Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France)

La, Tran Vu; Khenchaf, Ali; Comblet, Fabrice; Nahum, Carole

doi:10.1109/jstars.2017.2746349

Cited by 27 publications

(13 citation statements)

References 31 publications

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“…Many validation studies have addressed the accuracy of SAR wind fields compared to wind measurements from ocean buoys [8][9][10], scatterometers [11], meteorological masts [12], and wind lidars [13]. These studies suggest that wind speeds over the open ocean can typically be retrieved with a root mean square error on the order of 1.3-1.5 m s −1 .…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Wakes from SAR and Doppler Radar

Ahsbahs¹,

Nygaard

Newcombe

et al. 2020

Remote Sensing

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We retrieve atmospheric wake characteristics at the wind farm Westermost Rough from Sentinel-1 Synthetic Aperture Radar (SAR) images. For the first time, co-located reference measurements of the full flow field around the wind farm are available from Doppler radars. One case with a reference measurement of up to 10 km downstream of the wind farms shows that SAR images depict the wake better close to the wind farm than further downstream. The comparison of two cases with similar wind speed and direction indicate that under unstable atmospheric stratification, we can retrieve the structure of the wake field close to the wind farm from SAR, while this was not possible for a case with stable stratification. We find that openly available Sentinel-1 image archives can be used to study the structure of wind farm wakes depending on the atmospheric stability conditions. From an average of twelve available co-located cases, we find that velocity deficits at the wind turbine hub height are 8% from Doppler radar measurements and 4% from SAR wind retrievals.

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Section: Introductionmentioning

confidence: 99%

Wind Farm Wakes from SAR and Doppler Radar

Ahsbahs¹,

Nygaard

Newcombe

et al. 2020

Remote Sensing

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“…A wind direction error of 30°introduces wind speed uncertainty up to 40% (Lehner et al, 1998;Horstmann et al, 2000), which makes the inversion method based on wind streaks somewhat limited in application. Therefore, some researchers have explored the methods of retrieving wind speed directly without wind direction input, such as Komarov model (Komarov et al, 2014;La et al, 2017) and electromagnetic model (La et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

Zhang

Jiang²,

Xiang

et al. 2020

Front. Earth Sci.

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The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this article, we propose a new wind retrieval method by applying the optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated well-characterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. The results from the simulation data show that the error of the OI-retrieved wind is smaller than that of background wind in all considered cases; in particular, the accuracy of the OI-retrieved wind speed is significantly improved. Experiments on the Sentinel-1 SAR data show that the root mean square error of the OI-retrieved wind speed and direction are 1.4 m/s and 35°, respectively. Compared with other methods, the retrieved wind speed accuracy of the OI method is similar to that of the VAR method but higher than that of the direct wind retrieval method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The results indicate that the OI method can be effectively applied to SAR wind retrieval.

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“…In this article, we focus on three methods to estimate wind direction from a sea surface SAR image in order to evaluate which one offers the best performances and to allow further studies like [12][13][14] to estimate the wind direction and wind speed with the lowest errors and uncertainties. These methods provide a local direction by processing SAR image cells of size around 5 × 5 km.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Direction Estimation Methods from SAR Images

2020

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Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area.

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Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France)

Cited by 27 publications

References 31 publications

Wind Farm Wakes from SAR and Doppler Radar

Wind Farm Wakes from SAR and Doppler Radar

Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

Assessment of Wind Direction Estimation Methods from SAR Images

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