Dependence of mean square slope on wave state and its application in altimeter wind speed retrieval

Li, Shuiqing; Zhao, Dongliang; Zhou, Liangming; Liu, Bin

doi:10.1080/01431161.2012.713144

Cited by 17 publications

(13 citation statements)

References 32 publications

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“…The wave age dependence of WASV is thus dominated by the gravity wave spectrum component. This is consistent with some observational results, revealing that the wave age dependence for short waves is much weaker than for gravity waves (Li et al, 2013;Vandemark et al, 2004). Kudryavtsev et al (2003) suggested that the NRCS model significantly improves the radar backscatter intensity simulation by introducing a breaking wave scattering mechanism.…”

Section: Journal Of Geophysical Research: Oceanssupporting

confidence: 89%

Wind Wave Effects on Remote Sensing of Sea Surface Currents From SAR

Лиу

Shen

et al. 2020

JGR Oceans

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Wind waves contribute a significant component to the sea surface currents detected by synthetic aperture radar (SAR) systems via the orbital velocity; this component is typically referred to as the wind wave‐induced artifact surface velocity (WASV). Accordingly, it is essential to precisely describe the WASV effect, so that the real sea surface currents can be obtained from SAR measurements. In order to enhance our understanding about WASV, we carried out a numerical analysis of the sea state dependence of WASV under various radar configurations. The simulated WASV compares reasonably well with observations reported in the literature. It is found that WASV not only depends on the wind speed but also increases considerably with the development of the wind waves, as characterized by wave age. WASV may increase by two to three times, in fully developed wind wave conditions, as compared to young waves. The wave age dependence appears to largely account for the observed uncertainty in WASV at a given wind speed; moreover, swell is also found to significantly influence WASV through hydrodynamic modulation of wind wave properties. These effects are stronger at higher wind speeds and vary significantly with the different radar configurations that are possible, in terms of polarizations and incidence angles. The variations in WASV using different shortwave spectral functions and angular spreading functions are examined in this manuscript.

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Section: Journal Of Geophysical Research: Oceanssupporting

confidence: 89%

Wind Wave Effects on Remote Sensing of Sea Surface Currents From SAR

Лиу

Shen

et al. 2020

JGR Oceans

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“…Using the deep water dispersion relationship and the Jacobian operator as in [15], the MSS can be expressed using the direction-independent frequency spectrum, F ( f ), as follows [16]:…”

Section: Rationale Of the Retrieval Of Altimeter Wave Periodmentioning

confidence: 99%

Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

Wang

Ichikawa

2016

Remote Sensing

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Wave periods estimated from satellite altimetry data behave differently from those calculated from buoy data, especially in low-wind conditions. In this paper, the geometric mean wave period T a is calculated from buoy data, rather than the commonly used zero-crossing wave period T z . The geometric mean wave period uses the fourth moment of the wave frequency spectrum and is related to the mean-square slope of the sea surface measured using altimeters. The values of T a obtained from buoys and altimeters agree well (root mean square difference: 0.2 s) only when the contribution of high-frequency sea waves is estimated by a wavenumber spectral model to complement the buoy data, because a buoy cannot obtain data from waves having wavelengths that are shorter than the characteristic dimension of the buoy.

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“…The waveforms reflected from an inland water body might exhibit ocean-like shapes (winds blowing over lakes) or, most commonly, a peakier shape (smoother surfaces) for small water bodies. To adapt to the latter ones, the MSS, describing the sea surface roughness [66], was used instead of the SWH as a fitting parameter; this approach is initially considered in [59], when exploiting the SAMOSA model over Tibetan plateau lakes.…”

Section: Offset Center Of Gravity (Ocog) Retrackermentioning

confidence: 99%

“…The latter was integrated in a two-step fitting procedure, exploiting both its normal operation over ocean-like waveforms, and the adaptation to peaky-like waveforms, expected from inland waters. For the second typology, the mean squared slope (MSS) describing the sea surface roughness [66] was used as a fitting parameter instead of the significant wave height (SWH), as proposed and initially evaluated in [59] over Tibetan lakes when using SAMOSA model. (In isardSAT retracker, a rough surface of Gaussian distributions is assumed to define the scattering model, such that the normalized radar cross section (NRCS) as a function of incidence angle is modeled as a Gaussian [67], where the MSS is a parameter defining the directivity of surface radiation pattern, i.e.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Retrackers’ Performances and Water Level Retrieval over the Ebro River Basin Using Sentinel-3

et al. 2019

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Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can, therefore, play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This study aimed to retrieve water levels from Level-1B Sentinel-3 data with focus on the minimization of the land contamination over small- to middle-sized water bodies (130 m to 4.5 km), where continuous clean waveforms rarely exist. Three specialized algorithms or retrackers, together with a new waveform portion selection method, were evaluated to minimize land contamination in the waveforms and to select the nadir return associated with the water body being overflown. The waveform portion selection method, with consideration of the Digital Elevation Model (DEM), was used to fit the multipeak waveforms that arise when overflying the continental water bodies, exploiting a subwaveform-based approach to pick up the one corresponding to the nadir. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Center of Gravity (OCOG) retracker and two-step SAR physical-based retracker, were compared. No significant difference was found in the results of the three retrackers. However, waveform portion selection using DEM information great improved the results. Time series of water levels were retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (approximately 1.8 km wide) and Ribarroja Reservoir (approximately 400 m wide), with unbiased root-mean-square errors (RMSEs) down to 0.28 m and 0.16 m, respectively, depending on the retracker.

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Dependence of mean square slope on wave state and its application in altimeter wind speed retrieval

Cited by 17 publications

References 32 publications

Wind Wave Effects on Remote Sensing of Sea Surface Currents From SAR

Wind Wave Effects on Remote Sensing of Sea Surface Currents From SAR

Effect of High-Frequency Sea Waves on Wave Period Retrieval from Radar Altimeter and Buoy Data

Analysis of Retrackers’ Performances and Water Level Retrieval over the Ebro River Basin Using Sentinel-3

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