A new technique to sense non-Gaussian features of the sea surface from L-band bi-static GNSS reflections

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 effective 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. This may help to understand the real information content of L-band scattered signals, which is currently unclear. 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. A more in-depth analysis to check whether the skewness is a geophysical signature or an artifact of the technique, shows that it maps with the up-/down-asymmetries introduced by surface forces. In particular, this is the first time that GNSS-reflections have sensed and identified the up- and down-wind signature on the surface, putting and end to the 180° ambiguity that was usually attributed to GNSS observations of directional roughness.