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<p class="MsoNormal"><b>Abstract:<o:p></o:p></b></p>
<p class="MsoNormal"><b>Title: Advancing Unmanned Aircraft System: A New Paradigm for UAS/UGV-based Precision Agriculture and Environmental Sensing using Recycled Frequency Spectrum<o:p></o:p></b></p>
<p class="MsoNormal">Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatio-temporal
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 measurement techniques provide point measurements and are unsuitable for large
agricultural fields. As an alternative, this research 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 GNSS receivers. To estimate surface reflectivity, GNSS receivers are attached underneath an unmanned aircraft system (UAS), which flies over agricultural fields. The estimated surface reflectivity is directly proportional to the soil dielectric
permittivity and can be translated to volumetric soil moisture (VSM). Additionally, this research investigates a fully custom UAS-based dual-polarized (H-pol and V-pol) L-band microwave radiometric measurement technique over agricultural areas to estimate
surface brightness temperature (<span style="font-family:"Cambria Math",serif">𝑇𝐵</span>). The radiometer measures surface emissivity as
<span style="font-family:"Cambria Math",serif">𝑇𝐵</span>, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This surface emissivity can be derived from
surface reflectivity with the relationship of (Emissivity = 1 - Reflectivity)<o:p></o:p></p>
<p class="MsoNormal">Beyond soil moisture estimation, this research further expands GNSS remote sensing by investigating a GNSS Transmissometry (GNSS-T) approach to estimate vegetation optical depth (VOD) over large-scale forestry regions. This method utilizes
multi-angle GNSS signals transmitted from global navigation satellite constellations and received by unmanned ground vehicle (UGV)-mounted GNSS-T sensors, enabling the assessment of vegetation water content and biomass. Furthermore, this research explores
the coexistence of 5G active communication with passive L-band microwave radiometry, addressing the growing concern of RFI contamination in remote sensing measurements.<o:p></o:p></p>
<p class="MsoNormal">In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in combination with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for estimating surface reflectivity and
<span style="font-family:"Cambria Math",serif">𝑇𝐵</span>. Additionally, it advances GNSS-T-based VOD estimation and assesses the challenges and mitigation strategies for passive radiometry in the presence of 5G active transmissions, contributing to the development
of robust and scalable environmental sensing technologies.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Bio:<o:p></o:p></b></p>
<p class="MsoNormal">Dr. Farhad is a Postdoctoral Fellow at the University of Georgia. He received his Ph.D. from Mississippi State University. He specializes in Unmanned Aerial Systems (UAS), and UAS-based microwave remote sensing, applied electromagnetics,
and precision agriculture. His research focuses on designing autonomous UAS platforms and sensing systems that integrate environmental data with signals of opportunity to advance precision agriculture. Dr. Farhad is a recipient of the NASA Early Career Research
(ECR) Award FINESST22, securing a $150,000 grant, and has been awarded with the 2024 Bagley College of Engineering Best Graduate Research Award, Hall of Fame Student Award, and the 2023 ECE Best Graduate Research Award at Mississippi State University. His
work advances autonomous remote sensing technologies for applications in agriculture, forestry, and environmental monitoring.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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