Spatially Extensive Ground‐Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations. Issue 11 (21st November 2019)

• Record Type: Journal Article
• Authors: McGrath, Daniel; Webb, Ryan; Shean, David; Bonnell, Randall; Marshall, Hans‐Peter; Painter, Thomas H.; Molotch, Noah P.; Elder, Kelly; Hiemstra, Christopher; Brucker, Ludovic
• Is Part Of: Water resources research. Volume 55:Issue 11(2019)
• Page Start: 10026
• Page End: 10036
• Publication Date: 2019-11-21
• Subjects: Seasonal snow -- ground‐penetrating radar -- SnowEx -- remote sensing; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR024907
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Seasonal snow is an important component of Earth's hydrologic cycle and climate system, yet it remains challenging to consistently and accurately measure snow depth and snow water equivalent (SWE) across the range of diverse snowpack conditions that exist on Earth. The NASA SnowEx campaign is focused on addressing the primary gaps in snow remote sensing in order to gain an improved spatiotemporal understanding of this important resource and to further efforts toward a future satellite‐based snow remote sensing mission. Ground‐penetrating radar (GPR) is an efficient and mature approach for measuring snow depth and SWE. We collected ~1.3 million GPR snow depth observations during the NASA SnowEx 2017 campaign, yielding a spatially extensive (~133‐km total length) and high‐resolution (~10‐cm lateral spacing) validation data set to assess various remote sensing and modeling approaches. We found high correlation between the GPR and manual snow probe derived snow depths ( r = 0.89, p < 0.0001, root‐mean‐square error (RMSE) = 18 cm), but a median difference of −10 cm, which we attribute, in part, to probe penetration into the unfrozen subsurface. We also compared GPR‐derived snow depths to two other independent estimates of snow depth, as an example of how this data set can be used for validation of remote sensing techniques: Airborne Snow Observatory lidar‐derived snow depths ( r = 0.90, p < 0.0001, median difference = −1 cm, RMSE = 14 cm) and preliminary DigitalGlobe WorldView‐3 satellite‐derived snow depths ( r = 0.70, p < 0.0001, median difference = −3 cm, RMSE = 24 cm). Key Points: Ground‐penetrating radar surveys during SnowEx 2017 provide a high‐resolution, spatially extensive, and accurate validation data set Radar‐derived snow depths exhibit high correlation with three independent methods: snow probes, airborne lidar, and satellite stereo imagery Median differences between GPR and other approaches are between −1 and −10 cm, which is within published accuracies of these techniques