Status of remote sensing algorithms for estimation of land surface state parameters

Ecosystem process, biosphere-atmosphere transfer, and carbon exchange models all require parameterization of the land surface, including land vegetation cover and soil moisture. Although not yet a demonstrated global capability, the most feasible method for obtaining these parameters and updating them periodically, is satellite remote sensing. In this paper we will summarize our understanding of the desired land surface parameters, including soil moisture, and provide an assessment of the state of the art of surface state remote sensing algorithms to infer those parameters on a global basis. we will consider a) modeling requirements for land cover parameters, including vegetation community composition and biophysical parameters, for example, leaf area index (LAI), biomass density, fraction of photosynthetically active radiation (Fear) absorbed by the vegetated land surface, and b) modeling requirements for soil moisture. l then review the status of remote sensing algorithms for obtaining these parameters and examine a number of issues involved in the global implementation and testing of these algorithms. Finally, we will look at future needs to make global mapping of land cover parameters a reality.