Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning

Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mos...

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Vydáno v:Scientific reports Ročník 11; číslo 1; s. 4992 - 10
Hlavní autoři: Mohajerani, Yara, Jeong, Seongsu, Scheuchl, Bernd, Velicogna, Isabella, Rignot, Eric, Milillo, Pietro
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 02.03.2021
Nature Publishing Group
Nature Portfolio
Témata:
704/106
704/106/125
Automation
Bending stresses
Deep learning
Digitization
Equilibrium
Glaciers
Humanities and Social Sciences
Ice sheets
Ice shelves
Learning algorithms
Machine learning
Mathematical models
multidisciplinary
Neural networks
Radar
Satellites
Science
Science (multidisciplinary)
Sea level rise
Antarctica
ISSN:2045-2322, 2045-2322
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Shrnutí:Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-84309-3