Li, Chen (2014) Sea surface oil slick detection and wind field measurement using global navigation satellite system reflectometry. Masters thesis, Memorial University of Newfoundland.
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In this thesis, research for improving sea surface remote sensing using the Global Navigation Satellite System-Reflectometry (GNSS-R) signals is presented. Firstly, a method to enable the simulation of GNSS-R delay Doppler Map (DDM) of an oil slicked sea surfaces under general scenarios is proposed. The DDM of oil slicked sea surface under general scenarios is generated by combining the mean-square slope model for oil slicked/clean surfaces and the GNSS-R Zavorotny-Voronovich (Z-V) scattering model. The coordinate system transformation appropriate for general- elevation-angle scenarios is also incorporated. Secondly, a technique to detect sea surface oil spills using reflections from Global Navigation Satellite System (GNSS) satellites is presented. This technique is implemented by compensating the distor- tion induced during the DDM deconvolution process of scattering coefficient retrieval and employing the spatial integration approach (SIA) to retrieve the scattering co- efficients unambiguously using the DDMs obtained by two separate antenna beams. A performance characterization including retrieval accuracy and resolution is demon- strated with respect to the signal-to-noise ratio and the size of oil slicks, respectively. Simulation based on the oil slick distribution of the Deepwater Horizon oil spill ac- cident shows that the retrieval error can be reduced by the SIA after the distortion correction. The technique proposed here can be used to map oil slick extent on the ocean surface or it may be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDM’s from a single space-based platform. Lastly, a novel method is presented to retrieve sea surface wind speed and direction by fitting the two-dimensional simulated GNSS-R DDMs to measured data. An 18- second incoherent correlation is performed on the measured signal to reduce the noise level. Meanwhile, a variable step-size iteration as well as a fitting threshold are used to reduce the computational cost and error rate of the fitting procedure, respectively. Unlike previous methods, all the DDM points with normalized power higher than the threshold are used in the least-square fitting. An optimal fitting threshold is also proposed. To validate the proposed method, the retrieval results based on a dataset from the United Kingdom Disaster Monitoring Constellation satellite are compared with the in-situ measurements provided by the National Data Buoy Center, and good correlation is observed between the two.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (pages 86-94).|
|Department(s):||Engineering and Applied Science, Faculty of|
|Library of Congress Subject Heading:||Oil pollution of the sea--Remote sensing; Winds--Measurement; Winds--Remote sensing; Global Positioning System|
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