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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated retrieval of ocean surface wind and current fields from single-geometry SAR observations through a physically constrained Bayesian framework

Yuting Zhu, Shengren Fan, Marcos Portabella, Giuseppe Grieco et al.
Remote Sensing of Environment
Ocean Waves and Remote Sensing
article

Integrated retrieval of ocean surface wind and current fields from single-geometry SAR observations through a physically constrained Bayesian framework

Yuting Zhu, Shengren Fan, Marcos Portabella, Giuseppe Grieco, Xiaofeng Li, Xiaoqing Wang, Haohuan Fu
article en

Abstract

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.

Remote Sensing of EnvironmentVol. 348
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)
Openalex Percentile: Top 16%
Ocean Waves and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.