Information Fusion for Spaceborne GNSS-R Sea Surface Height Retrieval Using Modified Residual Multimodal Deep Learning Method Article Swipe
Qiang Wang
,
Wei Zheng
,
Fan Wu
,
Huizhong Zhu
,
Aigong Xu
,
Yifan Shen
,
Yelong Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15061481
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15061481
Traditional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) sea surface height (SSH) retrieval methods have the disadvantages of complicated error models, low retrieval accuracy, and difficulty using full DDM information. To compensate for these deficiencies while considering the heterogeneity of the input data, this paper proposes an end-to-end Modified Residual Multimodal Deep Learning (MRMDL) method that can utilize the entire range of DDM information. First, the MRMDL method is constructed based on the modified Residual Net (MResNet) and Multi-Hidden layer neural network (MHL-NN). The MResNet applicable to DDM structures is used to adaptively capture productive features of the full DDM and to convert the two-dimensional DDM data into one-dimensional numerical form. Then, the extracted features and auxiliary parameters are fused as the input data for MHL-NN to retrieve the SSH. Second, the reliability of the model is verified using SSH with tide-corrected DTU Sea Surface Height 18 (DTU18) and spaceborne radar altimeters (Jason3, HY-2C, HY-2B). Compared to the SSH provided by the DTU18 validation model and the spaceborne radar altimeter, the Pearson correlation coefficients (PCC) are 0.98 and 0.97, respectively. However, the CYGNSS satellite is not primarily employed for ocean altimetry, and the mean absolute differences (MAD) are 3.92 m and 4.32 m, respectively. Finally, the retrieval accuracy of the MRMDL method and the HALF retracking approach are compared and analyzed. Finally, this study also implements the HALF retracking algorithm to derive the SSH, and the results are compared with those computed by the MRMDL method. The MRMDL method is more accurate than the HALF retracking approach according to MAD, Root-Mean-Square Error (RMSE), and PCC, with an improvement of 35.21%, 17.25%, and 2.08%, respectively. The MRMDL method will contribute a new theoretical and methodological reference for future GNSS-R altimetry satellites with high spatiotemporal SSH retrieval.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15061481
- https://www.mdpi.com/2072-4292/15/6/1481/pdf?version=1678182233
- OA Status
- gold
- Cited By
- 11
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323567916
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4323567916Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs15061481Digital Object Identifier
- Title
-
Information Fusion for Spaceborne GNSS-R Sea Surface Height Retrieval Using Modified Residual Multimodal Deep Learning MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-07Full publication date if available
- Authors
-
Qiang Wang, Wei Zheng, Fan Wu, Huizhong Zhu, Aigong Xu, Yifan Shen, Yelong ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15061481Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/15/6/1481/pdf?version=1678182233Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/15/6/1481/pdf?version=1678182233Direct OA link when available
- Concepts
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Residual, GNSS applications, Computer science, Altimeter, Remote sensing, Sea-surface height, Satellite, Artificial intelligence, Algorithm, Global Positioning System, Geology, Engineering, Aerospace engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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