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Using Remote Sensing, Statistical, and Machine Learning Techniques to Assess Alaskan River Ice Phenology and Thickness
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  • Dylan Blaskey,
  • Isabelle Racine,
  • Merritt Harlan,
  • Yifan Cheng,
  • Andrew J Newman,
  • Karl-Erich Lindenschmidt,
  • Michael N. Gooseff,
  • Keith Musselman
Dylan Blaskey
University of Colorado Boulder Institute of Arctic and Alpine Research

Corresponding Author:[email protected]

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Isabelle Racine
University of Colorado Boulder Institute of Arctic and Alpine Research
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Merritt Harlan
United States Geological Survey
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Yifan Cheng
University at Buffalo
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Andrew J Newman
National Center for Atmospheric Research (UCAR)
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Karl-Erich Lindenschmidt
Global Institute for Water Security
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Michael N. Gooseff
University of Colorado Boulder
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Keith Musselman
Institute of Arctic and Alpine Research
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Abstract

Climate change is leading to river ice thinning and shorter ice cover durations, posing significant risks to travel safety and ecosystem health. Due to limited in-situ observations in Alaska, models and remote sensing are employed to understand changing river conditions. This study conducts a comparative evaluation of statistical, machine learning, and remote sensing techniques to assess river ice presence and thickness across Alaska and the Yukon River basin. Sentinel-1 synthetic aperture radar data, climate model outputs, and in-situ river ice observations throughout Alaska are used to evaluate the regional applications of these techniques for determining river ice phenology and thickness. Our analysis reveals that ice presence can be accurately identified using Sentinel-1 images and climate data processed through machine learning models, achieving high accuracy across Alaska. Predicting ice break-up and freeze-up with these methods also yields high accuracy, with a root mean square error (RMSE) of 5.3 and 15.0 days, respectively, for machine learning at out-of-sample locations. Statistical, machine learning, and remote sensing techniques each demonstrated similar performance in determining ice thickness, with RMSEs ranging from 18 to 23 cm for out-of-sample years or locations. However, an ensemble of these methods significantly reduced the RMSE to 13 cm. Using the best-performing models, we generated high-resolution estimates of river ice phenology and thickness for major rivers in Alaska. The ensemble river ice thickness methods and the machine learning ice presence model show promise for widespread application in diverse regions, facilitating environmental monitoring and enhancing river ice safety.
21 Oct 2024Submitted to ESS Open Archive
21 Oct 2024Published in ESS Open Archive