Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1441-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1441-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2022
Research article |
| 13 Jul 2022
| 13 Jul 2022
High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning
Louis de Montera
CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
Henrick Berger
CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
Romain Husson
CORRESPONDING AUTHOR
CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
Pascal Appelghem
Atmosky, Talence, France
Laurent Guerlou
CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
Mauricio Fragoso
CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
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Cited
16 citations as recorded by crossref.
- Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
- Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment M. Badger et al. https://doi.org/10.3390/en16093819
- From macro to micro: A multi-scale method for assessing coastal wind energy potential in China L. Deng et al. https://doi.org/10.1016/j.apenergy.2025.125729
- Green energy estimation using remote sensing techniques: A comprehensive review of methods, applications, and future directions A. Sonare et al. https://doi.org/10.1016/j.egyr.2026.109354
- Ship-based lidar measurements for validating ASCAT-derived and ERA5 offshore wind profiles H. Rubio et al. https://doi.org/10.5194/amt-18-4949-2025
- Reduction of Rain-Induced Errors for Wind Speed Estimation on SAR Observations Using Convolutional Neural Networks A. Colin et al. https://doi.org/10.1109/JSTARS.2023.3291236
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. https://doi.org/10.1088/1742-6596/2505/1/012027
- Characterizing the Atmospheric Boundary Layer for Offshore Wind Energy Using Synthetic Aperture Radar Imagery J. Stopa et al. https://doi.org/10.1002/we.2933
- Deep learning-based object detection of offshore platforms on Sentinel-1 imagery and the impact of synthetic training data R. Spanier et al. https://doi.org/10.1080/01431161.2026.2612908
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. https://doi.org/10.5194/wes-8-621-2023
- Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review R. Cavalli https://doi.org/10.3390/rs16030446
- Balancing Resource Potential and Investment Costs in Offshore Wind Projects: Evidence from Northern Colombia A. Ospino-Castro et al. https://doi.org/10.3390/en18226003
- An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi G. Placide & M. Lollchund https://doi.org/10.1186/s44329-024-00001-7
- Sentinel-1 for offshore wind energy application C. Hasager & K. Dimitriadou https://doi.org/10.1016/j.rse.2026.115369
- Atmospheric stability from numerical weather prediction models and microwave radiometer observations for onshore and offshore wind energy applications D. Cimini et al. https://doi.org/10.5194/amt-18-2041-2025
- Investigating Metocean Effects on Floating Offshore Wind Platform Positional Offset Using Sentinel-2 Imagery L. Filipe et al. https://doi.org/10.1109/ACCESS.2026.3662158
16 citations as recorded by crossref.
- Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
- Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment M. Badger et al. https://doi.org/10.3390/en16093819
- From macro to micro: A multi-scale method for assessing coastal wind energy potential in China L. Deng et al. https://doi.org/10.1016/j.apenergy.2025.125729
- Green energy estimation using remote sensing techniques: A comprehensive review of methods, applications, and future directions A. Sonare et al. https://doi.org/10.1016/j.egyr.2026.109354
- Ship-based lidar measurements for validating ASCAT-derived and ERA5 offshore wind profiles H. Rubio et al. https://doi.org/10.5194/amt-18-4949-2025
- Reduction of Rain-Induced Errors for Wind Speed Estimation on SAR Observations Using Convolutional Neural Networks A. Colin et al. https://doi.org/10.1109/JSTARS.2023.3291236
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. https://doi.org/10.1088/1742-6596/2505/1/012027
- Characterizing the Atmospheric Boundary Layer for Offshore Wind Energy Using Synthetic Aperture Radar Imagery J. Stopa et al. https://doi.org/10.1002/we.2933
- Deep learning-based object detection of offshore platforms on Sentinel-1 imagery and the impact of synthetic training data R. Spanier et al. https://doi.org/10.1080/01431161.2026.2612908
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. https://doi.org/10.5194/wes-8-621-2023
- Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review R. Cavalli https://doi.org/10.3390/rs16030446
- Balancing Resource Potential and Investment Costs in Offshore Wind Projects: Evidence from Northern Colombia A. Ospino-Castro et al. https://doi.org/10.3390/en18226003
- An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi G. Placide & M. Lollchund https://doi.org/10.1186/s44329-024-00001-7
- Sentinel-1 for offshore wind energy application C. Hasager & K. Dimitriadou https://doi.org/10.1016/j.rse.2026.115369
- Atmospheric stability from numerical weather prediction models and microwave radiometer observations for onshore and offshore wind energy applications D. Cimini et al. https://doi.org/10.5194/amt-18-2041-2025
- Investigating Metocean Effects on Floating Offshore Wind Platform Positional Offset Using Sentinel-2 Imagery L. Filipe et al. https://doi.org/10.1109/ACCESS.2026.3662158
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
A novel method for estimating offshore wind resources at turbine hub height with synthetic...
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