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...

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Autores principales:	Yara Mohajerani, Seongsu Jeong, Bernd Scheuchl, Isabella Velicogna, Eric Rignot, Pietro Milillo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/852b197f28a74683a6a38efa9c33d11d
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spelling	oai:doaj.org-article:852b197f28a74683a6a38efa9c33d11d2021-12-02T11:37:22ZAutomatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning10.1038/s41598-021-84309-32045-2322https://doaj.org/article/852b197f28a74683a6a38efa9c33d11d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84309-3https://doaj.org/toc/2045-2322Abstract 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.Yara MohajeraniSeongsu JeongBernd ScheuchlIsabella VelicognaEric RignotPietro MililloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yara Mohajerani Seongsu Jeong Bernd Scheuchl Isabella Velicogna Eric Rignot Pietro Milillo Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
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
author	Yara Mohajerani Seongsu Jeong Bernd Scheuchl Isabella Velicogna Eric Rignot Pietro Milillo
author_facet	Yara Mohajerani Seongsu Jeong Bernd Scheuchl Isabella Velicogna Eric Rignot Pietro Milillo
author_sort	Yara Mohajerani
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	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/852b197f28a74683a6a38efa9c33d11d
work_keys_str_mv	AT yaramohajerani automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning AT seongsujeong automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning AT berndscheuchl automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning AT isabellavelicogna automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning AT ericrignot automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning AT pietromilillo automaticdelineationofglaciergroundinglinesindifferentialinterferometricsyntheticapertureradardatausingdeeplearning
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Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning

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