A semiparametric algorithm to retrieve ocean wave spectra from synthetic aperture radar

Abstract

Abstract. A new wave retrieval method for the ERS synthetic aperture radar(SAR)wave mode is presented. The new algorithm, named semiparametric retrieval algorithm (SPRA), uses the full nonlinear mapping relations as proposed by Hasselmann and Hasselmann [1991]. It differs from previous retrieval algorithms in that it does not require a priori information on the sea state. Instead, it combines the observed SAR spectrum with the collocated wind vector from the ERS scatterometer to make an estimate of the wind sea spectrum. The residual signal in the SAR spectrum is interpreted as swell. The method has been validated by collocating over 5 years of SAR wave mode observations with spectral buoy measurements at 11 locations on the open ocean. For wave components longer than 225 m, the standard deviation between the retrieved spectra and buoy observations is 0.41 m, which corresponds to a relative RMS error of 29%. About 10% of the observed SAR spectra were rejected, in particular in light wind conditions when nonwave features such as those caused by slicks dominated the imagette. The bias and scatter in the results obtained under light wind conditions could be reduced by introducing a wind-dependent tilt modulation. This wind-dependent tilt formulation is derived from the empirical CMOD4 relation between the wind vector, the incidence angle, and the radar backscatter for the ERS scatterometer. BackgroundIn this paper we present a method that extracts spectral wave data from ERS synthetic aperture radar (SAR) observations. The ERS-1 and ERS-2 satellites (launched in 1991 and 1995, respectively) have gathered a few million SAR spectra over the open ocean. The retrieval algorithm described here was developed to exploit this unique data set, and use it to augment the description of the wave climate in remote areas.The way in which an ocean wave spectrum is mapped onto a SAR spectrum is reasonably well understood. The method described in this paper does not require a priori knowledge of the sea state to retrieve an ocean wave spectrum from a SAR observation. Instead, the fact that on the ERS satellites the scatterometer is operated simultaneously with the SAR wave mode is ex- Hasselmann and

Ocean Wave Parameters Retrieval from Sentinel-1 SAR Imagery

“…Referring to the achievements of several studies for wave retrieval from C-band SAR data, the standard deviation (SDE) of Hs ranges from 0.4 to 0.7 m against in situ buoy measurements or numeric wave model results [8,[12][13][14]21]. The semiempirical algorithm herein is expected to work well for various types of C-band SAR data.…”

“…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]. They all rely on the first-guess wave spectra, which can be obtained from numeric ocean wave models or be calculated from parametric functions, such as the Jonswap function [15].…”

Comparison of the two-dimensional directional wave spectra retrieved from spaceborne synthetic aperture radar images using the MPI scheme against directional buoy measurements

“…Ignoring such nonlinearities by applying a quasi-linear approximation might cause spurious swell peaks when the SAR image spectrum is mapped back into the wave spectrum (see Hasselmann et al, 1985). The Semi-Parametric Retrieval Algorithm (SPRA), the third retrieval scheme, was proposed by Mastenbroek and de Valk (2000), who employ additionally the wind information from the scatterometer that is operating simultaneously with the SAR. In this approach there is no need for a firstguess wave spectrum since they apply a parameterised wind sea spectrum and estimate its direction of propagation from the wind measurements.…”

A new method for significant wave height retrieval from ScanSAR mode imagery

“…Then any wave parameters like significant wave height (SWH) can be extracted from the spectra [1][2][3][4][5][6]. The above method is complex and only aims at the imagery having wave stripes.…”