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

Abstract 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-consu...

Full description

Bibliographic Details
Published in:Scientific Reports
Main Authors: Mohajerani, Yara, Jeong, Seongsu, Scheuchl, Bernd, Velicogna, Isabella, Rignot, Eric, Milillo, Pietro
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2021
Subjects:
Online Access:http://dx.doi.org/10.1038/s41598-021-84309-3
http://www.nature.com/articles/s41598-021-84309-3.pdf
http://www.nature.com/articles/s41598-021-84309-3
id crspringernat:10.1038/s41598-021-84309-3
record_format openpolar
spelling crspringernat:10.1038/s41598-021-84309-3 2023-05-15T14:12:40+02:00 Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro 2021 http://dx.doi.org/10.1038/s41598-021-84309-3 http://www.nature.com/articles/s41598-021-84309-3.pdf http://www.nature.com/articles/s41598-021-84309-3 en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 11, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2021 crspringernat https://doi.org/10.1038/s41598-021-84309-3 2022-01-04T11:42:01Z Abstract 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. Article in Journal/Newspaper Antarc* Antarctica Getz Ice Shelf Ice Sheet Ice Shelf West Antarctica Springer Nature (via Crossref) Getz ENVELOPE(-145.217,-145.217,-76.550,-76.550) Getz Ice Shelf ENVELOPE(-126.500,-126.500,-74.250,-74.250) West Antarctica Scientific Reports 11 1
institution Open Polar
collection Springer Nature (via Crossref)
op_collection_id crspringernat
language English
topic Multidisciplinary
spellingShingle Multidisciplinary
Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
topic_facet Multidisciplinary
description Abstract 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.
format Article in Journal/Newspaper
author Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
author_facet Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
author_sort Mohajerani, Yara
title Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
title_short Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
title_full Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
title_fullStr Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
title_full_unstemmed Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
title_sort automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
publisher Springer Science and Business Media LLC
publishDate 2021
url http://dx.doi.org/10.1038/s41598-021-84309-3
http://www.nature.com/articles/s41598-021-84309-3.pdf
http://www.nature.com/articles/s41598-021-84309-3
long_lat ENVELOPE(-145.217,-145.217,-76.550,-76.550)
ENVELOPE(-126.500,-126.500,-74.250,-74.250)
geographic Getz
Getz Ice Shelf
West Antarctica
geographic_facet Getz
Getz Ice Shelf
West Antarctica
genre Antarc*
Antarctica
Getz Ice Shelf
Ice Sheet
Ice Shelf
West Antarctica
genre_facet Antarc*
Antarctica
Getz Ice Shelf
Ice Sheet
Ice Shelf
West Antarctica
op_source Scientific Reports
volume 11, issue 1
ISSN 2045-2322
op_rights https://creativecommons.org/licenses/by/4.0
https://creativecommons.org/licenses/by/4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.1038/s41598-021-84309-3
container_title Scientific Reports
container_volume 11
container_issue 1
_version_ 1766285001411788800