Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2023
Research article |
| 24 Aug 2023
| 24 Aug 2023
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini
Institute of Geophysics, The University of Texas, Austin, TX 78758, USA
Ginny Catania
Institute of Geophysics, The University of Texas, Austin, TX 78758, USA
Department of Geological Sciences, The University of Texas, Austin, TX 78712, USA
Daniel T. Trugman
Nevada Seismological Laboratory, University of Reno, Nevada, NV 89557, USA
Related authors
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels J. Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, https://doi.org/10.5194/tc-16-3215-2022, 2022
Short summary
Short summary
Terminus traces have been used to understand how Greenland's glaciers have changed over time; however, manual digitization is time-intensive, and a lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39 000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
Kevin Shionalyn, Ginny Catania, Daniel Trugman, Michael Shahin, Leigh Stearns, and Denis Felikson
EGUsphere, https://doi.org/10.5194/egusphere-2025-3483, https://doi.org/10.5194/egusphere-2025-3483, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The ocean-facing front of a glacier changes with the seasons. We know this cycle is controlled by the shape and speed of the glacier as well as by the climate, but we do not have a full understanding of these processes. Our study uses 20 years of data and a machine learning model to predict this pattern and identifies which factors matter most. We find that while several factors influence the seasonal cycle, the shape of the glacier plays a key role in how much a glacier changes annually.
Andrew O. Hoffman, Paul T. Summers, Jenny Suckale, Knut Christianson, Ginny Catania, and Howard Conway
EGUsphere, https://doi.org/10.5194/egusphere-2025-1239, https://doi.org/10.5194/egusphere-2025-1239, 2025
Short summary
Short summary
In Antarctica, fast-flowing ice streams drive most ice loss. Radar data from Conway Ice Ridge reveal that the van der Veen and Mercer Ice Streams were wider ~3000 years ago and narrowed progressively. Numerical modeling demonstrates that small thickness changes can rapidly alter shear-margin locations. These findings offer crucial insights into Late Holocene Ice Sheet readvance.
Nicole Abib, David A. Sutherland, Rachel Peterson, Ginny Catania, Jonathan D. Nash, Emily L. Shroyer, Leigh A. Stearns, and Timothy C. Bartholomaus
The Cryosphere, 18, 4817–4829, https://doi.org/10.5194/tc-18-4817-2024, https://doi.org/10.5194/tc-18-4817-2024, 2024
Short summary
Short summary
The melting of ice mélange, or dense packs of icebergs and sea ice in glacial fjords, can influence the water column by releasing cold fresh water deep under the ocean surface. However, direct observations of this process have remained elusive. We use measurements of ocean temperature, salinity, and velocity bookending an episodic ice mélange event to show that this meltwater input changes the density profile of a glacial fjord and has implications for understanding tidewater glacier change.
Evan Carnahan, Ginny Catania, and Timothy C. Bartholomaus
The Cryosphere, 16, 4305–4317, https://doi.org/10.5194/tc-16-4305-2022, https://doi.org/10.5194/tc-16-4305-2022, 2022
Short summary
Short summary
The Greenland Ice Sheet primarily loses mass through increased ice discharge. We find changes in discharge from outlet glaciers are initiated by ocean warming, which causes a change in the balance of forces resisting gravity and leads to acceleration. Vulnerable conditions for sustained retreat and acceleration are predetermined by the glacier-fjord geometry and exist around Greenland, suggesting increases in ice discharge may be sustained into the future despite a pause in ocean warming.
Sophie Goliber, Taryn Black, Ginny Catania, James M. Lea, Helene Olsen, Daniel Cheng, Suzanne Bevan, Anders Bjørk, Charlie Bunce, Stephen Brough, J. Rachel Carr, Tom Cowton, Alex Gardner, Dominik Fahrner, Emily Hill, Ian Joughin, Niels J. Korsgaard, Adrian Luckman, Twila Moon, Tavi Murray, Andrew Sole, Michael Wood, and Enze Zhang
The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, https://doi.org/10.5194/tc-16-3215-2022, 2022
Short summary
Short summary
Terminus traces have been used to understand how Greenland's glaciers have changed over time; however, manual digitization is time-intensive, and a lack of coordination leads to duplication of efforts. We have compiled a dataset of over 39 000 terminus traces for 278 glaciers for scientific and machine learning applications. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for the Greenland Ice Sheet.
John Erich Christian, Alexander A. Robel, and Ginny Catania
The Cryosphere, 16, 2725–2743, https://doi.org/10.5194/tc-16-2725-2022, https://doi.org/10.5194/tc-16-2725-2022, 2022
Short summary
Short summary
Marine-terminating glaciers have recently retreated dramatically, but the role of anthropogenic forcing remains uncertain. We use idealized model simulations to develop a framework for assessing the probability of rapid retreat in the context of natural climate variability. Our analyses show that century-scale anthropogenic trends can substantially increase the probability of retreats. This provides a roadmap for future work to formally assess the role of human activity in recent glacier change.
Cited articles
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh,
M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., and
Nahavandi, S.: A review of uncertainty quantification in deep learning:
Techniques, applications and challenges, Inform. Fusion, 76, 243–297,
https://doi.org/10.1016/j.inffus.2021.05.008, 2021. a
Arendt, K. E., Agersted, M. D., Sejr, M. K., and Juul-Pedersen, T.: Glacial
meltwater influences on plankton community structure and the importance of
top-down control (of primary production) in a NE Greenland fjord, Estuar.
Coast. Shelf Sci., 183, 123–135, https://doi.org/10.1016/j.ecss.2016.08.026,
2016. a
Arrigo, K. R., Dijken, G. L. v., Castelao, R. M., Luo, H., Rennermalm, A. K.,
Tedesco, M., Mote, T. L., Oliver, H., and Yager, P. L.: Melting glaciers
stimulate large summer phytoplankton blooms in southwest Greenland waters,
Geophys. Res. Lett., 44, 6278–6285, https://doi.org/10.1002/2017gl073583,
2017. a
Aschwanden, A., Fahnestock, M. A., Truffer, M., Brinkerhoff, D. J., Hock, R.,
Khroulev, C., Mottram, R., and Khan, S. A.: Contribution of the Greenland
Ice Sheet to sea level over the next millennium, Sci. Adv., 5,
eaav9396, https://doi.org/10.1126/sciadv.aav9396, 2019. a
Bamler, R.: Principles of synthetic aperture radar, Surv. Geophys.,
21, 147–157, https://doi.org/10.1023/a:1006790026612, 2000. a
Bassis, J. N. and Jacobs, S.: Diverse calving patterns linked to glacier
geometry, Nat. Geosci., 6, 833–836, https://doi.org/10.1038/ngeo1887, 2013. a
Bhatia, M. P., Kujawinski, E. B., Das, S. B., Breier, C. F., Henderson, P. B.,
and Charette, M. A.: Greenland meltwater as a significant and potentially
bioavailable source of iron to the ocean, Nat. Geosci., 6, 274–278,
https://doi.org/10.1038/ngeo1746, 2013. a
Bjørk, A. A., Kruse, L. M., and Michaelsen, P. B.: Brief communication: Getting Greenland's glaciers right – a new data set of all official Greenlandic glacier names, The Cryosphere, 9, 2215–2218, https://doi.org/10.5194/tc-9-2215-2015, 2015. a
Brough, S., Carr, J. R., Ross, N., and Lea, J. M.: Exceptional retreat of
Kangerlussuaq Glacier, east Greenland, between 2016 and 2018, Front.
Earth Sci., 7, 123, https://doi.org/10.3389/feart.2019.00123, 2019. a
Bunce, C., Carr, J. R., Nienow, P. W., Ross, N., and Killick, R.: Ice front
change of marine-terminating outlet glaciers in northwest and southeast
Greenland during the 21st century, J. Glaciol., 64, 523–535,
https://doi.org/10.1017/jog.2018.44, 2018. a
Böning, C. W., Behrens, E., Biastoch, A., Getzlaff, K., and Bamber, J. L.:
Emerging impact of Greenland meltwater on deepwater formation in the North
Atlantic Ocean, Nat. Geosci., 9, 523–527, https://doi.org/10.1038/ngeo2740, 2016. a
Catania, G. A., Stearns, L. A., Sutherland, D. A., Fried, M. J., Bartholomaus,
T. C., Morlighem, M., Shroyer, E. L., and Nash, J. D.: Geometric Controls on
Tidewater Glacier Retreat in Central Western Greenland, J.
Geophys. Res.-Earth, 123, 2024–2038,
https://doi.org/10.1029/2017jf004499, 2018. a, b, c
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.:
Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation, in: Proceedings of the European Conference on Computer Vision
(ECCV), 8–14 September 2018, Munich, Germany, 801–818, https://doi.org/10.48550/arXiv.1802.02611, 2018. a, b, c
Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019, The Cryosphere, 15, 1663–1675, https://doi.org/10.5194/tc-15-1663-2021, 2021. a, b, c, d, e, f, g, h
Choi, Y., Morlighem, M., Rignot, E. J., and Wood, M. H.: Ice dynamics will
remain a primary driver of Greenland ice sheet mass loss over the next
century, Nat. Commun. Earth Environ., 2, 26,
https://doi.org/10.1038/s43247-021-00092-z, 2021. a
Cook, A. J., Holland, P. R., Meredith, M. P., Murray, T., Luckman, A., and
Vaughan, D. G.: Ocean forcing of glacier retreat in the western Antarctic
Peninsula, Science, 353, 283–286, https://doi.org/10.1126/science.aae0017, 2016. a, b
Davari, A., Islam, S., Seehaus, T., Hartmann, A., Braun, M., Maier, A.,
Christlein, V., and Davari, A.: On Mathews Correlation Coefficient and
Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation
in SAR Imagery, IEEE T. Geosci. Remote, 60,
1–12, https://doi.org/10.1109/tgrs.2021.3115883, 2021. a
Davari, A., Baller, C., Seehaus, T., Braun, M., Maier, A., and Christlein, V.:
Pixelwise Distance Regression for Glacier Calving Front Detection and
Segmentation, IEEE T. Geosci. Remote, 60, 1–10,
https://doi.org/10.1109/TGRS.2022.3158591, 2022. a
Enderlin, E. M., Howat, I. M., and Vieli, A.: High sensitivity of tidewater outlet glacier dynamics to shape, The Cryosphere, 7, 1007–1015, https://doi.org/10.5194/tc-7-1007-2013, 2013. a
Felikson, D., Bartholomaus, T. C., Catania, G. A., Korsgaard, N. J., Kjær,
K. H., Morlighem, M., Noël, B. P. Y., Broeke, M. R. v. d., Stearns, L. A.,
Shroyer, E. L., Sutherland, D. A., and Nash, J. D.: Inland thinning on the
Greenland ice sheet controlled by outlet glacier geometry, Nat.
Geosci., 10, 366–369, https://doi.org/10.1038/ngeo2934, 2017. a
Fried, M. J., Catania, G. A., Stearns, L. A., Sutherland, D. A., Bartholomaus,
T. C., Shroyer, E., and Nash, J.: Reconciling Drivers of Seasonal Terminus
Advance and Retreat at 13 Central West Greenland Tidewater Glaciers, J. Geophys. Res.-Earth, 123, 1590–1607,
https://doi.org/10.1029/2018jf004628, 2018. a, b
Gal, Y. and Ghahramani, Z.: Dropout as a Bayesian Approximation: Representing
Model Uncertainty in Deep Learning, Proceedings of the 33rd International
Conference on Machine Learning, 19–24 June 2016, New York, United States, 1050–1059, https://doi.org/10.48550/arXiv.1506.02142, 2016. a
Goliber, S., Black, T., Catania, G., Lea, J. M., Olsen, H., Cheng, D., Bevan, S., Bjørk, A., Bunce, C., Brough, S., Carr, J. R., Cowton, T., Gardner, A., Fahrner, D., Hill, E., Joughin, I., Korsgaard, N. J., Luckman, A., Moon, T., Murray, T., Sole, A., Wood, M., and Zhang, E.: TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications, The Cryosphere, 16, 3215–3233, https://doi.org/10.5194/tc-16-3215-2022, 2022. a, b, c, d, e, f, g
Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery, Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, 2022. a, b, c
Hansen, N., Simonsen, S. B., Boberg, F., Kittel, C., Orr, A., Souverijns, N., van Wessem, J. M., and Mottram, R.: Brief communication: Impact of common ice mask in surface mass balance estimates over the Antarctic ice sheet, The Cryosphere, 16, 711–718, https://doi.org/10.5194/tc-16-711-2022, 2022. a
Hartmann, A., Davari, A., Seehaus, T., Braun, M., Maier, A., and Christlein,
V.: Bayesian U-Net for Segmenting Glaciers in Sar Imagery, 2021 IEEE
International Geoscience and Remote Sensing Symposium IGARSS, 11–16 July 2021, Brussels, Belgium, 00, 3479–3482,
https://doi.org/10.1109/igarss47720.2021.9554292, 2021. a, b
Heidler, K., Mou, L., Baumhoer, C., Dietz, A., and Zhu, X. X.: HED-UNet:
Combined Segmentation and Edge Detection for Monitoring the Antarctic
Coastline, IEEE T. Geosci. Remote, 60, 1–14,
https://doi.org/10.1109/tgrs.2021.3064606, 2021. a
Heidler, K., Mou, L., Loebel, E., Scheinert, M., Lefèvre, S., and Zhu, X. X.:
Deep Active Contour Models for Delineating Glacier Calving Fronts, in: IGARSS
2022–2022 IEEE International Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia,
4490–4493, https://doi.org/10.1109/IGARSS46834.2022.9884819, 2022. a
Hill, E. A., Carr, J. R., Stokes, C. R., and Gudmundsson, G. H.: Dynamic changes in outlet glaciers in northern Greenland from 1948 to 2015, The Cryosphere, 12, 3243–3263, https://doi.org/10.5194/tc-12-3243-2018, 2018. a, b
Holland, D. M., Thomas, R. H., Young, B. D., Ribergaard, M. H., and Lyberth,
B.: Acceleration of Jakobshavn Isbræ triggered by warm subsurface ocean
waters, Nat. Geosci., 1, 659–664, https://doi.org/10.1038/ngeo316, 2008. a
Holzmann, M., Davari, A., Seehaus, T., Braun, M., Maier, A., and Christlein,
V.: Glacier Calving Front Segmentation Using Attention U-Net, 2021 IEEE
International Geoscience and Remote Sensing Symposium IGARSS, 11–16 July 2021, Brussels, Belgium, 3483–3486,
https://doi.org/10.1109/igarss47720.2021.9555067, 2021. a
Howat, I. M.: MEaSURES Greenland Ice Velocity: Selected Glacier Site Velocity
Maps from Optical Images, Version 2., NSIDC (National Snow and Ice Data Center),
https://doi.org/10.5067/VM5DZ20MYF5C, 2017. a, b
Howat, I. M., Negrete, A., and Smith, B. E.: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere, 8, 1509–1518, https://doi.org/10.5194/tc-8-1509-2014, 2014. a, b, c
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working
Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, Tech. rep., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a
Joughin, I., Shean, D. E., Smith, B. E., and Floricioiu, D.: A decade of variability on Jakobshavn Isbræ: ocean temperatures pace speed through influence on mélange rigidity , The Cryosphere, 14, 211–227, https://doi.org/10.5194/tc-14-211-2020, 2020. a
Kandel, I. and Castelli, M.: The effect of batch size on the generalizability
of the convolutional neural networks on a histopathology dataset, ICT
Express, 6, 312–315, https://doi.org/10.1016/j.icte.2020.04.010, 2020. a
Kehrl, L. M., Joughin, I., Shean, D. E., Floricioiu, D., and Krieger, L.:
Seasonal and interannual variabilities in terminus position, glacier
velocity, and surface elevation at Helheim and Kangerlussuaq Glaciers from
2008 to 2016, J. Geophys. Res.-Earth, 122,
1635–1652, https://doi.org/10.1002/2016jf004133, 2017. a
Khazendar, A., Fenty, I. G., Carroll, D., Gardner, A., Lee, C. M., Fukimori,
I., Wang, O., Zhang, H., Moller, D., Broeke, M. R., Dinardo, S., and Willis,
J.: Interruption of two decades of Jakobshavn Isbrae acceleration and
thinning as regional ocean cools, Nat. Geosci., 12, 277–283,
https://doi.org/10.1038/s41561-019-0329-3, 2019. a
King, M. D., Howat, I. M., Candela, S. G., Noh, M.-J., Jeong, S., Noël, B.
P. Y., Broeke, M. R. v. d., Wouters, B., and Negrete, A.: Dynamic ice loss
from the Greenland Ice Sheet driven by sustained glacier retreat, Nat.
Commun. Earth Environ., 1, 1,
https://doi.org/10.1038/s43247-020-0001-2, 2020. a
Kjeldsen, K. K., Khan, S. A., Colgan, W. T., MacGregor, J. A., and Fausto,
R. S.: Time Varying Ice Sheet Mask: Implications on Ice Sheet Mass Balance
and Crustal Uplift, J. Geophys. Res.-Earth, 125, e2020JF005775,
https://doi.org/10.1029/2020jf005775, 2020. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015. a
Loebel, E., Scheinert, M., Horwath, M., Heidler, K., Christmann, J., Phan,
L. D., Humbert, A., and Zhu, X. X.: Extracting Glacier Calving Fronts by Deep
Learning: The Benefit of Multispectral, Topographic, and Textural Input
Features, IEEE T. Geosci. Remote, 60, 1–12,
https://doi.org/10.1109/TGRS.2022.3208454, 2022. a
Luo, H., Castelao, R. M., Rennermalm, A. K., Tedesco, M., Bracco, A., Yager,
P. L., and Mote, T. L.: Oceanic transport of surface meltwater from the
southern Greenland ice sheet, Nat. Geosci., 9, 528–532,
https://doi.org/10.1038/ngeo2708, 2016. a
Marochov, M., Stokes, C. R., and Carbonneau, P. E.: Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods, The Cryosphere, 15, 5041–5059, https://doi.org/10.5194/tc-15-5041-2021, 2021. a
Miles, B. W. J., Stokes, C. R., Vieli, A., and Cox, N. J.: Rapid,
climate-driven changes in outlet glaciers on the Pacific coast of East
Antarctica, Nature, 500, 563–566, https://doi.org/10.1038/nature12382, 2013. a
Miles, B. W. J., Stokes, C. R., and Jamieson, S. S. R.: Pan-ice-sheet
glacier terminus change in East Antarctica reveals sensitivity of Wilkes Land
to sea-ice changes, Sci. Adv., 2, e1501350,
https://doi.org/10.1126/sciadv.1501350, 2016. a, b
Mohajerani, Y., Wood, M. H., Velicogna, I., and Rignot, E. J.: Detection of
Glacier Calving Margins with Convolutional Neural Networks: A Case Study,
Remote Sensing, 11, 74, https://doi.org/10.3390/rs11010074, 2019. a, b, c
Moon, T. and Joughin, I. R.: Changes in ice front position on Greenland's
outlet glaciers from 1992 to 2007, J. Geophys. Res.-Earth, 113, F02022, https://doi.org/10.1029/2007jf000927, 2008. a, b
Moon, T., Sutherland, D. A., Carroll, D., Felikson, D., Kehrl, L., and Straneo,
F.: Subsurface iceberg melt key to Greenland fjord freshwater budget,
Nat. Geosci., 11, 49–54, https://doi.org/10.1038/s41561-017-0018-z, 2018. a
Mouginot, J., Rignot, E. J., Bjørk, A. A., Broeke, M. R. v. d., Millan, R.,
Morlighem, M., Noël, B. P. Y., Scheuchl, B., and Wood, M. H.: Forty-six
years of Greenland Ice Sheet mass balance from 1972 to 2018, P.
Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.7280/d1mm37,
2019. a
Murray, T., Scharrer, K., Selmes, N., Booth, A. D., James, T. D., Bevan, S. L.,
Bradley, J. A., Cook, S., Llana, L. C., Drocourt, Y., Dyke, L. M., Goldsack,
A., Hughes, A. L. C., Luckman, A. J., and McGovern, J.: Extensive Retreat of
Greenland Tidewater Glaciers, 2000–2010,
Arct. Antarct. Alp. Res., 47, 427–447, https://doi.org/10.1657/aaar0014-049, 2015. a, b, c
Oltmanns, M., Karstensen, J., and Fischer, J.: Increased risk of a shutdown of
ocean convection posed by warm North Atlantic summers, Nat. Clim.
Change, 8, 1–6, https://doi.org/10.1038/s41558-018-0105-1, 2018. a
Overeem, I., Hudson, B. D., Syvitski, J. P., Mikkelsen, A. P. B., Hasholt, B.,
Broeke, M. R. v. d., Noël, B. P. Y., and Morlighem, M.: Substantial export
of suspended sediment to the global oceans from glacial erosion in
Greenland, Nat. Geosci., 10, 859–863, https://doi.org/10.1038/ngeo3046, 2017. a
Pan, X. L., Li, B. F., and Watanabe, Y. W.: Intense ocean freshening from melting glacier around the Antarctica during early twenty-first century, Sci. Rep., 12, 383, https://doi.org/10.1038/s41598-021-04231-6, 2022. a
Periyasamy, M., Davari, A., Seehaus, T., Braun, M., Maier, A., and Christlein,
V.: How to Get the Most Out of U-Net for Glacier Calving Front
Segmentation, IEEE J. Sel. Top. Appl., 15, 1712–1723, https://doi.org/10.1109/jstars.2022.3148033, 2022. a
Rignot, E., Xu, Y., Menemenlis, D., Mouginot, J., Scheuchl, B., Li, X.,
Morlighem, M., Seroussi, H., Broeke, M. v. d., Fenty, I., Cai, C., An, L.,
and Fleurian, B. d.: Modeling of ocean‐induced ice melt rates of five west
Greenland glaciers over the past two decades, Geophys. Res. Lett.,
43, 6374–6382, https://doi.org/10.1002/2016gl068784, 2016. a, b
Rignot, E. J., Mouginot, J., Scheuchl, B., Broeke, M. R. v. d., Wessem, M. v.,
and Morlighem, M.: Four decades of Antarctic Ice Sheet mass balance from
1979–2017, P. Natl. Acad. Sci. USA, 116, 1095–1103,
https://doi.org/10.1073/pnas.1812883116, 2019. a
Schild, K. M. and Hamilton, G. S.: Seasonal variations of outlet glacier
terminus position in Greenland, J. Glaciol., 59, 759–770,
https://doi.org/10.3189/2013jog12j238, 2013. a
Seroussi, H., Nakayama, Y., Larour, E. Y., Menemenlis, D., Morlighem, M.,
Rignot, E. J., and Khazendar, A.: Continued retreat of Thwaites Glacier,
West Antarctica, controlled by bed topography and ocean circulation,
Geophys. Res. Lett., 44, 6191–6199, https://doi.org/10.1002/2017gl072910,
2017. a
Slater, D. A., Benn, D. I., Cowton, T. R., Bassis, J. N., and Todd, J. A.:
Calving Multiplier Effect Controlled by Melt Undercut Geometry, J. Geophys. Res.-Earth, 126, e2021JF006191, https://doi.org/10.1029/2021jf006191, 2021. a
Small, D. and Schubert, A.: Guide to sentinel-1 geocoding, Remote Sensing Lab.
Univ. Zurich (RSL), Zürich, Switzerland, Tech. Rep. UZHS1-GC-AD, 2019. a
Straneo, F. and Heimbach, P.: North Atlantic warming and the retreat of
Greenland's outlet glaciers, Nature, 504, 36–43, https://doi.org/10.1038/nature12854,
2013.
a
Wood, M. H., Rignot, E. J., Fenty, I. G., An, L., Bjørk, A., Broeke, M. R.
v. d., Cai, C., Kane, E., Menemenlis, D., Millan, R., Morlighem, M.,
Mouginot, J., Noël, B. P. Y., Scheuchl, B., Velicogna, I., Willis, J. K.,
and Zhang, H.: Ocean forcing drives glacier retreat in Greenland, Sci.
Adv., 7, eaba7282, https://doi.org/10.1126/sciadv.aba7282, 2021. a, b
Xu, M., Papageorgiou, D. P., Abidi, S. Z., Dao, M., Zhao, H., and Karniadakis,
G. E.: A deep convolutional neural network for classification of red blood
cells in sickle cell anemia, PLOS Comput. Biol., 13, 1–27,
https://doi.org/10.1371/journal.pcbi.1005746, 2017. a
Ye, Y., Yang, C., Zhu, B., Zhou, L., He, Y., and Jia, H.: Improving
Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images, Remote
Sensing, 13, 928, https://doi.org/10.3390/rs13050928, 2021. a, b
Zhang, B., Zhang, E., Liu, L., Khan, S. A., Dam, T. v., Yao, Y., Bevis, M., and
Helm, V.: Geodetic measurements reveal short-term changes of glacial mass
near Jakobshavn Isbræ (Greenland) from 2007 to 2017, Earth Planet.
Sc. Lett., 503, 216–226, https://doi.org/10.1016/j.epsl.2018.09.029, 2018. a
Zhang. E.: AutoTerm: A “big data” repository of glacier termini delineated using deep learning (Version 4), Zenodo [data set], https://doi.org/10.5281/zenodo.7782039, 2022. a
Zhang, E.: AutoTerm: an automated pipeline of glacier termini delineated using deep learning, Zenodo [code], https://doi.org/10.5281/zenodo.8270875, 2023. a
Zhang, E., Liu, L., Huang, L., and Ng, K. S.: An automated, generalized,
deep-learning-based method for delineating the calving fronts of Greenland
glaciers from multi-sensor remote sensing imagery, Remote Sens.
Environ., 254, 112–265, https://doi.org/10.1016/j.rse.2020.112265, 2021. a, b, c, d, e, f, g, h, i, j
Zhang, P.: Numerical solution for the line curvature, GitHub [code], https://github.com/peijin94/PJCurvature (last access: 21 August 2023), 2018. a
Short summary
Glacier termini are essential for studying why glaciers retreat, but they need to be mapped automatically due to the volume of satellite images. Existing automated mapping methods have been limited due to limited automation, lack of quality control, and inadequacy in highly diverse terminus environments. We design a fully automated, deep-learning-based method to produce termini with quality control. We produced 278 239 termini in Greenland and provided a way to deliver new termini regularly.
Glacier termini are essential for studying why glaciers retreat, but they need to be mapped...
