A neural network sea ice edge classifier for the NASA Scatterometer

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 (SASS). These results demonstrate the utility of neural network classifiers to provide this ice flag. Results are presented for both multilayer perceptron (MLP) and a learning vector quantization (LVQ) neural networks. Classification skill is evaluated by comparisons with surface truth and with an independent ice-flagging algorithm.