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Studies of Land and Ocean Remote Sensing Using Spaceborne GNSS-R Systems

Al-Khaldi, Mohammad Mazen

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2020, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Techniques for conducting spaceborne earth remote sensing are well established in the literature. Existing approaches include active and passive methods typically involving the launch of dedicated satellite platforms into orbit. More recently, there has been increasing interest in a relatively less mature mode of remote sensing, Global Navigation Satellite Signal Reflectometry (GNSS-R), which has opened new venues of investigation for the retrievals of geophysical parameters of interest at a global scale with unprecedented spatial and temporal coverage at a fraction of the cost compared to conventional satellite missions. This dissertation aims to support the use of spaceborne GNSS-R observations for global land and ocean remote sensing through investigating the nature and dependencies on surface geophysical properties of these returns and by developing algorithms to retrieve those of interest. The utility of the proposed analyses and methodologies are investigated in the context of NASA's Earth Venture Mission, CYGNSS (Cyclone Global Navigation Satellite System). For studies of land remote sensing, a time series retrieval method is introduced for near surface volumetric soil moisture content retrievals. This is supported by an analysis of the physical dependence of GNSS-R DDMs on land properties, showing that variations with soil moisture and composition, vegetation cover, and surface roughness are all to be expected. The proposed time series retrieval algorithm leverages the slowly varying nature of many of these processes to retrieve soil moisture. Development of complementary approaches to reduce the corrupting effects of low SNR and coherent DDMs in addition to compensating for incidence angle variability are all presented. The utility of the proposed methodology is first explored with simulated GNSS-R measurements and is subsequently extended to CYGNSS measurements on the local, regional and global scales. In support of the broader science community's land GNSS-R remote sensing investigations, a global coherence detection algorithm is developed. Coherence over land is expected to occur over the exceptionally flat surfaces typically manifested by inland water bodies. Due to the expectation that the correspondence of these returns to the geophysical properties of the surrounding land surface will be limited at best, it is crucial to be able to identify and separate these returns. The proposed methodology is shown to be highly effective at isolating coherent data, and tests over a large CYGNSS dataset suggests direct correlation of the prevalence of these returns with the presence of water bodies within the measurements' footprint. The ability to detect coherence on a global scale together with this correlation, provides a further opportunity to use GNSS-R measurements for the mapping of inland water bodies and analyzing their dynamics. This work therefore, also demonstrates the creation of dynamic inland water body masks through the use of the proposed coherence detector as part of a methodology that bypasses many of the uncertainties associated with existing techniques. The utility of the coherence detection algorithm over ocean surfaces is also explored. For studies of ocean remote sensing, this work aims to improve storm feature characterization using spaceborne GNSS-R systems by demonstrating maximum hurricane wind speed retrievals through the use of forward models for GNSS-R measurements. The retrieval approach is based on the matching of observations to a synthetic dataset created for a similar track through synthetic storm models having maximum wind speeds and radius as the fundamental parameters. The efficacy of the proposed methodology and its dependencies on storm model and measurement delay extent are explored using CYGNSS special acquisitions, ``Full DDMs'' (delay-Doppler Maps) and ``Raw I/F''. With the expectation that existing spaceborne GNSS-R systems will continue to operate under nominal conditions for the foreseeable future and will be followed by future missions offering further improvements to measurement resolution and that of the retrieved geophysical parameters' of interest as a consequence with a more comprehensive pole-to-pole coverage, this dissertation provides general frameworks and retrieval methodologies that leverage the sensitivity of GNSS-R measurements to various surface properties to support observing, understanding, modeling, and predicting the Earth's surface dynamics.
Joel Johnson (Advisor)
Fernando Teixeira (Committee Member)
Ethan Kubatko (Committee Member)
316 p.

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Citations

  • Al-Khaldi, M. M. (2020). Studies of Land and Ocean Remote Sensing Using Spaceborne GNSS-R Systems [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595798390010879

    APA Style (7th edition)

  • Al-Khaldi, Mohammad. Studies of Land and Ocean Remote Sensing Using Spaceborne GNSS-R Systems. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1595798390010879.

    MLA Style (8th edition)

  • Al-Khaldi, Mohammad. "Studies of Land and Ocean Remote Sensing Using Spaceborne GNSS-R Systems." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595798390010879

    Chicago Manual of Style (17th edition)