SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics

This study focuses on the Significant Wave Height (SWH) retrieval from the Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite by combining backscattering and interference characteristics. To this end, the backscattering-related and interference-related parameters were jointly used as inputs to develop a machine learning model. Here, the backscattering-related data include normalized radar cross-section (NRCS), incidence angle, and the image spectra parameters extracted from KaRIn Level 1B (L1B) data, while the interference-related data correspond to the Level 2 (L2) volumetric correlation, which characterizes the influence of ocean wave scattering on interferometric coherence. The machine learning model is built upon a Multi-Layer Perceptron (MLP), which serves as a nonlinear fitting tool. SWH retrievals from the proposed method and the existing L2 SWH product as a reference were validated by the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, Haiyang2C (HY2C) and Haiyang2D (HY2D) altimeter data, and National Data Buoy Center (NDBC) buoy data. Validations show that both KaRIn SWH have a good agreement with collocations in terms of correlation coefficient (COR), BIAS and root mean square error (RMSE). Moreover, the retrieval accuracy from the proposed method (with an RMSE of about 0.29 m) is better than that of the L2 product (with an RMSE of about 0.46 m) when validated against the collocated ECMWF datasets. Ablation analysis further confirms that image spectra parameters and volumetric correlation are the dominant factors driving the retrieval accuracy improvement, with notable contribution differences among the sub-parameters of spectral features. This performance gain arises from the complementary physical mechanisms of backscattering and interferometric observables, which describe sea state information from independent dimensions. These accurate SWH retrievals can help correct sea state biases for collocated KaRIn sea surface height products and complement wave products from other satellite sensors.

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Publication Details

Journal
Remote Sensing
Published
2026-08-27
DOI
https://doi.org/10.3390/rs18172899
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

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article

SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics

Xiao Dong, Yongjun Jia, T Hu, Zhiyang Jiang et al.
Remote Sensing
Ocean Waves and Remote Sensing
article

SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics

Xiao Dong, Yongjun Jia, T Hu, Zhiyang Jiang, Yiqi Wang, Yi Zhang, Limin Cui, Lin Ren, Han Han, Yinquan Zhang
article en

Abstract

This study focuses on the Significant Wave Height (SWH) retrieval from the Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite by combining backscattering and interference characteristics. To this end, the backscattering-related and interference-related parameters were jointly used as inputs to develop a machine learning model. Here, the backscattering-related data include normalized radar cross-section (NRCS), incidence angle, and the image spectra parameters extracted from KaRIn Level 1B (L1B) data, while the interference-related data correspond to the Level 2 (L2) volumetric correlation, which characterizes the influence of ocean wave scattering on interferometric coherence. The machine learning model is built upon a Multi-Layer Perceptron (MLP), which serves as a nonlinear fitting tool. SWH retrievals from the proposed method and the existing L2 SWH product as a reference were validated by the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, Haiyang2C (HY2C) and Haiyang2D (HY2D) altimeter data, and National Data Buoy Center (NDBC) buoy data. Validations show that both KaRIn SWH have a good agreement with collocations in terms of correlation coefficient (COR), BIAS and root mean square error (RMSE). Moreover, the retrieval accuracy from the proposed method (with an RMSE of about 0.29 m) is better than that of the L2 product (with an RMSE of about 0.46 m) when validated against the collocated ECMWF datasets. Ablation analysis further confirms that image spectra parameters and volumetric correlation are the dominant factors driving the retrieval accuracy improvement, with notable contribution differences among the sub-parameters of spectral features. This performance gain arises from the complementary physical mechanisms of backscattering and interferometric observables, which describe sea state information from independent dimensions. These accurate SWH retrievals can help correct sea state biases for collocated KaRIn sea surface height products and complement wave products from other satellite sensors.

Remote SensingVol. 18(17)
Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Second Institute of Oceanography (CN), National Space Science Center (CN), Ministry of Natural Resources (RW), National Satellite Ocean Application Service (CN), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN)
National Key Research and Development Program of China, Natural Science Foundation of Zhejiang Province
Life below water
Openalex Percentile: Top 13%
Ocean Waves and Remote Sensing
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