Using Remote Sensing, Statistical, and Machine Learning Techniques to
Assess Alaskan River Ice Phenology and Thickness
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.