Sustainable Marine Structures

High-Precision Offshore Wind Energy Assessment Using SAR Satellite Data: A Case Study of Zhuanghe Offshore Waters

Ling Yuan

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

Haichuan Long

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

Xia Ruan

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

Fengzhi Yang

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

Jianke Li

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

Peng Chen

China State Shipbuilding Corporation (CSSC) Haizhuang Windpower Co., Ltd., Chongqing 401123, China

DOI: https://doi.org/10.36956/sms.v8i1.3004

Received: 17 December 2025 | Revised: 22 January 2026 | Accepted: 3 February 2026 | Published Online: 25 February 2026

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Abstract

With growing global demand for clean energy, offshore wind resource assessment is crucial. This study comprehensively utilizes multi-source Synthetic Aperture Radar (SAR) satellite data, including European Sentinel-1 and Chinese Gaofen-3 satellites, along with in-situ wind mast measurements, to systematically assess the wind energy resources in the offshore area of Zhuanghe, Liaoning Province, China, from 2017 to 2022. Sea surface wind fields were retrieved through data preprocessing, polarization conversion, geophysical model inversion, and multi-source data fusion. By coupling a vertical extrapolation model that accounts for sea state effects, the 10‑m wind speeds were extrapolated to turbine hub heights of 40 m, 80 m, and 100 m, enabling the calculation of key wind energy parameters such as wind speed and wind power density. Results indicate that at 100 m height, the annual average wind speed is 6.40 m·s⁻¹ and the annual average wind power density is 312.8 W·m⁻², corresponding to wind class 2. Wind speeds are higher in spring and winter and lower in summer and autumn, with prevailing northerly winds. Compared with ERA5 reanalysis data, SAR data offer advantages including high spatial resolution, rich historical records, and superior local accuracy, making them suitable for detailed offshore wind resource assessment. This research fills the gap in high‑precision wind energy evaluation under complex meteorological and data‑scarce conditions in the region, provides reliable data support for local offshore wind farm planning and development, and offers transferable experience for the application of SAR technology in similar coastal areas in China.

Keywords: Synthetic Aperture Radar (SAR); Wind Resource Assessment; Offshore; Zhuanghe Wind Farm; Vertical Extrapolation; Wind Energy Potential

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References

[1] International Energy Agency, 2020. World Energy Outlook 2020. IEA: Paris, France. pp. 1–25. Available from: https://www.iea.org/reports/world-energy-outlook-2020

[2] Hote, K., Kaushik, R., Tasnin, W., 2022. Global Offshore Wind Scenario: A Review. ECS Transactions. 107(1), 11083–11103. DOI: https://doi.org/10.1149/10701.11083ecst

[3] Barthelmie, R.J., Pryor, S.C., 2003. Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods. Journal of Applied Meteorology and Climatology. 43, 739–750. DOI: https://doi.org/10.1175/2096.1

[4] Elyouncha, A., Eriksson, L.E.B., Broström, G., et al., 2021. Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method. Remote Sensing of Environment. 260, 112455. DOI: https://doi.org/10.1016/j.rse.2021.112455

[5] Yang, H.Y., Fang, C., Wang, S., et al., 2024. On the ambiguity removal of wind direction derived from space-borne SAR imagery using deep learning. Remote Sensing of Environment. 308, 114202. DOI: https://doi.org/10.1016/j.rse.2024.114202

[6] Ricciardulli, L., Wentz, F.J., 2015. A Scatterometry Geophysical Model Function for Climate-Quality Winds: QuikSCAT Ku-2011. Journal of Atmospheric and Oceanic Technology. 32, 1829–1846. DOI: https://doi.org/10.1175/JTECH-D-15-0008.1

[7] Hersbach, H., 2003. CMOD5: An improved geophysical model function for ERS C-band scatterometry. European Centre for Medium Range Weather Forecasts (ECMWF): Reading, UK.

[8] Stoffelen, A., Verspeek, J.A., Vogelzang, J., et al., 2017. The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(5), 2123–2134. DOI: https://doi.org/10.1109/JSTARS.2017.2681806

[9] Takeyama, Y., Ohsawa, T., Kozai, K., et al., 2013. Comparison of geophysical model functions for SAR wind speed retrieval in Japanese coastal waters. Remote Sensing. 5(4), 1956–1973. DOI: https://doi.org/10.3390/rs5041956

[10] Monaldo, F., Jackson, C., Li, X., et al., 2016. Preliminary Evaluation of Sentinel-1A Wind Speed Retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(6), 2638–2642. DOI: https://doi.org/10.1109/JSTARS.2015.2504324

[11] Lin, H., Xu, Q., Zheng, Q., 2008. An Overview on SAR measurements of sea surface wind. Progress in Natural Science. 18(8), 913–919. DOI: https://doi.org/10.1016/j.pnsc.2008.03.008

[12] Zhang, B., Lu, Y., Perrie, W., et al., 2021. Compact polarimetry synthetic aperture radar ocean wind retrieval: Model development and validation. Journal of Atmospheric and Oceanic Technology. 38(4), 747–757. DOI: https://doi.org/10.1175/JTECH-D-20-0035.1

[13] Zhao, X., Shao, W., Hao, M., et al., 2023. Novel Approach to Wind Retrieval from Sentinel-1 SAR in Tropical Cyclones. Canadian Journal of Remote Sensing. 49(1), 2254839. DOI: https://doi.org/10.1080/07038992.2023.2254839

[14] Gao, Y., Zhang, J., 2021. Tropical Cyclone Wind Speed Retrieval from SAR Imagery Using A Neural Network. In Proceedings of the IEEE Radar Conference, Haikou, China, 15–19 December 2021; pp. 568–572. DOI: https://doi.org/10.1109/Radar53847.2021.10028517

[15] Zanchetta, A., Zecchetto, S., 2021. Wind direction retrieval from Sentinel-1 SAR images using ResNet. Remote Sensing of Environment. 253, 112178. DOI: https://doi.org/10.1016/j.rse.2020.112178

[16] Torres, R., Snoeij, P., Geudtner, D., et al., 2012. GMES Sentinel-1 Mission. Remote Sensing of Environment. 120, 9–24. DOI: https://doi.org/10.1016/j.rse.2011.05.028

[17] Majidi Nezhad, M., Groppi, D., Marzialetti, P., et al., 2019. Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands. Renewable and Sustainable Energy Reviews. 109, 499–513. DOI: https://doi.org/10.1016/j.rser.2019.04.059

[18] Chang, R., Zhu, R., Badger, M., et al., 2015. Offshore wind resources assessment from multiple satellite data and WRF modeling over South China Sea. Remote Sensing. 7(1), 467–487. DOI: https://doi.org/10.3390/rs70100467

[19] Fang, H., Xie, T., Perrie, W., et al., 2018. Comparison of C-band quad-polarization synthetic aperture radar wind retrieval models. Remote Sensing. 10(9), 1448. DOI: https://doi.org/10.3390/rs10091448

[20] Komarov, S., Komarov, A., Zabeline, V., 2012. Marine wind speed retrieval from RADARSAT-2 dual-polarization imagery. Canadian Journal of Remote Sensing. 37(5), 520–528. DOI: https://doi.org/10.5589/m11-063

[21] Kim, T.-S., Park, K.-A., Choi, W.-M., et al., 2012. L-band SAR-derived Sea Surface Wind Retrieval off the East Coast of Korea and Error Characteristics. Korean Journal of Remote Sensing. 28(5), 477–487. DOI: https://doi.org/10.7780/kjrs.2012.28.5.1 (in Korean)

[22] Yao, R., Shao, W., Jiang, X., et al., 2022. Wind speed retrieval from Chinese Gaofen-3 synthetic aperture radar using an analytical approach in the nearshore waters of China’s seas. International Journal of Remote Sensing. 43(8), 3028–3048. DOI: https://doi.org/10.1080/01431161.2022.2079019

[23] Wan, Y., Guo, S., Li, L., et al., 2021. Data quality evaluation of sentinel-1 and GF-3 SAR for wind field inversion. Remote Sensing. 13(18), 3723. DOI: https://doi.org/10.3390/rs13183723

[24] Prajapati, J., Chakraborty, A., Shukla, B.P., et al., 2020. Development of wind speed retrieval model using RISAT-1 SAR cross-polarized observations. Current Science. 118(8), 1282–1286. DOI: https://doi.org/10.18520/cs/v118/i8/1282-1286

[25] de Montera, L., Remmers, T., O’Connell, R., et al., 2020. Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland. Wind Energy Science. 5(3), 1023–1036. DOI: https://doi.org/10.5194/wes-5-1023-2020

[26] Ahsbahs, T., Badger, M., Karagali, I., et al., 2017. Validation of sentinel-1A SAR coastal wind speeds against scanning LiDAR. Remote Sensing. 9(6), 552. DOI: https://doi.org/10.3390/rs9060552

[27] Hadjipetrou, S., Liodakis, S., Sykioti, A., et al., 2022. Evaluating the suitability of Sentinel-1 SAR data for offshore wind resource assessment around Cyprus. Renewable Energy. 182, 1228–1239. DOI: https://doi.org/10.1016/j.renene.2021.10.100

[28] Rana, F.M., Adamo, M., Lucas, R., et al., 2019. Sea surface wind retrieval in coastal areas by means of Sentinel-1 and numerical weather prediction model data. Remote Sensing of Environment. 225, 379–391. DOI: https://doi.org/10.1016/j.rse.2019.03.019

[29] Miglietta, M.M., Zecchetto, S., De Biasio, F., et al., 2013. A comparison of WRF model simulations with SAR wind data in two case studies of orographic lee waves over the Eastern Mediterranean Sea. Atmospheric Research. 120–121, 127–146. DOI: https://doi.org/10.1016/j.atmosres.2012.08.009

[30] Salvação, N., Bentamy, A., Guedes Soares, C., et al., 2022. Developing a new wind dataset by blending satellite data and WRF model wind predictions. Renewable Energy. 198, 283–295. DOI: https://doi.org/10.1016/j.renene.2022.07.049

[31] Barone, M., Galdi, C., di Bisceglie, M., et al., 2024. A Cross-Comparison Study on CYGNSS and Sentinel-1 wind Speed Products in Tropical Cyclones. In Proceedings of the 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 7–12 July 2024; pp. 842–845. DOI: https://doi.org/10.1109/IGARSS53475.2024.10642263

[32] Ahsbahs, T., MacLaurin, G., Draxl, C., et al., 2020. US East Coast synthetic aperture radar wind atlas for offshore wind energy. Wind Energy Science. 5(3), 1191–1210. DOI: https://doi.org/10.5194/wes-5-1191-2020

[33] Badger, M., 2009. Satellite SAR Wind Resource Mapping in China (SAR-China). Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Research Center Risoe: Kongens Lyngby, Denmark. Available from: https://backend.orbit.dtu.dk/ws/portalfiles/portal/3466486/ris-r-1706.pdf

[34] Chang, R., Zhu, R., Badger, M., et al., 2014. Applicability of synthetic aperture radar wind retrievals on offshore wind resources assessment in Hangzhou Bay, China. Energies. 7(5), 3339–3354. DOI: https://doi.org/10.3390/en7053339

[35] Badger, M., Fujita, A., Orzel, K., et al., 2023. Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment. Energies. 16(9), 3819. DOI: https://doi.org/10.3390/en16093819

[36] Ma, Y., Xu, J., Wu, Y., et al., 2025. Wind dataset assessment and energy estimation for potential future offshore wind farm development areas on the Scotian Shelf. Wind Energy Science. 10, 2965–2999. DOI: https://doi.org/10.5194/wes-10-2965-2025

[37] Palatos-Plexidas, A., Gremmo, S., van Beeck, J., et al., 2025. Assessing the Accuracy of a Three-Year High-Resolution Mesoscale Wind Farm Wake Simulation with Lidar and Satellite Radar Data. Wind Energy Science Discuss. Preprints. DOI: https://doi.org/10.5194/wes-2025-202

[38] de Montera, L., Berger, H., Husson, R., et al., 2022. High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning. Wind Energy Science. 7(4), 1441–1453. DOI: https://doi.org/10.5194/wes-7-1441-2022

[39] Ahsbahs, T., Badger, M., Volker, P., et al., 2018. Applications of satellite winds for the offshore wind farm site Anholt. Wind Energy Science. 3(2), 573–588. DOI: https://doi.org/10.5194/wes-3-573-2018

[40] Owda, A., Badger, M., 2022. Wind Speed Variation Mapped Using SAR before and after Commissioning of Offshore Wind Farms. Remote Sensing. 14(6), 1464. DOI: https://doi.org/10.3390/rs14061464

[41] Buonanno, S., Cuozzo, G., Fusco, A., 2008. Wind map retrieval from SAR data for offshore wind turbines positioning. In IEEE International Symposium on Industrial Electronics, Cambridge, UK, 30 June–2 July 2008; pp. 2311–2316. DOI: https://doi.org/10.1109/ISIE.2008.4677080

[42] Hasager, C.B., Barthelmie, R.J., Christiansen, M.B., et al., 2006. Quantifying offshore wind resources from satellite wind maps: Study area the North Sea. Wind Energy. 9(1–2), 63–74. DOI: https://doi.org/10.1002/we.190

[43] Hauser, D., Abdalla, S., Ardhuin, F., et al., 2023. Satellite Remote Sensing of Surface Winds, Waves, and Currents: Where Are We Now? Surveys in Geophysics. 44, 1357–1446. DOI: https://doi.org/10.1007/s10712-023-09771-2

[44] Pierdicca, N., Rocca, F., Perissin, D., et al., 2011. Numerical weather prediction models and SAR interferometry: Synergic use for meteorological and INSAR applications. In SAR Image Analysis, Modeling, and Techniques XI, Prague, Czech Republic, 21–22 September 2011; 817912. DOI: https://doi.org/10.1117/12.898756

[45] Ivanova, T., Porchetta, S., Buckingham, S., et al., 2025. Improving wind and power predictions via four-dimensional data assimilation in the WRF model: Case study of storms in February 2022 at Belgian offshore wind farms. Wind Energy Science. 10(1), 245–268. DOI: https://doi.org/10.5194/wes-10-245-2025

[46] Khachatrian, E., Asemann, P., Zhou, L., et al., 2024. Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic. Atmosphere. 15(2), 146. DOI: https://doi.org/10.3390/atmos15020146

[47] Liu, Q., Babanin, A.V., Zieger, S., et al., 2016. Wind and Wave Climate in the Arctic Ocean as Observed by Altimeters. Journal of Climate. 29, 7957–7975. DOI: https://doi.org/10.1175/JCLI-D-16-0219.1

[48] Doubrawa, P., Barthelmie, R.J., Pryor, S.C., et al., 2015. Satellite Winds as a Tool for Offshore Wind Resource Assessment: The Great Lakes Wind Atlas. Remote Sensing of Environment. 168, 349–359. DOI: https://doi.org/10.1016/j.rse.2015.07.008

[49] Zhou, Y., Yang, B., Yin, Q., et al., 2024. Improved SAR Radiometric Cross-Calibration Method Based on Scene-Driven Incidence Angle Difference Correction and Weighted Regression. IEEE Transactions on Geoscience and Remote Sensing. 62, 1–16. DOI: https://doi.org/10.1109/TGRS.2024.3471084

[50] Nie, B., Li, J., 2018. Technical potential assessment of offshore wind energy over shallow continent shelf along China coast. Renewable Energy. 128(Part A), 391–399. DOI: https://doi.org/10.1016/j.renene.2018.05.081