications that merge reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.

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[1] Our knowledge of the distribution and amount of terrestrial biomass is based almost entirely on ground measurements over an extremely small, and possibly biased sample, with many regions still unmeasured. Our understanding of changes in terrestrial biomass is even more rudimentary, although changes in land use, largely tropical deforestation, are estimated to have reduced biomass, globally. At the same time, however, the global carbon balance requires that terrestrial carbon storage has increased, albeit the exact magnitude, location, and causes of this residual terrestrial sink are still not well quantified. A satellite mission capable of measuring aboveground woody biomass could help reduce these uncertainties by delivering three products. First, a global map of aboveground woody biomass density would halve the uncertainty of estimated carbon emissions from land use change. Second, an annual, global map of natural disturbances could define the unknown but potentially large proportion of the residual terrestrial sink attributable to biomass recovery from such disturbances. Third, direct measurement of changes in aboveground biomass density (without classification of land cover or carbon modeling) would indicate the magnitude and distribution of at least the largest carbon sources (from deforestation and degradation) and sinks (from woody growth). The information would increase our understanding of the carbon cycle, including better information on the magnitude, location, and mechanisms responsible for terrestrial sources and sinks of carbon. This paper lays out the accuracy, spatial resolution, and coverage required for a satellite mission that would generate these products.