Regression techniques for oceanographic parameter retrieval using space-borne microwave radiometry

Abstract

Variations of conventional multiple regression techniques are applied to the problem of remote sensing of oceanographic parameters from space. The techniques are specifically adapted to the scanning multichannel microwave radiometer (SMRR) launched on the Seasat and Nimbus 7 satellites to determine ocean surface temperature, wind speed, and atmospheric water content. The retrievals are studied primarily from a theoretical viewpoint, to illustrate the retrieval error structure, the relative importances of different radiometer channels, and the tradeoffs between spatial resolution and retrieval accuracy. Comparisons between regressions using simulated and actual SMMR data are discussed; they show similar behavior.