Detecting seasonal ice dynamics in satellite images

• Published in: The Cryosphere
• Main Authors: Greene, Chad A., Gardner, Alex S., Andrews, Lauren C.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: article; Verlagsveröffentlichung; Antarc*; Antarctic; glacier; Greenland; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-14-4365-2020; https://noa.gwlb.de/receive/cop_mods_00054871; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054522/tc-14-4365-2020.pdf; https://tc.copernicus.org/articles/14/4365/2020/tc-14-4365-2020.pdf

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 a spotty satellite record that contains no optical observations during dark, polar winters. Furthermore, velocities obtained by feature tracking are marked by high noise when image pairs are separated by short time intervals and contain no direct insights into variability that occurs between images separated by long time intervals. In this paper, we describe a method of analyzing optical- or radar-derived 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 decadal 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.