Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a similar to 82,000 km(2) area at the 79 degrees N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016-2019, with an average size of 64,212 m(2). Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km(2) and a maximum individual lake size of 30 km(2). With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

Automated summary

The study demonstrates the feasibility of using multispectral satellite data to detect and monitor supraglacial meltwater ponds in western Greenland. By developing a sequence of algorithms, researchers successfully identified 880 individual lakes in the northeast Greenland glacier area, covering 479 time steps from 2016 to 2019, providing insights into short-term climate-glacier feedback.