Suppression of Parasitic Peaks on CFOSAT SWIM Wave Spectra Based on a Specific Parametric Method

Parasitic peaks are observed in the low wavenumber regions of Surface Waves Investigation and Monitoring (SWIM) wave height spectra. They can be attributed to random fluctuations in the wave spectra caused mainly by speckle noise, compromising the quality of SWIM wave spectra, or can be attributed to a lack of homogeneity over the SWIM footprint. Some recent studies have proposed methods to suppress parasitic peaks: unfortunately, they are intended only for one-dimensional wave spectra, or they lack validation of the quality of wave spectra. In this study, a specific parametric method is proposed to suppress parasitic peaks in two-dimensional wave spectra in order to solve these problems. The parametrized wave spectra are derived by integrating multiple empirical spectra with directional functions, and a cost function is formulated to identify the most suitable parametrized wave spectrum. Subsequently, the quality and wave parameters of the most suitable parametrized wave spectrum are derived. It should be pointed out that the parametric method relies on the wave products provided by SWIM for empirical spectral fitting, so it cannot solve the 180° ambiguity problem. The results show that the specific parametric method effectively suppresses parasitic peaks in the low wavenumber regions while preserving wave information in SWIM wave height spectra. Additionally, the specific parametric method enhances the accuracy of the wave parameters of SWIM data, including significant wave height, dominant wavelength, and dominant wave direction.

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Publication Details

Journal
Remote Sensing
Published
2025-12-25
DOI
https://doi.org/10.3390/rs18010077
Primary Topic
Seismic Waves and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Suppression of Parasitic Peaks on CFOSAT SWIM Wave Spectra Based on a Specific Parametric Method

Xiuzhong LI, Bosen Jiang, Jingwei Gu, B. Liu et al.
Remote Sensing
Seismic Waves and Analysis
article

Suppression of Parasitic Peaks on CFOSAT SWIM Wave Spectra Based on a Specific Parametric Method

Xiuzhong LI, Bosen Jiang, Jingwei Gu, B. Liu, Shuyan Lang, Yijun He
article en

Abstract

Parasitic peaks are observed in the low wavenumber regions of Surface Waves Investigation and Monitoring (SWIM) wave height spectra. They can be attributed to random fluctuations in the wave spectra caused mainly by speckle noise, compromising the quality of SWIM wave spectra, or can be attributed to a lack of homogeneity over the SWIM footprint. Some recent studies have proposed methods to suppress parasitic peaks: unfortunately, they are intended only for one-dimensional wave spectra, or they lack validation of the quality of wave spectra. In this study, a specific parametric method is proposed to suppress parasitic peaks in two-dimensional wave spectra in order to solve these problems. The parametrized wave spectra are derived by integrating multiple empirical spectra with directional functions, and a cost function is formulated to identify the most suitable parametrized wave spectrum. Subsequently, the quality and wave parameters of the most suitable parametrized wave spectrum are derived. It should be pointed out that the parametric method relies on the wave products provided by SWIM for empirical spectral fitting, so it cannot solve the 180° ambiguity problem. The results show that the specific parametric method effectively suppresses parasitic peaks in the low wavenumber regions while preserving wave information in SWIM wave height spectra. Additionally, the specific parametric method enhances the accuracy of the wave parameters of SWIM data, including significant wave height, dominant wavelength, and dominant wave direction.

Remote SensingVol. 18(1)
Nanjing University of Information Science and Technology (CN), Ministry of Natural Resources (CN), National Satellite Ocean Application Service (CN), Sanya University (CN), China Central Television (CN), State Key Laboratory of Remote Sensing Science (CN)
National Natural Science Foundation of China, Nanjing University of Information Science and Technology, Nanjing University
Life below water
Openalex Percentile: Top 30%
Seismic Waves and Analysis
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