Theses and Dissertations

ORCID

https://orcid.org/0000-0001-9391-719X

Advisor

Gurbuz, Ali Cafer

Committee Member

Kurum, Mehmet

Committee Member

Ball, John E.

Committee Member

Green, Ryan

Date of Degree

5-10-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, horticulture, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatiotemporal resolution information about surface SM is crucial. However, the use of invasive SM probes and other sensors is expensive and requires extensive manpower. Moreover, these intrusive techniques provide point measurements and are unsuitable for large agricultural fields. As an alternative, this dissertation explores the remote sensing of surface SM by utilizing the surface reflectivity estimated from global navigation satellite systems reflectometry (GNSS-R) data acquired through smartphones and off-the-shelf, cost-effective U-blox global navigation satellite systems (GNSS) receivers. To estimate surface reflectivity, the GNSS receivers are attached underneath a small unmanned aircraft system (UAS), which flies over agricultural fields. Additionally, this dissertation investigates a fully custom UAS-based dual-polarized L-band microwave radiometric measurement technique over agricultural areas to estimate surface brightness temperature (����). The radiometer measures surface emissivity as ����, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This radiometer processes the data in near real-time onboard the UAS, collecting raw in-phase and quadratic (I&Q) signals across the study field. This feature mitigates the RFI onboard and significantly reduces post-processing time. In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in conjunction with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for the estimation of surface reflectivity and ����. The radiometric measurement of surface emissivity is related to surface reflectivity through the relationship (Emissivity = 1 -Reflectivity).

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