## 1 Introduction  
Ocean surface currents (OSCs) play a crucial role in marine dynamics and consist of multiple components, including geostrophic currents, Ekman currents [1], Stokes drift [2], tidal currents, and near-inertial currents [3]. An accurate knowledge of OSCs is essential for a variety of maritime applications, such as navigation, fisheries, and marine search and rescue operations [4, 5, 6, 7]. Direct in situ measurements of ocean current velocities can be obtained using instruments, such as the Acoustic Doppler Current Profiler (ADCP) [8], Acoustic Wave and Current Profiler (AWAC) [9], wave gliders [10], sailrones [11], and drifting buoys [12]. While these instruments provide high-quality current measurements, they are constrained by their limited spatial coverage, high operational costs, and relatively short deployment durations. To address these limitations, remote sensing technologies, such as airborne Doppler radar [13] and high-frequency (HF) radar [14], have been developed. HF radar offers high temporal and spatial resolution for surface current measurements, but is generally limited to within 300 km of the coastline. Airborne radar systems can provide broader coverage with finer spatial resolution; however, their performance is often highly sensitive to weather conditions. Recent advancements in satellite remote sensing have made it increasingly feasible to derive surface current fields from satellite observations. For instance, satellite altimetry enables the estimation of geostrophic surface currents through analyses of the sea surface height (SSH) variability [15]. However, geostrophic currents represent only one component of the total OSC velocity. In recent years, spaceborne synthetic aperture radar (SAR) has emerged as a valuable tool for observing surface ocean currents, owing to its all-weather capability, continuous data availability, and high spatial resolution [16]. Rather than directly measuring the current velocity, SAR measures the Doppler frequency shifts induced by the motion of the sea surface. These observed Doppler shifts include both geophysical and non-geophysical components. The non-geophysical contributions arise from factors such as satellite attitude variations, scalloping effects, antenna electromagnetic mispointing, and residual errors. In contrast, geophysical Doppler shifts are associated with wave-induced motion and the actual dynamics of the ocean current. To isolate the Doppler shift attributable solely to ocean currents, both the non-geophysical and wave-induced Doppler contributions must be corrected [17; 18]. The satellite attitude variations are typically corrected using pitch and yaw angles derived from onboard gyroscope telemetry [19]. The scalloping effects are commonly addressed through linear regression techniques [20] or Fourier transform methods [21]. The biases resulting from antenna mispointing and residual Doppler errors can be mitigated by referencing the \"0 Hz\" Doppler shift measured over the land areas in the SAR scenes [22; 23]. Significant effort has also been devoted to estimating the geophysical Doppler contribution associated with the sea state. A widely used model, the CDOP, estimates this component based on the wind fields and radar configuration parameters [24]. However, the CDOP does not account for wave characteristics. To overcome this limitation, several empirical models, such as the CDOP3S, CDOP3 SX, CDOP3 SiX, and CDOP-Yn, have been developed to incorporate more comprehensive sea state information by including wave characteristic parameters alongside wind fields as the input variables [25; 26; 27]. Unlike purely data-driven empirical models, we developed a semi-empirical approach, known as the dual co-polarization Doppler velocity (DPDop) model, to estimate the wave-induced Doppler shifts [28; 29]. This model accounts for the four primary contributors to the Doppler velocity: short Bragg waves, breaking waves, tilt, and hydrodynamic modulation associated with long waves.