Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

• Published in: Remote Sensing
• Main Authors: Debvrat Varshney, Maryam Rahnemoonfar, Masoud Yari, John Paden, Oluwanisola Ibikunle, Jilu Li
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: ice layer thickness; semantic segmentation; fully convolutional networks; Snow Radar; Science; Q; Greenland; Ice Sheet
• Online Access: https://doi.org/10.3390/rs13142707; https://doaj.org/article/2ae1e73dfda2403d86b1730d1b0e0817

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.