Integrated retrieval of ocean surface wind and current fields from single-geometry SAR observations through a physically constrained Bayesian framework
Ocean surface winds and currents are coupled through air–sea interaction but are commonly retrieved independently from spaceborne synthetic aperture radar (SAR). For operational single-geometry SAR, simultaneous wind–current retrieval is underdetermined and remains reliant on background information. We propose a physically constrained Bayesian framework that combines radar backscatter and Doppler centroid anomaly measurements to retrieve 10-m wind and radial surface current. An empirical wind-drift relation softly constrains the radial-current increment relative to a product-derived background current, and the solution is obtained by weighted maximum a posteriori estimation. The framework is evaluated using multi-year Sentinel-1 observations collocated with scatterometer, buoy, and high-frequency radar measurements across different sea states. Parameters calibrated in a Gulf Stream-influenced U.S. East Coast region are fixed for temporally held-out events and an independent Southern California test. Across the East Coast data, wind-speed root-mean-square difference (RMSD) decreases by 0.59 − 0.77 m s − 1 relative to ERA5 atmospheric reanalysis. Against high-frequency radar, radial-current RMSD is 0.21 m s − 1 , compared with 0.59 m s − 1 for the operational Sentinel-1 radial-velocity product, 0.48 m s − 1 after a conventional wind-wave Doppler correction, and 0.38 m s − 1 for an ocean-model projection. Event-level ablations show that the physical constraint provides further RMSD reductions of 0.171 m s − 1 and 0.324 m s − 1 in the two independent tests. These results isolate radial-current skill attributable to the physical term beyond joint data fusion alone and demonstrate the value of jointly exploiting complementary SAR observables. The framework provides a practical pathway toward joint kilometer-scale mapping of coastal winds and radial currents from routine single-geometry acquisitions.
Authors
- Yuting Zhu (ORCID: https://orcid.org/0000-0001-5039-9601)
- Shengren Fan (ORCID: https://orcid.org/0000-0002-6656-9940)
- Marcos Portabella (ORCID: https://orcid.org/0000-0002-9972-9090)
- Giuseppe Grieco (ORCID: https://orcid.org/0000-0002-1255-599X)
- Xiaofeng Li (ORCID: https://orcid.org/0000-0001-7038-5119)
- Xiaoqing Wang
- Haohuan Fu
Institutions
- Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- Nanjing University of Information Science and Technology (CN)
- Nanjing University of Science and Technology (CN)
- Institute of Oceanology (CN)
- Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT)
- Institut de Ciències del Mar (ES)
- Tsinghua Shenzhen International Graduate School (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Remote Sensing of Environment
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1016/j.rse.2026.115686
- Primary Topic
- Ocean Waves and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.00