Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry


Yildiz S., AKYÜREK S. Z. , Binley A.

Hydrological Processes, vol.35, no.5, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1002/hyp.14190
  • Journal Name: Hydrological Processes
  • Journal Indexes: Science Citation Index Expanded, Scopus, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: digital surface model, digital terrain model, ground penetrating radar, photogrammetry, snow density, snow tube, snow water equivalent, unmanned aerial vehicle, SPATIAL VARIABILITY, VELOCITY ANALYSIS, SYSTEMS UASS, DEPTH, GPR, ACCURACY, MOUNTAIN

Abstract

© 2021 John Wiley & Sons Ltd.This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.