Piecewise log-normal modeling of Sentinel-1 co-cross polarization coherence and application to wind field Bayesian inversion
Abstract
Accurate estimates of kilometer-scale ocean surface wind fields is critical for many applications, including weather forecasting, marine navigation, offshore wind energy development, ... Synthetic Aperture Radar (SAR) plays a key role in retrieving wind speed by analyzing changes in sea surface roughness, described through Geophysical Model Functions (GMFs). However, the inverse problem of wind retrieval from SAR data is under-determined, requiring ancillary information from Numerical Weather Models (NWP) to resolve ambiguities in the wind direction. The Co-Cross-Polarization Coherence (CCPC) has recently emerged as a valuable SAR-derived observation for improving wind direction estimates. While an initial GMF for CCPC was introduced by \cite{10.1109/tgrs.2021.3055979}, it proved inadequate for high wind speeds. We present PGMF-2, a refined GMF for CCPC, ensuring reliable performance up to 30 m/s by modeling its wind speed components as log-normal functions. The need for crosstalk calibration is mitigated by a burst-based processing.Monte Carlo simulations confirm that the unique odd-symmetry of the CCPC and the different impact of the wind direction modulation on its real and imaginary parts complements the Normalized Radar Cross Section (NRCS) to estimate the wind direction. This combination enables wind direction retrieval without NWP priors, even outperforming NWP-based inversions between 7 and 15 m/s. The Mean Absolute Error on direction reaches as low as 25° in favorable high-wind conditions.A case study on Storm Alex (2020) serves as a proof of concept of the Bayesian scheme on real-world observations, successfully retrieving the typical flow organization of the cyclonic system without reliance on NWP priors.
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BibTeX
@article{colin2026piecewise,
title = {{Piecewise log-normal modeling of Sentinel-1 co-cross polarization coherence and application to wind field Bayesian inversion}},
author = {Aurélien Colin and Romain Husson and Alexis Mouche and Antoine Grouazel and Frederic Nouguier},
journal = {Remote Sensing of Environment},
year = {2026},
month = nov,
doi = {10.1016/j.rse.2026.115664},
url = {https://hal.science/hal-05854061v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Piecewise log-normal modeling of Sentinel-1 co-cross polarization coherence and application to wind field Bayesian inversion
AU - Aurélien Colin
AU - Romain Husson
AU - Alexis Mouche
AU - Antoine Grouazel
AU - Frederic Nouguier
PY - 2026
DA - 2026/11/05
JO - Remote Sensing of Environment
DO - 10.1016/j.rse.2026.115664
UR - https://hal.science/hal-05854061v1
AB - Accurate estimates of kilometer-scale ocean surface wind fields is critical for many applications, including weather forecasting, marine navigation, offshore wind energy development, ... Synthetic Aperture Radar (SAR) plays a key role in retrieving wind speed by analyzing changes in sea surface roughness, described through Geophysical Model Functions (GMFs). However, the inverse problem of wind retrieval from SAR data is under-determined, requiring ancillary information from Numerical Weather Models (NWP) to resolve ambiguities in the wind direction. The Co-Cross-Polarization Coherence (CCPC) has recently emerged as a valuable SAR-derived observation for improving wind direction estimates. While an initial GMF for CCPC was introduced by \cite{10.1109/tgrs.2021.3055979}, it proved inadequate for high wind speeds. We present PGMF-2, a refined GMF for CCPC, ensuring reliable performance up to 30 m/s by modeling its wind speed components as log-normal functions. The need for crosstalk calibration is mitigated by a burst-based processing.Monte Carlo simulations confirm that the unique odd-symmetry of the CCPC and the different impact of the wind direction modulation on its real and imaginary parts complements the Normalized Radar Cross Section (NRCS) to estimate the wind direction. This combination enables wind direction retrieval without NWP priors, even outperforming NWP-based inversions between 7 and 15 m/s. The Mean Absolute Error on direction reaches as low as 25° in favorable high-wind conditions.A case study on Storm Alex (2020) serves as a proof of concept of the Bayesian scheme on real-world observations, successfully retrieving the typical flow organization of the cyclonic system without reliance on NWP priors.
SN - 0034-4257
SN - 1879-0704
ER -