Six Consecutive Seasons of High-Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery

被引:6
作者
Hu, J. Michelle [1 ]
Shean, David [1 ]
Bhushan, Shashank [1 ]
机构
[1] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
关键词
seasonal snow; Pleiades; digital elevation model; WorldView; SnowEx; co-registration; WATER EQUIVALENT; AIRBORNE; TERRAIN;
D O I
10.1029/2023GL104871
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 Pleiades-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. Detailed observations of snow depth can help us better understand how much water is stored as snow during important times of the year. We used high-resolution images from commercial satellites to create detailed maps of snow-covered surfaces for a study site in Colorado. Using a technique called stereo photogrammetry, we created precise three-dimensional models of surface elevation from these images. By subtracting a snow-free summer ground surface model from the winter snow surface models, we estimated snow depth over large areas and multiple years. Our satellite snow depth estimates agreed with snow depth measurements from airborne lidar and field campaigns. This satellite stereo approach helps us understand how mountain snow depth varies from year to year, providing valuable information to improve models and decisions for water resources management. Satellite stereo photogrammetry offers repeat, spatially continuous, high-resolution snow depth measurements over large areasStereo snow depth measurements are within similar to 0.13-0.33 m of near-contemporaneous airborne lidar and in situ measurementsStereo snow depth captures detailed intra- and interannual snow depth variability in low and high snow years
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页数:11
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