An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)algorithm.The research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,respectively.These results demonstrate the model’s effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model’s superior inversion accuracy over other methods.

A joint method to retrieve directional ocean wave spectra from SAR and wave spectrometer data

This paper proposes a joint method to simultaneously retrieve wave spectra at dif ferent scales from spaceborne Synthetic Aperture Radar(SAR) and wave spectrometer data. The method combines the output from the two dif ferent sensors to overcome retrieval limitations that occur in some sea states. The wave spectrometer sensitivity coeffi cient is estimated using an ef fective signifi cant wave height(SWH), which is an average of SAR-derived and wave spectrometer-derived SWH. This averaging extends the area of the sea surface sampled by the nadir beam of the wave spectrometer to improve the accuracy of the estimated sensitivity coeffi cient in inhomogeneous sea states. Wave spectra are then retrieved from SAR data using wave spectrometer-derived spectra as fi rst guess spectra to complement the short waves lost in SAR data retrieval. In addition, the problem of 180° ambiguity in retrieved spectra is overcome using SAR imaginary cross spectra. Simulated data were used to validate the joint method. The simulations demonstrated that retrieved wave parameters, including SWH, peak wave length(PWL), and peak wave direction(PWD), agree well with reference parameters. Collocated data from ENVISAT advanced SAR(ASAR), the airborne wave spectrometer STORM, the PHAROS buoy, and the European Centre for Medium-Range Weather Forecasting(ECMWF) were then used to verify the proposed method. Wave parameters retrieved from STORM and two ASAR images were compared to buoy and ECMWF wave data. Most of the retrieved parameters were comparable to reference parameters. The results of this study show that the proposed joint retrieval method could be a valuable complement to traditional methods used to retrieve directional ocean wave spectra, particularly in inhomogeneous sea states.

Effect of non-Gaussian properties of the sea surface on the low-incidence radar backscatter and its inversion in terms of wave spectra by an ocean wave spectrometer

The principle of ocean wave spectrometers was first presented several decades ago to detect the directional wave spectrum with real-aperture radar(Jackson,1981). To invert wave spectra using an ocean wave spectrometer,for simplicity,the hydrodynamic forcing and wave-wave interaction effect are neglected and a Gaussian slope probability density function(pdf) is used to calculate the normalized backscattering cross-section( σ 0) of the ocean surface. However,the real sea surface is non-Gaussian. It is not known whether the non-Gaussian property of the sea surface will affect the performance of the inversion of the wave spectrum if following existing inversion steps and methods. In this paper,the pdf of the sea surface slope is expressed as a Gram-Charlier fourth-order expansion,which is quasi-Gaussian. The modulation transfer function(MTF) is derived for a non-Gaussian slope pdf. The effects of non-Gaussian properties of the sea surface slope on the inversion process and result are then studied in a simulation of the SWIM(Surface Waves Investigation and Monitoring) instrument configuration to be used on the CFOSAT(China-France Oceanography Satellite) mission. The simulation results show that the mean trend of σ 0 depends on the sea slope pdf,and the peakedness and skewness coefficients of the slope pdf affect the shape of the mean trend of σ 0 versus incidence and azimuth; owing to high resolution of σ 0 in the range direction,MTF obtained using the mean trend of σ 0 is almost as accurate as that set in the direct simulation; in the inversion,if ignoring the non-Gaussian assumption,the inversion performances for the wave spectrum decrease,as seen for an increase in the energy error of the inverted wave slope spectrum. However,the peak wavelength and wave direction are the same for inversions that consider and ignore the non-Gaussian property.