Six Consecutive Seasons of High-Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery
Abstract
Fine-scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very-high-resolution satellite stereo images from WorldView-2, WorldView-3, and Pléiades-HR 1A/1B, we produced a six-year time series (2017–2022) of spatially continuous digital elevation models for an 874 km2 study area over Grand Mesa, Colorado. We generated high-resolution stereo snow depth maps that capture intra- and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near-contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies.
Michelle Hu
PhD student
Michelle Hu is a PhD student in the Hydrology and Hydrodynamics Program in the Terrain Analysis and Cryosphere Observation Lab. Her current research focuses on very-high-resolution remote sensing of seasonal snow using machine learning and optical and terrain-based data fusion in the Western US. She is broadly interested in water resources and climate interconnections, extreme events, transdiscplinary perspectives and people-focused data science applications.
David Shean
Assistant Professor
Shashank Bhushan
Postdoctoral scholar
Dr. Shashank Bhushan is a postdoctoral scholar in Civil and Environmental Engineering at the University of Washington. His research work focuses on developing methods and workflows for deriving high-resolution topography from satellite imagery, and using the derived products for quantifying mountain glacier dynamics and mass balance at various time scales. He is currently learning the ropes of photogrammetry, glaciology, coding, big data computing and most importantly, time management.