Non‐Gaussian effects in the two‐scale model for rough surface scattering

Nickolaev, N. I.; Yordanov, O.I.; Michalev, M.A.

doi:10.1029/92jc01492

Cited by 16 publications

(5 citation statements)

References 33 publications

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“…3. The estimated PDF's are similar to the Edge-Worth expansion, demonstrated experimentally, and theoretically in [28,[39][40][41][42][43][44][45][46], and deviate from the Gaussian model as the wind speed increases. …”

Section: Generation Of a Synthetic Non-gaussian Sea Surfacementioning

confidence: 54%

Numerical Computation of the Electromagnetic Bias in GNSS-R Altimetry

Ghavidel

Schiavulli

Camps

2016

IEEE Trans. Geosci. Remote Sensing

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In radar altimetry the electromagnetic (EM) bias is originated by the smaller reflectivity of wave crests than troughs, thus the average sea surface height is under-estimated. Bias uncertainty is currently the largest factor in altimetry error budgets. The EM bias in a bistatic forward-scattering configuration at L-band, such as in Global Navigation Satellite SystemsReflectometry (GNSS-R) altimetry, remains one of the major sources of uncertainty in the altimetry error budget. In this work the EM bias is computed using numerical simulations.To do so, a time-dependent synthetic non-Gaussian sea surface is created using the PiersonMoskowitz and Elfouhaily sea surface height spectra and spreading function. The sea surface is then discretized in facets and "illuminated" using a Right Hand Circular Polarization data at C-and Ku-bands. Then, the numerical model is applied at L-band, for bistatic configurations, including different azimuth angles, and different wind speeds. It is found that the EM bias is almost insensitive to the sea surface spectra selected and increases with increasing wind speed and incidence/scattering angle (up to ~20 cm at θi,s = 45° and U10 = 12 m/s), and it also exhibits a non-negligible azimuthal dependence, that must be accounted for in the error budgets of upcoming GNSS-R altimetry missions.

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Section: Generation Of a Synthetic Non-gaussian Sea Surfacementioning

confidence: 54%

Numerical Computation of the Electromagnetic Bias in GNSS-R Altimetry

Ghavidel

Schiavulli

Camps

2016

IEEE Trans. Geosci. Remote Sensing

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“…[41,42]. Nickolae et al found that a non-Gaussian distribution of sea surface height undulations can cause an asymmetry in sea surface electromagnetic scattering in upwind and downwind directions [43]. For GNSS-R applications, initially, Estel et al proposed a new algorithm to extract the sea surface slope PDF from the sea surface reflected GNSS signal from the KA-GO model using airborne experimental data to detect the ocean wind from the perspective of the sea surface slope PDF, and found that the inverse-performed PDF is non-Gaussian without presetting the shape distribution of the PDF, which can effectively solve the 180 • ambiguity problem of wind direction detection [6].…”

Section: Introductionmentioning

confidence: 99%

Scattering Properties of Non-Gaussian Ocean Surface with the SSA Model Applied to GNSS-R

et al. 2023

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Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging earth observation method for remote sensing of feature parameters using reflected signals from navigation satellites, and is a purely specular bistatic forward scattering observation means with special right-handed circular polarization incident wave. In this paper, the small-slope approximation model of non-Gaussian sea surface is used as the basis to construct the scattering model for the observation geometry of GNSS-R as well as the L-band characteristics, and the fully-polarization normalized bistatic radar scattering cross section (NBRCS) are simulated by the method of polarization synthesis to analyze the scattering characteristics under different wind speeds and directions on the ocean surface, which highlights the variation of NBRCS with wind direction, and the scattering modeling accuracy is improved by comparing with the data of CYGNSS. In addition, we adopt the observation geometry deviating from purely specular geometry, discuss the scattering azimuth angle, scattering influence, and the relative relationship between different polarizations of the scattering angle under the non-specular geometry.

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“…The first commonly used ASF was put forward by Longuet-Higgins [33] which is a cosine-shape parametric function which was improved by Mitsuyasu et al [34]. Over the following decades, continuous improvements were made by Donelan et al [25], Fung and Lee [22], Nickolaev et al [35], Apel et al [23] and Elfouhaily et al [21], etc. However, these ASFs have a different noncentrosymmetry, and different spreading functions have diversities of mathematical properties.…”

Section: An Improved Directional Spectrummentioning

confidence: 99%

An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band

Yang

Chen

et al. 2017

Remote Sensing

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L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel's omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions.

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Non‐Gaussian effects in the two‐scale model for rough surface scattering

Cited by 16 publications

References 33 publications

Numerical Computation of the Electromagnetic Bias in GNSS-R Altimetry

Numerical Computation of the Electromagnetic Bias in GNSS-R Altimetry

Scattering Properties of Non-Gaussian Ocean Surface with the SSA Model Applied to GNSS-R

An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band

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