Rights statement: This is the peer reviewed version of the following article:Yildiz, S., Akyurek, Z., & Binley, A. (2021). Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry. Hydrological Processes, 35: e14190. doi: 10.1002/hyp.14190 which has been published in final form at https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2Fhyp.14190 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Quantifying snow water equivalent using terrestrial GPR and UAV photogrammetry
AU - Yildiz, Semih
AU - Akyurek, Zuhal
AU - Binley, Andrew
N1 - This is the peer reviewed version of the following article:Yildiz, S., Akyurek, Z., & Binley, A. (2021). Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry. Hydrological Processes, 35: e14190. doi: 10.1002/hyp.14190 which has been published in final form at https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2Fhyp.14190 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2021/5/31
Y1 - 2021/5/31
N2 - This study demonstrates the potential value of a combined 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.
AB - This study demonstrates the potential value of a combined 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.
KW - Digital Surface Model
KW - Digital Terrain Model
KW - Ground Penetrating Radar
KW - Photogrammetry
KW - Snow Density
KW - Snow Tube
KW - Snow Water Equivalent
KW - Unmanned Aerial Vehicle
U2 - 10.1002/hyp.14190
DO - 10.1002/hyp.14190
M3 - Journal article
VL - 35
JO - Hydrological Processes
JF - Hydrological Processes
SN - 0885-6087
IS - 5
M1 - e14190
ER -