Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning
A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the...
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MDPI AG
2025-06-01
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| author | Kai Sun Jianjun Liang Xiao-Ming Li Jie Pan |
| author_facet | Kai Sun Jianjun Liang Xiao-Ming Li Jie Pan |
| author_sort | Kai Sun |
| collection | DOAJ |
| description | A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. |
| format | Article |
| id | doaj-art-23cbecbaef3a40e2853a3ebebcbc7e88 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-23cbecbaef3a40e2853a3ebebcbc7e882025-08-20T03:16:47ZengMDPI AGRemote Sensing2072-42922025-06-011713213310.3390/rs17132133Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep LearningKai Sun0Jianjun Liang1Xiao-Ming Li2Jie Pan3Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Wenchang 571333, ChinaKey Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Wenchang 571333, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaA reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies.https://www.mdpi.com/2072-4292/17/13/2133synthetic aperture radar (SAR)Doppler oceanographyocean surface current (OSC)deep learning |
| spellingShingle | Kai Sun Jianjun Liang Xiao-Ming Li Jie Pan Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning Remote Sensing synthetic aperture radar (SAR) Doppler oceanography ocean surface current (OSC) deep learning |
| title | Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning |
| title_full | Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning |
| title_fullStr | Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning |
| title_full_unstemmed | Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning |
| title_short | Retrieval of Ocean Surface Currents by Synergistic Sentinel-1 and SWOT Data Using Deep Learning |
| title_sort | retrieval of ocean surface currents by synergistic sentinel 1 and swot data using deep learning |
| topic | synthetic aperture radar (SAR) Doppler oceanography ocean surface current (OSC) deep learning |
| url | https://www.mdpi.com/2072-4292/17/13/2133 |
| work_keys_str_mv | AT kaisun retrievalofoceansurfacecurrentsbysynergisticsentinel1andswotdatausingdeeplearning AT jianjunliang retrievalofoceansurfacecurrentsbysynergisticsentinel1andswotdatausingdeeplearning AT xiaomingli retrievalofoceansurfacecurrentsbysynergisticsentinel1andswotdatausingdeeplearning AT jiepan retrievalofoceansurfacecurrentsbysynergisticsentinel1andswotdatausingdeeplearning |