On the Use of Azimuth Cutoff for Sea Surface Wind Speed Retrieval From SAR

Abstract

The accurate retrieval of sea-surface wind field data is crucial for weather forecasting and climate modeling. Despite this, the complexity of sea surface conditions poses significant challenges for satellite-based synthetic aperture radar (SAR) wind retrieval techniques. This study introduces a Bayesian inversion algorithm that incorporates azimuth cutoff wavelength information—a parameter previously underutilized and highly sensitive to varying wind conditions. We aimed to enhance the accuracy of SAR-derived wind estimations to enable more reliable interpretations of marine atmospheric dynamics. The methodology probabilistically combines SAR data with ancillary meteorological information and optimizes the retrieval process through a cost function that leverages the sensitivity of the azimuth cutoff to changes in wind vector fields. The proposed method was comprehensively validated using Sentinel-1 and Gaofen-3 SAR datasets against buoy measurements and wind estimations from scatterometers. The results demonstrated that the proposed method significantly improved the accuracy of wind speed estimations, especially under low-wind conditions and different sea-state conditions, without substantially increasing the computational burden. Although the wind direction retrieval displayed limited enhancement, the improved accuracy in wind speed estimations provides considerable benefits for operational meteorological applications. These findings suggest that the integration of azimuth cutoff information could be a critical step toward obtaining more accurate and reliable wind field retrievals from SAR data, thereby advancing the field of remote sensing and oceanography.

Keywords

• Azimuth cutoff
• Bayesian inversion algorithm
• sea-surface
• wind retrieval