Using a Random Forest Model to Combine Airborne Lidar and Snotel Data for Daily Estimates of Snow Depth Across Mountain Drainage Basins of Colorado

Abstract Machine learning (ML) has emerged as an effective tool for estimating snow depth and snow water equivalent at unsampled times and locations. Airborne lidar surveys are particularly useful for ML applications: the high‐resolution, high‐precision snow depth data allow for algorithm training a...

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發表在:	Water Resources Research
Main Authors:	J. N. Herbert, M. S. Raleigh, E. E. Small
格式:	Article
語言:	英语
出版:	Wiley 2025-08-01
主題:	snow hydrology machine learning lidar remote sensing Snotel hydrology
在線閱讀:	https://doi.org/10.1029/2024WR039775