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

• Published in: Remote Sensing
• Main Authors: Hochreuther, Philipp, Neckel, Niklas, Reimann, Nathalie, Humbert, Angelika, Braun, Matthias
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2021
• Subjects: ddc:550; Greenland; glacier
• Online Access: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/15717; https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-157170; https://doi.org/10.3390/rs13020205; https://opus4.kobv.de/opus4-fau/files/15717/remotesensing-13-00205-v2.pdf

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 ~82,000 km2 area at the 79 °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 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. 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.