View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics.
Authors: Jiang, Zhiyang1,2 (AUTHOR), Hu, Tong1,2 (AUTHOR), Ren, Lin1,2,3 (AUTHOR) renlin210@sio.org.cn, Jia, Yongjun4 (AUTHOR), Dong, Xiao5 (AUTHOR), Zhang, Yinquan1,2 (AUTHOR), Zhang, Yi2,4 (AUTHOR), Cui, Limin3,4 (AUTHOR), Wang, Yiqi1,4 (AUTHOR), Han, Han1,5 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 17, p2899. 22p.
Subjects: Backscattering, Radar interferometry, Multilayer perceptrons, Radar equipment, Telecommunication satellites, Ocean surface topography, Machine learning
Abstract: Highlights: What are the main findings? The proposed method integrating backscattering and interferometric features achieves favorable SWH retrieval accuracy. Compared with the operational KaRIn L2 SWH product, it reduces the RMSE by approximately 0.17 m, as validated against collocated ECMWF data. What are the implications of the main findings? The retrieved SWH can be used to correct the sea state bias of sea surface height measurements obtained by the SWOT KaRIn, as well as to supplement wave products derived from other satellite sensors. 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 197043749
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Zhiyang%22">Jiang, Zhiyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Tong%22">Hu, Tong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Lin%22">Ren, Lin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> renlin210@sio.org.cn</i><br /><searchLink fieldCode="AR" term="%22Jia%2C+Yongjun%22">Jia, Yongjun</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Xiao%22">Dong, Xiao</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yinquan%22">Zhang, Yinquan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yi%22">Zhang, Yi</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Limin%22">Cui, Limin</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yiqi%22">Wang, Yiqi</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Han%22">Han, Han</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 17, p2899. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Backscattering%22">Backscattering</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+equipment%22">Radar equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+satellites%22">Telecommunication satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+surface+topography%22">Ocean surface topography</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The proposed method integrating backscattering and interferometric features achieves favorable SWH retrieval accuracy. Compared with the operational KaRIn L2 SWH product, it reduces the RMSE by approximately 0.17 m, as validated against collocated ECMWF data. What are the implications of the main findings? The retrieved SWH can be used to correct the sea state bias of sea surface height measurements obtained by the SWOT KaRIn, as well as to supplement wave products derived from other satellite sensors. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=197043749
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18172899
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 2899
    Subjects:
      – SubjectFull: Backscattering
        Type: general
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Multilayer perceptrons
        Type: general
      – SubjectFull: Radar equipment
        Type: general
      – SubjectFull: Telecommunication satellites
        Type: general
      – SubjectFull: Ocean surface topography
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: SWH Retrieval from SWOT KaRIn Data by Combining Backscattering and Interference Characteristics.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jiang, Zhiyang
      – PersonEntity:
          Name:
            NameFull: Hu, Tong
      – PersonEntity:
          Name:
            NameFull: Ren, Lin
      – PersonEntity:
          Name:
            NameFull: Jia, Yongjun
      – PersonEntity:
          Name:
            NameFull: Dong, Xiao
      – PersonEntity:
          Name:
            NameFull: Zhang, Yinquan
      – PersonEntity:
          Name:
            NameFull: Zhang, Yi
      – PersonEntity:
          Name:
            NameFull: Cui, Limin
      – PersonEntity:
          Name:
            NameFull: Wang, Yiqi
      – PersonEntity:
          Name:
            NameFull: Han, Han
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 17
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1