An empirical approach for the retrieval of integral ocean wave parameters from synthetic aperture radar data

Abstract

[1] In this study a new empirical approach to retrieve integral ocean wave parameters from synthetic aperture radar (SAR) data is presented. The idea behind this computationally efficient technique is to estimate integral ocean wave parameters without the intermediate step of retrieving the two-dimensional ocean wave spectrum. The method has the radiometrically calibrated SAR image as the only source of information and is based on a quadratic model function with 22 input parameters. These parameters include the radar cross section, the image variance, and 20 parameters computed from the SAR image variance spectrum using a set of orthonormal functions. The coefficients of the quadratic function were fitted for the estimation of H s , the mean periods T m01 , T m02 , T À10 , the wave power, and the wave heights associated with different spectral bands. The fit procedure is based on a stepwise regression method. A data set of 12,000 globally distributed ERS-2 wave mode image spectra and colocated WAM ocean wave spectra was available for the study. Two separate subsets of 6000 collocation pairs each were used to fit the model and to carry out comparisons of the retrieved wave parameters with numerical model results. Additional comparisons were performed using NDBC buoy measurements. Scatterplots and global maps with the derived parameters are presented. It is shown that the rms of the SAR derived H s with respect to the WAM H s is about 0.5 m. For the mean period T mÀ10 an rms of 0.72 s with a high-frequency cutoff period of about 6 s is achieved.

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

“…Thus, NRCS can represent information on wind sea (short wave roughness) and co-polarized (VV) NRCS has been introduced into SAR empirical SWH algorithms [19,20,23]. Besides, C-band cross-polarization (VH or HV) has demonstrated good performance to extract high winds even in hurricanes when saturation was observed in co-polarization i.e., [46,47].…”

“…The normalized variance which is used for the imagette inhomogeneity rejection (see Section 2.1) is also found to contain the longer wave information caused by the swell modulation [19,20,23]. Here, following the existing empirical SAR SWH models, normalized variance computed from VV polarized GF-3 WM imagettes, denoted as cvar_vv here, is selected as another input for the QPCWAVE_GF3 empirical model.…”

“…Accordingly, the SWH retrieval algorithm QPCWAVE_GF3 proposed here includes the cross-polarized NRCS, which have been not taken into account for the empirical models dedicated to single polarization SAR data i.e., [19,20,23], or even Radarsat-2 SAR images with quad-polarization [28]. Therefore, it is interesting to discuss the contribution of cross-polarized NRCS to our proposed empirical SWH retrieval algorithm.…”

Ocean Wave Parameters Retrieval from Sentinel-1 SAR Imagery

“…And then Hs is calculated from SAR-derived cutoff wavelength λ c , radar incidence angle θ and peak wave direction relative to range direction ϕ by using Equation (6). Finally, T mw is calculated by using Equation (16 The block diagram of the scheme of semi-empirical algorithm for wave parameters retrieval based on cutoff wavelength estimation is shown in Figure 7. First, the two-dimensional SAR image spectra is calculated from NRCS by using two-dimensional FFT method.…”

“…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]. The second type includes empirical algorithms, such as CWAVE_ERS [16], CWAVE_ENVI [17]. Although 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.…”