Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar

Abstract

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600–2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho = 0.9$ </tex-math></inline-formula> demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.