## 5 Conclusions  
This study presents a comprehensive evaluation of radial OSC retrievals in the SCS using Sentinel-1 SAR Doppler shift data and drifting buoy observations. To achieve accurate radial current velocities, the DPDop model was applied to correct for the wave-induced Doppler contributions. Unlike traditional empirical methods, the DPDop model simultaneously accounts for the contributions from short Bragg waves, breaking waves, tilt, and hydrodynamic modulation associated with longer waves. This integrated correction approach significantly improves the retrieval accuracy across diverse sea state conditions. In the Beibu Gulf, where the sea state is predominantly driven by wind waves, the SAR-derived radial currents exhibited close agreement with the SMOC dataset, with a bias of 0.07 m/s, a RMSE of 0.14 m/s, and a MAE of 0.12 m/s. In contrast, in mixed wave fields or the swell-dominated region south of Taiwan influenced by the Kuroshio current, the SAR-retrieved radial OSC velocities effectively captured a prominent anticyclonic eddy caused by Kuroshio intrusion. Both the location and intensity of the eddy closely aligned with the SMOC data. For this case, the bias, RMSE, and MAE were -0.06 m/s, 0.23 m/s, and 0.19 m/s, respectively. These results, derived from differing sea state conditions, demonstrate the strong performance and robustness of the radial current velocity retrievals, a conclusion further supported by the comprehensive statistical analysis. To evaluate the model's performance across diverse oceanic conditions, we analyzed 6431 SAR Doppler shift observations collected in the SCS from November 2017 to October 2023, matched with 177 drifting buoy measurements spanning low, moderate, and high sea states. The comparison revealed a bias of 0.01 m/s, an RMSE of 0.18 m/s, and a MAE of 0.16 m/s between the SAR-retrieved radial OSC velocities and the drifting buoy observations, demonstrating that SAR-measured Doppler shifts can reliably be used to derive surface current information under varying and complex sea states. Furthermore, the high correlation coefficients, ranging from 0.92 to 0.96 for the two case studies, supported by the overall statistical analysis, further confirm the robust performance of the DPDop model at effectively removing the Doppler contributions from the wind waves and swell. **Author Contributions:** Conceptualization, methodology, S.F. and B.Z. ; writing--original draft preparation, S.F. ; writing--review and editing, S.F., B.Z., and V.K. ; visualization, S.F. ; supervision, B.Z. and V.K. ; funding acquisition, B.Z. and V.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Key R&D Program of China under Grant 2024 YFC2815703, the Hainan Province and Technology Special Fund under Grant SOLZSKY2025009, the Ministry of Science and Higher Education of Russia Assignment under Grant FSZU-2025-0005 from RSHU and under Grant FNNNN-2024-0001 from MHI RAS. **Data Availability Statement:** The Sentinel-1 Level-2 OCN data are available at [[https://search.asf.alaska.edu](https://search.asf.alaska.edu)]([https://search.asf.alaska.edu](https://search.asf.alaska.edu)) (accessed on 1 June 2024). The 1 h GDP drifter data are available at [[https://www.aoml.noaa.gov/phod/gdp/hourly_data.php](https://www.aoml.noaa.gov/phod/gdp/hourly_data.php)]([https://www.aoml.noaa.gov/phod/gdp/hourly_data.php](https://www.aoml.noaa.gov/phod/gdp/hourly_data.php)) (accessed on 1 June 2024). The 6 h GDP drifter are available at [[https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php](https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php)]([https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php](https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php)) (accessed on 1 June 2024).