Neural network approach to the determination of the geophysical model function of the ERS-1 C-band spaceborne radar scatterometer
Alhumaidi, Sami M.
Jones, W. Linwood
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Geophysical Model Functions (GMF) describing the relationship between the scatterometer normalized radar cross section (sigma-0) and useful geophysical parameters such as sea-surface wind vectors, wave heights, and sea- surface temperatures have been undergoing extensive research and development during the last decade. In this study, we investigate the use of two feed-forward neural networks, Multilayer Perceptron and Radial Basis Functions, for developing a useful and accurate representation of the C- band GMF. Collocated radar sigma-0 cells with global wind vector models were used as the database of the study. The resulting well-known biharmonic relationship between the sigma-0 and the relative azimuth angle between the scatterometer antenna beam azimuth and wind direction shows the excellent agreement between the neural network and previous results. The applicability of the neural techniques in this application are clearly presented and the potential for possible enhancement over previous approaches are discussed.