Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

• DOI: 10.5194/tc-18-3253-2024
• Publication Date: 2024-07-22T09:01:29Z
• Authors: Tate G. Meehan; Ahmad Hojatimalek...; Hans-Peter Marshall; Elias J. Deeb; Shad O'Neel; Daniel McGrath; Ryan W. Webb; Randall Bonnell; Mark S. Raleigh; Christopher Hiemstra; Kelly Elder
• Keywords: Ground-Penetrating Radar

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert depth water equivalent (SWE). However, retrieval of bulk unsolved, limited data available evaluate model mountainous terrain. Toward goal landscape-scale retrievals density, we estimated length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations airborne-lidar depths collected during mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements our approach include an automated layer-picking method that leverages GPR reflection coherence distributed lidar–GPR-retrieved with machine learning. The root-mean-square error between situ is 11 cm 27 kg m−3 46 mm SWE. median relative uncertainty SWE 13 %. Interactions wind, terrain, vegetation display corroborated controls on show observation agreement. Knowledge spatial patterns predictors critical accurate assessment essential research applications. spatially continuous over approximately 16 km2 may serve as necessary calibration validation stepping prospective techniques toward broad-scale retrieval.