SWIMSAT: A Real-Aperture Radar to Measure Directional Spectra of Ocean Waves from Space—Main Characteristics and Performance Simulation

Hauser, Danièle; Soussi, Elbatoul; Thouvenot, Éric; Rey, L.

doi:10.1175/1520-0426(2001)018<0421:sarart>2.0.co;2

Cited by 66 publications

(51 citation statements)

References 22 publications

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“…After spectral estimation, the sea wave spectra can be acquired [41]. However, in extreme wind conditions the information of ocean water fluctuation at the bottom cannot be acquired by the Ku-band EM waves because the relatively "smooth" surface backscatters EM energy and thus no fluctuation signals can be used to retrieve the ocean water fluctuation.…”

Section: Cfosatmentioning

confidence: 99%

Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions

Zhang

Mouche

et al. 2017

Remote Sensing

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This paper reports Ku-band normalized radar cross section (NRCS) at low incidence angles ranging from 0 • to 18 • and in the wind speed range from 6 to 70 m/s. The precipitation radar onboard the tropical rainfall measuring mission and Jason-1 and 2 have provided 152 hurricanes observations between 2008 and 2013 that were collocated with stepped-frequency microwave radiometer measurements. It is found that the NRCS decreases with increasing incidence angle. The decrease is more dramatic in the 40-70 m/s range of wind speeds than in the 6-20 m/s range, indicating that the NRCS is very sensitive to low incidence angles under extreme wind conditions and insensitive to the extreme wind speed. Consequently, the sea surface appears relatively "smooth" to Ku-band electromagnetic microwaves. This phenomenon validates the observed drag coefficient reduction under extreme wind conditions, from a remote sensing viewpoint. Using the NRCS dependence on incidence angle under extreme wind conditions, we also present an empirical linear relationship between NRCS and incidence angles, which may assist future-satellites missions operating at small incidence angles to measure sea surface wind and wave field.

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Section: Cfosatmentioning

confidence: 99%

Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions

Zhang

Mouche

et al. 2017

Remote Sensing

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“…σ 0 modulated by long waves is generated by calculating m ( X , φ ) in Eq.4. Speckle noise and thermal noise are added to σ 0 employing the method used by Hauser et al (2001). To simulate the radar signal perturbed by speckle noise, we made a random selection from samples of a gamma function of six looks with a mean of m ( X , φ )+1 calculated using Eq.4.…”

Section: Direct Simulationmentioning

confidence: 99%

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

Chen

Yin²,

Huang³

2015

Chin. J. Ocean. Limnol.

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The principle of ocean wave spectrometers was fi rst 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 confi guration 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 coeffi cients 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.Keyword : slope probability density of the sea surface; ocean wave spectrometer; non-Gaussian; directional wave spectrum; energy error 1 INTRODUCTION

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“…Smearing imaging eff ects over a moving surface may cause a cutoff of wave information in the azimuth dimension. As a result, only waves with a wavelength longer than 150-200 m in the azimuth are detectable (Hauser et al, 2001). Therefore, only swell wave spectra can be retrieved directly from single-polarization SAR imagery (Lyzenga, 2002;Collard et al, 2005;European Space Agency, 2007) or multi-polarization SAR imagery (He et al, 2006;Zhang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Kuband and C-band airborne wave spectrometers have been developed, including the Radar Ocean Wave Spectrometer (ROWS) and RESSAC, later renamed STORM (Jackson, 1987;Hauser et al, 1992;Mouche et al, 2005). The Surface Wave Investigation and Monitoring from SATellite (SWIMSAT) mission, conceived and simulated based on instrument parameters, further supported the concept of spaceborne wave spectrometers (Hauser et al, 2001;Tison et al, 2009;Ren et al, 2010). The investigation on spaceborne wave spectrometers will continue with the Surface Wave Investigation and Monitoring (SWIM) system due to be launched as part of the Chinese French Ocean SATellite (CFOSAT) mission in 2018.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, observational data are used to estimate the sensitivity coeffi cient for airborne wave spectrometer data (Hauser et al, 1992;Ren et al, 2011). For spaceborne wave spectrometers, the signifi cant wave height (SWH) derived from the nadir beam is used to estimate the coeffi cient (Hauser et al, 2001). Because the nadir beam footprint is relatively narrow relative to the large area of the sea surface being observed, the coeffi cient estimated using this method might not correctly represent the energy of the entire observed area in inhomogeneous sea states.…”

Section: Introductionmentioning

confidence: 99%

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

Ren

Yang

Zheng

et al. 2015

Chin. J. Ocean. Limnol.

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This paper proposes a joint method to simultaneously retrieve wave spectra at diff erent scales from spaceborne Synthetic Aperture Radar (SAR) and wave spectrometer data. The method combines the output from the two diff erent sensors to overcome retrieval limitations that occur in some sea states. The wave spectrometer sensitivity coeffi cient is estimated using an eff ective 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.

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SWIMSAT: A Real-Aperture Radar to Measure Directional Spectra of Ocean Waves from Space—Main Characteristics and Performance Simulation

Cited by 66 publications

References 22 publications

Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions

Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions

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

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

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