New Algorithms for Ocean Surface Wind Retrievals Using Multi-Frequency Signals of Opportunity
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Global Navigation Satellite System Reflectometry (GNSS-R) has presented a great potential as an important approach for ocean remote sensing. Numerous studies have demonstrated that the shape of a code-correlation waveform of forward-scattered Global Positioning System (GPS) signals may be used to measure ocean surface roughness and related geophysical parameters such as wind speed. Recent experiments have extended the reflectometry technique to transmissions from communication satellites. Due to the high power and frequencies of these signals, they are more sensitive to smaller scale ocean surface features, which makes communication satellites a promising signal of opportunity (SoOp) for ocean remote sensing. Recent advancements in fundamental physics are represented by the new scattering model and bistatic radar function developed by Voronovich and Zavorotny based on the SSA (Small Slope Approximation). This new model allows the partially coherent scattering in low wind conditions to be correctly described, which overcomes the limitations of diffuse scattering inherited in the conventional KA-GO (Kirchhoff Approximation-Geometric Optics) model. Furthermore, exploration and practice using spaceborne platforms have become a primary research focus, which is highlighted by the launch of CYGNSS (Cyclone Global Navigation Satellite System) in 2016. CYGNSS is a NASA (National Aeronautics and Space Administration) Earth Venture Mission consisting of an 8 micro-satellite constellation of GNSS-R instruments designed to observe tropical cyclones.
However, in spite of the significant achievements made in the past 10 years, there
are still a variety of challenges to be addressed currently in the ocean reflectometry
field. To begin with, the airborne demonstration experiments conducted previously for S-band reflectometry provided neither sufficient amount of data nor the desired
scenarios to assess high wind retrieval performance of S-band signals. The current
L-band empirical model function theoretically does not also apply to S-band reflectometry. With respect to scattering models, there have been no results of actual data
processing so far to verify the performance of the SSA model, especially on low wind
retrievals. Lastly, the conventional model fitting methods for ocean wind retrievals
were proposed for airborne missions, and new approaches will need to be developed
to satisfy the requirement of spaceborne systems.
The research described in this thesis is mainly focused on the development, application and evaluation of new models and algorithms for ocean wind remote sensing.
The first part of the thesis studies the extension of reflectometry methods to the general class of SoOps. The airborne reception of commercial satellite S-band transmissions is demonstrated under both low and high wind speed conditions. As part of this
effort, a new S-band geophysical model function (GMF) is developed for ocean wind
remote sensing using S-band data collected in the 2014 NOAA (National Oceanic and Atmospheric Administration) hurricane campaign.
The second part introduces a dual polarization L- and S-band reflectometry experiment, performed in collaboration with Naval Research Lab (NRL), to retrieve and
analyze surface winds and compare the results with CYGNSS satellite retrievals and
NOAA data buoy measurements. The problems associated with low wind speed retrieval arising from near specular surface reflections are studied. Results have shown
improved wind speed retrieval accuracy using bistatic radar cross section (BRCS)
modeled by the SSA when compared with KA-GO, in the cases of low to medium
diffuse scattering. The last part focuses on the contributions to the NASA-funded
spaceborne CYGNSS project. It shows that the accuracy of CYGNSS ocean wind
retrieval is improved by an Extended Kalman Filter (EKF) algorithm. Compared
with the baseline observable methods, preliminary results showed promising accuracy
improvement when the EKF was applied to actual CYGNSS data.