Sea surface slopes’ PDF from GNSS reflected signals

We present a new technique to analyze L-band signals of the Global Navigation Satellite System (GNSS) that have rebounded off the sea surface, with the aim of retrieving information about surface roughness in the form of the Probability Density Function (PDF) of the slopes. Unlike earlier techniques, which parameterize the PDF (usually as normal bivariate distributions), this approach does not constrain the surface slopes' PDF to the shape of a particular analytical distribution. After validating the algorithm by means of end- to-end simulations, we have applied it to real data. The tests on real data show that the retrievals are robust, consistent with the results obtained with standard GNSS-reflection techniques, and in agreement with independent sources of information. Moreover, the retrieved slopes' PDF present non-Gaussian features, such as skewness, that maps with the up-/down- asymmetries introduced by surface forces. This is the first time that GNSS-reflections have sensed and identified the up- and down-wind signature on the surface.

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