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GNSS-R Remote Sensing of the Ocean: Surface Waves and Related Phenomena

dc.contributor.authorChen, David
dc.date.accessioned2017-01-26T22:18:17Z
dc.date.availableNO_RESTRICTION
dc.date.available2017-01-26T22:18:17Z
dc.date.issued2016
dc.date.submitted2016
dc.identifier.urihttps://hdl.handle.net/2027.42/135779
dc.description.abstractIn this thesis, we explore several fundamental issues in GNSS-R remote sensing. Global Navigation Satellite System - Reflectometry (GNSS-R) is a relatively young remote sensing technique proposed to measure geophysical surface features and processes, such as ocean surface wind speed and roughness. GNSS-R uses a bistatic geometry at L-band frequencies. These two factors imply GNSS-R sense longer surface waves than traditional radar scatterometers and altimeters. Longer waves are known to take a longer time and more spatial coverage to respond to wind and propagate further before decaying. Our focus in this thesis is on quantifying some of these effects in the context of GNSS-R sensing of windspeed. We first attempt to bound the response time of GNSS-R surface roughness due to wind, using in-situ buoy measurements. These measurements are then used to validate a surface wave model. Coupling this surface wave model with an electromagnetic scattering model, we develop a novel end-to-end forward model for GNSS-R. This model shows superior performance against spaceborne GNSS-R measurements, with significant skill improvements over a state-of-the-art model. Among its many uses, it sheds light on factors that can improve GNSS-R remote sensing of ocean surface windspeed. The results presented herein are applicable to L-band bistatic sensing techniques in general, including those leveraging reflectometry of communication signals of opportunity.
dc.language.isoen_US
dc.subjectremote sensing
dc.subjectGNSS-R
dc.subjectocean surface waves
dc.titleGNSS-R Remote Sensing of the Ocean: Surface Waves and Related Phenomena
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineElectrical Engineering
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studies
dc.contributor.committeememberRuf, Christopher S
dc.contributor.committeememberBassis, Jeremy N
dc.contributor.committeememberDe Roo, Roger Dean
dc.contributor.committeememberTsang, Leung
dc.subject.hlbsecondlevelElectrical Engineering
dc.subject.hlbsecondlevelEngineering (General)
dc.subject.hlbsecondlevelNaval Architecture and Marine Engineering
dc.subject.hlbsecondlevelAtmospheric, Oceanic and Space Sciences
dc.subject.hlbsecondlevelGeology and Earth Sciences
dc.subject.hlbsecondlevelPhysics
dc.subject.hlbsecondlevelScience (General)
dc.subject.hlbtoplevelEngineering
dc.subject.hlbtoplevelScience
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/135779/1/ddchen_1.pdf
dc.identifier.orcid0000-0001-7465-1613
dc.identifier.name-orcidChen, David; 0000-0001-7465-1613en_US
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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