Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Kurum, Mehmet
Committee Member
Ball, John E.
Committee Member
Donohoe, J. Patrick
Committee Member
Gurbuz, Ali
Date of Degree
12-13-2019
Original embargo terms
Worldwide
Document Type
Dissertation - Open Access
Major
Electrical and Computer Engineering
Degree Name
Doctor of Philosophy
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.
URI
https://hdl.handle.net/11668/16435
Sponsorship
Grant # 80NSSC18K1329 from the National Aeronautics and Space Administration (NASA) Earth and Space Science Fellowship Program
Recommended Citation
Eroglu, Orhan, "Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques" (2019). Theses and Dissertations. 2729.
https://scholarsjunction.msstate.edu/td/2729
Comments
bistatic scattering||CYGNSS||GNSS-Reflectometry||information retrieval||learning- based||physics-aware||SCoBi||Signals of Opportunity||soil moisture