A Novel Approach to SAR Ocean Wind Retrieval

Abstract

A novel approach to a Bayesian ocean wind retrieval from high-range bandwidth synthetic aperture radar (SAR) data is demonstrated and validated using global Sentinel-1 (S1) A and B WV data acquired in October 2016 and January 2017. These spectral parameters are defined from the full-resolution image cross spectra. The first parameter is the integral spectral value (ISV) defined as the signed spectral energy at high-range wavenumber. Two other parameters, the azimuth phase plane slope (APPS) and range phase plane slope (RPPS), are the slope of the phase plane from the image cross spectra. Together with the normalized radar cross section (NRCS), these parameters form the input to our data-driven model for ocean wind retrieval. The model is trained on S1B from October 2016 data and validated on S1A and S1B from January 2017 data colocated with European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric wind model as “ground” truth. The APPS proves to be the result of two sinusoidal functions, one symmetric and one antisymmetric, the antisymmetric part is in direct relation with the azimuth wind direction. Our Bayesian model achieves standard deviations of 1.73 m/s and 49.26° for January 2017 S1B data set with a bias of 0.03 m/s and −1.55°, corresponding results for January 2017 S1A data were 1.79 m/s and 49.95° with biases 0.41 m/s and −1.89°. Including data with ECMWF wind speed above 7 m/s we achieve standard deviations of 1.81 m/s and 33.16° with biases 0.1 m/s and −1.31° for the January 2017 S1B data set.