Repair wind field in oil contaminated areas with SAR images

Guo, Jie; He, Yijun; Long, Xiao; Hou, Chawei; Liu, Xin; Meng, Junmin

doi:10.1007/s00343-015-3242-7

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…ASCAT was launched on 19 October 2006 in C band in frequency at 5.255 GHz. The wind products of ASCAT are distributed in two resolutions at 50 km in 25-km grid (Guo et al, 2015).…”

Section: Datamentioning

confidence: 99%

Impact of tropical cyclone Matmo on mixed zone of the Yellow and Bohai Seas

Guo

Ji²,

Hou³

et al. 2018

J. Ocean. Limnol.

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The Bohai Sea is a low-lying semi-enclosed sea area that is linked to the Yellow Sea via the Bohai straits (mixed zone). Its off shore seabed is shallow, which makes it vulnerable to serious marine meteorological disasters associated with the northward passage of Pacifi c tropical cyclones. Analyses on data of remote sensing and buoy of the mixed zone of the Yellow and Bohai seas indicate that all the wind speed, signifi cant wave height, and salinity (SAL) increased, sea surface temperature decreased, and wind energy density changed considerably during the passage of tropical cyclone Matmo on July 25, 2014. It was found that the SAL inversion layer in the mixed zone of the Yellow and Bohai Seas was caused by the tropical cyclone. Furthermore, it was found that the tropical cyclone transported the northern Yellow Sea cold water mass (NYSCWM) into the mixed zone of the Yellow and Bohai Seas. The NYSCWM has direct infl uence on both the aquaculture and the ecological environment of the region. Therefore, further research is needed to establish the mechanism behind the formation of the SAL inversion layer in the mixed zone, and to determine the infl uence of tropical cyclones on the NYSCWM.

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“…ASCAT was launched on 19 October 2006 in C band in frequency at 5.255 GHz. The wind products of ASCAT are distributed in two resolutions at 50 km in 25-km grid (Guo et al, 2015).…”

Section: Datamentioning

confidence: 99%

Impact of tropical cyclone Matmo on mixed zone of the Yellow and Bohai Seas

Guo

Ji²,

Hou³

et al. 2018

J. Ocean. Limnol.

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“…Therefore, satellite synthetic aperture radar (SAR) data from ERS-1/2 (European Remote Sensing Satellites), ENVISAT (Environmental Satellite), ALOS (Advanced Land Observing Satellite), RADARSAT-1/2 and TerraSAR-X have been widely used to detect and monitor oil spills [1][2][3][4][5][6][7][8] due to the large spatial coverage, all-weather conditions and imaging capability during day-night times [9]. In addition, airborne SAR sensors, such as Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) developed by JPL at L-band and E-SAR (developed by DLR), have proven their potential for scientific research on ocean or land [10,11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

Zhang

Liang

et al. 2017

Applied Sciences

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Abstract:In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual-and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual-and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

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A hindcast method to simulate oil spill trajectories for the Bohai Sea, Northeast China

Cui

et al. 2016

Ocean Engineering

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Repair wind field in oil contaminated areas with SAR images

Cited by 3 publications

References 18 publications

Impact of tropical cyclone Matmo on mixed zone of the Yellow and Bohai Seas

Impact of tropical cyclone Matmo on mixed zone of the Yellow and Bohai Seas

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

A hindcast method to simulate oil spill trajectories for the Bohai Sea, Northeast China

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