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

remote sensing;GNSS-R;ocean surface waves;Electrical Engineering;Engineering (General);Naval Architecture and Marine Engineering;Atmospheric;Oceanic and Space Sciences;Geology and Earth Sciences;Physics;Science (General);Engineering;Science;Electrical Engineering

Chen, DavidTsang, Leung ;

University of Michigan

关键词: remote sensing;    GNSS-R;    ocean surface waves;    Electrical Engineering;    Engineering (General);    Naval Architecture and Marine Engineering;    Atmospheric;    Oceanic and Space Sciences;    Geology and Earth Sciences;    Physics;    Science (General);    Engineering;    Science;    Electrical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/135779/ddchen_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

In 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.