Advancing the classification of supraglacial lake winter behaviours on the Greenland Ice Sheet using Sentinel-1 time series analysis

Abstract

Abstract Supraglacial lakes on the Greenland Ice Sheet are becoming increasingly abundant, impacting the distribution and routing of surface runoff with implications for drainage system evolution and ice flow. Their dynamics are well understood in summer when optical satellite imagery provides effective monitoring; however, their distribution and impact are less clear in winter when monitoring is restricted by the 24-hour darkness of the polar night. Using Sentinel-1 C-band Synthetic Aperture Radar (SAR) time series from 2018 to 2019, we assess whether an unsupervised time series clustering approach can be used to identify dynamic lake processes in winter. Our results show that: 1) winter lakes can be detected with an 87.6% accuracy; 2) SAR backscatter time series can be used to automatically and accurately (75.0%) classify winter lake behaviour (freeze-through, buried, no winter lake presence) without training data; 3) the temporal backscatter pattern of winter lake behaviour is consistent across regions and years and therefore generalisable; and 4) individual lakes can exhibit multiple behaviours over winter. Our work underscores the need for methods that capture spatio-temporal complexities in SAR backscatter, and provides insights into how C-band backscatter can be used to contribute towards year-round monitoring of lake dynamics across Greenland.