Surface and Subsurface Water Resources Quantification using an Ultra-wideband UAV-SDRadar

Abstract

Rapid changes in our environment, which have been exacerbated by climate change, require more tools to monitor our environment and water resources, including soil conditions and snow properties. While traditional remote sensing platforms such as satellites, high-altitude aircraft, and aerostats have been used for decades, there remains a need for low-cost, easily deployable systems with high spatial and temporal resolution. Uncrewed aerial vehicles (UAVs) with radars offer these advantages. Since using radars on UAVs is recent, there is a gap in the literature about radar cross-section (RCS) calibration and surface and subsurface quantification. This dissertation develops and validates simple, low-cost RCS calibration schemes for an ultra-wideband (UWB) software-defined radar (SDRadar) on a small, low-altitude UAV. Two methods are presented: a corner reflector (CR) for nadir and side-look calibration, and a circular disk optimized for nadir calibration with minimal background effects. Both achieved low standard deviation in the calibration factors. Beyond calibration, the dissertation presents methodologies for quantifying surface and subsurface water resources, including freshwater bathymetry, snow properties, groundwater, and soil moisture. Field results include successful detection of riverbeds at depths up to 3 m, quantification of snow depth, density, and snow water equivalent (SWE), and development of an algorithm for identifying snow layer transitions. Finally, A full-wave forward model was also developed, simulating a flat, multi-layered medium using the same antenna as in field experiments. The accuracy of the forward model was verified.