Abstract
In this paper, a semi-empirical algorithm for significant wave height (Hs) and mean wave period (Tmw) retrieval from C-band VV-polarization Sentinel-1 synthetic aperture radar (SAR) imagery is presented. We develop a semi-empirical function for Hs retrieval, which describes the relation between Hs and cutoff wavelength, radar incidence angle, and wave propagation direction relative to radar look direction. Additionally, Tmw can be also calculated through Hs and cutoff wavelength by using another empirical function. We collected 106 C-band stripmap mode Sentinel-1 SAR images in VV-polarization and wave measurements from in situ buoys. There are a total of 150 matchup points. We used 93 matchups to tune the coefficients of the semi-empirical algorithm and the rest 57 matchups for validation. The comparison shows a 0.69 m root mean square error (RMSE) of Hs with a 18.6% of scatter index (SI) and 1.98 s RMSE of Tmw with a 24.8% of SI. Results indicate that the algorithm is suitable for wave parameters retrieval from Sentinel-1 SAR data.
Highlights
Space-borne synthetic aperture radar (SAR) has been used to detect wave information in a large coverage (10 × 10 km2 to 400 × 400 km2 ) with high spatial resolution
SAR data is available from C-band (5.3 GHz) Radarsat-2 and Sentinel-1; X-band (9.8 GHz) TerraSAR-X with its twins TanDEM-X, and Cosmo-SkyMed; and L-band (1.2 GHz) ALOS-2 satellites
The methodology of wave spectra retrieval needs a good understanding of complicated SAR wave imaging mechanisms typically explained by the two-scale model, including the tilt and hydrodynamic modulations [2] on sea surface short waves
Summary
Space-borne synthetic aperture radar (SAR) has been used to detect wave information in a large coverage (10 × 10 km to 400 × 400 km2 ) with high spatial resolution (up to 1 m). The first type includes theoretical-based algorithm, such as the Max-Planck Institute (MPI) [6,7], semi-parametric retrieval algorithm (SPRA) [8,9], parameterized first-guess spectrum method (PFSM) [10,11,12], and the partition rescaling and shift algorithm (PARSA) [13,14] The second type includes empirical algorithms, such as CWAVE_ERS [16], CWAVE_ENVI [17] These second-type algorithms do not require prior wind information from either SAR-derived or other sources, they only work for ERS-2 or Envisat-ASAR wave mode data.
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