## 1 Introduction  
Accurately assessing local wind forces is crucial for studying the dynamic characteristics of ocean surface waves and currents [1, 2], developing strategies for ocean wind energy [3], and predicting the diffusion trajectory of pollutants in marine oil spill incidents [4]. Over the past few decades, spaceborne remote sensing technology has advanced significantly and become a crucial data source for observing global ocean geophysical variables. Among these technologies, microwave active radar has become dominant, as it collects observational data by correlating normalized radar cross-section (NRCS) with ocean surface wind fields. Spaceborne scatterometers are currently the most widely used instruments for observing ocean surface winds globally. However, they have two primary limitations: relatively low spatial resolution (ranging from approximately 12.5 to 50 km [5]) and susceptibility to interference from land echoes [6], which prevents accurate measurement of winds in coastal zones and hinders the detection of small-scale wind features. In contrast, synthetic aperture radar (SAR) offers higher spatial resolution [7] and can capture wind field information in complex environments, such as closed bays, fjords, and inland waters, making it the leading technology for high-resolution ocean surface wind field retrieval [8]. Currently, SAR-based wind vector retrieval methods are primarily divided into the direct inversion method and the variational analysis method (VAM). The direct inversion method retrieves ocean surface wind speed directly using a geophysical model function (GMF), with the required wind direction information typically provided by numerical weather prediction (NWP) models or scatterometer measurements. This method performs well in areas with small wind direction gradients over the open ocean regions. However, in coastal areas, the coarse resolution of NWP models or scatterometers (typically 0.25\({}^{\circ}\)) cannot capture small-scale wind direction variations caused by topographic effects, limiting its practical application [6]. While wind directions can also be approximated from wind streak features extracted from SAR images, not all images exhibit these wind streak phenomena under stable atmospheric conditions [9]. To address the limitations of the direct inversion method, VAM has been proposed for wind field inversion based on Bayesian theory [10]. This approach combines SAR-measured backscatters, NWP wind fields, and the uncertainties of related data to construct a variational equation, determining the optimal wind vector by minimizing the cost function. However, while the VAM, which employs only SAR NRCS as the observation field, effectively improves wind speed accuracy, its effectiveness in enhancing wind direction accuracy remains limited [11]. Due to the dynamic nature of sea surface motion, the observed SAR Doppler frequency often deviates from theoretical predictions. These deviations contain valuable geophysical information, reflecting the coupled interactions between ocean surface winds, waves, and currents [12]. The Doppler centroid anomaly (DCA) is derived by calculating the difference between the measured Doppler frequency and the theoretical Doppler shift predicted from satellite orbital dynamics and antenna pointing geometry. [PERSON] et al. [13] established a quantitative relationship between SAR-derived DCA and ocean surface dynamics through the analysis of Envisat's Advanced Synthetic Aperture Radar (ASAR) data, demonstrating its sensitivity to both wind fields and surface currents. Therefore, researchers began to incorporate DCA as a new observation parameter in VAM to improve the accuracy of wind speed and direction retrieval. Through collocation analysis of wind fields measured by ASCAT (advanced scatterometer) and DCAs derived from Envisat ASAR, [PERSON] et al. [14] developed a C-band Doppler (CDOP) GMF. Their results demonstrated that integrating the CDOP model into the VAM algorithm significantly enhances the retrieval accuracy of sea surface wind direction.