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

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 pa- rameter 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 probabilisti- cally 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 mea- surements 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 oper- ational 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.