Abstract
The NASA Scatterometer (NSCAT) to be launched in August 1996 is designed to measure wind vectors over ice-free oceans. To prevent contamination of the wind measurements by the presence of sea ice, an algorithm based on only NSCAT data is described. Results are presented for a neural network trained using dual linear polarized Ku-band backscatter measured by the SeaSat-A Satellite Scatterometer. These results demonstrate the utility of neural network classifiers to provide this ice flag. Results are presented for both multilayer perceptron and a learning vector quantization neural networks. Classification skill is evaluated by comparisons with surface truth and with an independent ice-flagging algorithm. (Author)