Articles | Volume 20, issue 7
https://doi.org/10.5194/tc-20-3759-2026
https://doi.org/10.5194/tc-20-3759-2026
Research article
 | 
03 Jul 2026
Research article |  | 03 Jul 2026

Mapping daily snow depth with machine learning and airborne lidar across two contrasting snowpacks

Caleb G. Pan, Jeremy Johnston, Jennifer M. Jacobs, and Shad O'Neel

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Short summary
We developed a simple method to turn a few airborne snow-mapping flights and one daily snow record into continuous maps showing how snow depth changes each day. Tested in Idaho and New Hampshire, the approach works well in both deep and shallow snow regions and helps plan when and how often to fly lidar surveys for the best results.
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