Enhanced neural-network based sea/ice discrimination using ERS scatterometer data

Although other uses have emerged, ERS scatterometer data is operationally used to measure wind speed at the surface of the oceans. The wind speed and direction can indeed be inverted from the measured backscattering coefficients provided the measurements were performed over sea. While a land-mask can be used to reject measurements made over land, operational constraints make the use of an externally-provided ice-mask unpractical. It is thus desirable to discriminate between measurements made over sea and measurements made over ice using the backscattering coefficients alone. Due to operational constraints, a temporal averaging of the measurements is not feasible. Several methods have been proposed to discriminate between sea and ice. These are based on measuring the distance between the measurements made and a model. An ice model and a wind model are available. Measurements located far from the ice model were most likely not performed over ice and similarly, measurements close to the wind model were most likely performed over sea. However, for particular values of the incidence angles, these models are very close to each other, which leads to classification errors. In this paper, we propose to enhance the criterion of the distance to the wind model by taking into account the wind direction. This permits a better discrimination between ice- and sea-measurements. The enhanced criterion is implemented using a neural-network. The other methods proposed in the literature are also implemented in the same neural-network framework, which permits an easy comparison of their relative performances. Finally, the various methods are combined in a Bayesian framework.