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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-122
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-122
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 May 2020

Submitted as: research article | 26 May 2020

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This preprint is currently under review for the journal TC.

Detecting seasonal ice dynamics in satellite images

Chad A. Greene1, Alex S. Gardner1, and Lauren C. Andrews2 Chad A. Greene et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made difficult by the finite integration time over which ice velocities are measured from optical and repeat SAR imagery, and by a spotty satellite record that contains no optical observations throughout dark, polar winters. In this paper, we describe a method of analyzing feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr−1. We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice.

Chad A. Greene et al.

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Latest update: 04 Jul 2020
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Short summary
Seasonal variability is a fundamental characteristic of any Earth surface system, but we don't fully understand which of the world's glaciers speed up and slow down on an annual cycle. Such short-timescale accelerations may offer clues about how individual glaciers will respond to longer-term changes in climate, but understanding any behavior requires an ability to observe it. This paper describes how to extract the magnitude and timing of seasonal ice dynamics from satellite images.
Seasonal variability is a fundamental characteristic of any Earth surface system, but we don't...
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