A Review of Satellite Data Algorithms for Studies of the Land Surface

Abstract

Satellite observations are essential for the global monitoring of climatologically significant interactions between the earth's atmosphere and land surface. In practice, however, interpretation of remote-sensing data requires the use of algorithms?specialized, semi-empirical relationships that connect observed radiances with the actual physical variables needed for climate studies and modeling. At issue is the physical/empirical basis for these algorithms, their effectiveness and shortcomings, and the scope for further improvement. The International Satellite Land-Surface Climatology Project (ISLSCP) Satellite Data Algorithms Workshop, conducted 5?8 January 1987 at the Jet Propulsion Laboratory in Pasadena, California, was organized to address these questions. The introduction to this paper describes ISLSCP and presents an overview of the scientific topics covered in the workshop. Derivation of the following key surface parameters from satellite data are discussed in detail: 1) Insulation; 2) Albedo; 3) Upward longwave flux and land-surface temperature; 4) Downward longwave flux; 5) Surface net radiation; 6) Vegetation index (optical radiometry); and 7) Soil moisture and vegetation cover (microwave radiometry) Shortcomings of the various algorithms and actions required to alleviate them are discussed.