Derivation of Sea Surface Wind Directions from TerraSAR-X Data Using the Local Gradient Method

Wang, Yiran; Li, Xiaoming

doi:10.3390/rs8010053

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The GMF relates radar backscatter with sea surface wind vectors (both wind speed and wind direction) and incidence angles. The visible wind streaks in SAR images are often used as indicators to derive sea surface wind direction [ Gerling , ; Lehner et al ., ; Koch , ; Wang and Li , ], which is the input to the GMF for sea surface wind field retrievals.…”

Section: Introductionmentioning

confidence: 99%

Sea surface wind streaks in spaceborne synthetic aperture radar imagery

Zhao

Sha

2016

JGR Oceans

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Wind streaks are often observed in Synthetic aperture radar (SAR) images. They are used to determine the sea surface wind direction for sea surface wind field retrievals. It is generally understood that visible wind streaks are caused by roll vortices in the marine atmospheric boundary layer. In this study, 227 X‐band spaceborne SAR images of TerraSAR‐X and TanDEM‐X acquired from the three FiNO platforms in the North Sea and Baltic Sea were thoroughly analyzed for a comprehensive understanding of the manifestation of wind streaks in SAR images. Approximately 48.0% of the 227 SAR images displayed wind streaks, among which 67.3%, 20.0%, and 12.7% occurred under unstable, neutral, and stable atmospheric conditions, respectively. The proportions indicate that wind streaks are more likely to be generated from thermal convection. Further investigations suggest that the inflection point and the wind shear may be essential for the appearance of wind streaks in SAR images under stable atmospheric conditions.

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Section: Introductionmentioning

confidence: 99%

Sea surface wind streaks in spaceborne synthetic aperture radar imagery

Zhao

Sha

2016

JGR Oceans

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“…The orientations of these wind streaks in spaceborne SAR images is commonly thought to represent the SSW directions [4]. Following this rationale, SSW directions have been derived from images acquired by various spaceborne SAR missions, e.g., ERS/SAR, ENVISAT/ASAR, and TerraSAR-X, using either the fast Fourier transform (FFT) method [5] or the local gradient (LG) method [6,7]. Approximately 50% of space borne SAR images present clear wind streaks [8,9].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

TianYu

Qian

et al. 2019

Remote Sensing

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In this paper, the sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) polarizations is investigated in detail based on 3170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s, but a rather high root mean square error (RMSE) of 2.36 m/s when compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle-dependent and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which yields very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ± 0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

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“…The orientations of these wind streaks in spaceborne SAR images is commonly thought to represent the SSW directions [4]. Following this rationale, SSW directions have been derived from images acquired by various spaceborne SAR missions, e.g., ERS/SAR, ENVISAT/ASAR and TerraSAR-X, using either the fast Fourier transform (FFT) method [5] or the local gradient (LG) method [6,7]. Interestingly, approximately 50% of spaceborne SAR images present clear wind streaks [8,9].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

TianYu¹,

Li²,

Qian³

et al. 2019

Preprint

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In this paper, sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH) and vertical-horizontal (VH) polarizations is investigated in detail based on 3,170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s but a rather high root mean square error (RMSE) of 2.36 m/s compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle- and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which surprisingly yields a very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ±0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

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Derivation of Sea Surface Wind Directions from TerraSAR-X Data Using the Local Gradient Method

Cited by 8 publications

References 28 publications

Sea surface wind streaks in spaceborne synthetic aperture radar imagery

Sea surface wind streaks in spaceborne synthetic aperture radar imagery

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

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