Articles | Volume 19, issue 7
https://doi.org/10.5194/tc-19-2431-2025
© Author(s) 2025. 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-19-2431-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2025
Research article |
| 08 Jul 2025
| 08 Jul 2025
Automatic grounding line delineation of DInSAR interferograms using deep learning
Sindhu Ramanath
CORRESPONDING AUTHOR
Remote Sensing Technology Institute, German Aerospace Center, Oberpfaffenhofen, Germany
School of Engineering and Design, Technical University of Munich, Munich, Germany
Lukas Krieger
Remote Sensing Technology Institute, German Aerospace Center, Oberpfaffenhofen, Germany
Remote Sensing Technology Institute, German Aerospace Center, Oberpfaffenhofen, Germany
Codruț-Andrei Diaconu
Remote Sensing Technology Institute, German Aerospace Center, Oberpfaffenhofen, Germany
School of Engineering and Design, Technical University of Munich, Munich, Germany
Konrad Heidler
School of Engineering and Design, Technical University of Munich, Munich, Germany
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Cited articles
Aschwanden, A., Bartholomaus, T. C., Brinkerhoff, D. J., and Truffer, M.: Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level, The Cryosphere, 15, 5705–5715, https://doi.org/10.5194/tc-15-5705-2021, 2021. a
Avbelj, J., Müller, R., and Bamler, R.: A metric for polygon comparison and building extraction evaluation, IEEE Geosci. Remote Sens. Lett., 12, 170–174, 2014. a
Bindschadler, R. and Choi, H.: High-resolution image-derived grounding and hydrostatic lines for the Antarctic Ice Sheet, U.S. Antarctic Program (USAP) Data Cente, https://doi.org/10.7265/N56T0JK2, 4932, 4913–4936, 2011. a
Brunt, K. M., Fricker, H. A., Padman, L., Scambos, T. A., and O’Neel, S.: Mapping the grounding zone of the Ross Ice Shelf, Antarctica, using ICESat laser altimetry, Ann. Glaciol., 51, 71–79, 2010. a
Catania, G., Hulbe, C., and Conway, H.: Grounding-line basal melt rates determined using radar-derived internal stratigraphy, J. Glaciol., 56, 545–554, 2010. a
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, Munich, Germany, 801–818, 2018. a
Christianson, K., Bushuk, M., Dutrieux, P., Parizek, B. R., Joughin, I. R., Alley, R. B., Shean, D. E., Abrahamsen, E. P., Anandakrishnan, S., Heywood, K. J., Kim, T. W., Lee, S. H., Nicholls, K., Stanton, T., Truffer, M., Webber, B. G. M., Jenkins, A., Jacobs, S., Bindschadler, R., and Holland, D. M.: Sensitivity of Pine Island Glacier to observed ocean forcing, Geophys. Res. Lett., 43, 10–817, 2016. a
Dawson, G. J. and Bamber, J. L.: Antarctic Grounding Line Mapping From CryoSat-2 Radar Altimetry: ANTARCTIC GROUNDING LINES FROM CRYOSAT-2, Geophys. Res. Lett., 44, 11886–11893, https://doi.org/10.1002/2017GL075589, 2017. a
Dawson, G. J. and Bamber, J. L.: Measuring the location and width of the Antarctic grounding zone using CryoSat-2, The Cryosphere, 14, 2071–2086, https://doi.org/10.5194/tc-14-2071-2020, 2020. a
Depoorter, M. A., Bamber, J. L., Griggs, J. A., Lenaerts, J. T. M., Ligtenberg, S. R. M., van den Broeke, M. R., and Moholdt, G.: Calving Fluxes and Basal Melt Rates of Antarctic Ice Shelves, Nature, 502, 89–92, https://doi.org/10.1038/nature12567, 2013. a
Falcon, W. and The PyTorch Lightning team: PyTorch Lightning, Zenodo [code], https://doi.org/10.5281/zenodo.3828935, 2019. a
Floricioiu, D., Krieger, L., A., C. T., and Baessler, M.: ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding line location for key glaciers, Antarctica, 1994–2020, v2.0, https://catalogue.ceda.ac.uk/uuid/7b3bddd5af4945c2ac508a6d25537f0a (last access: 3 July 2025), 2021. a
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021. a
Fricker, H. A. and Padman, L.: Ice shelf grounding zone structure from ICESat laser altimetry, Geophys. Res. Lett., 33, L15502, https://doi.org/10.1029/2006GL026907, 2006. a
Fricker, H. A., Coleman, R., Padman, L., Scambos, T. A., Bohlander, J., and Brunt, K. M.: Mapping the Grounding Zone of the Amery Ice Shelf, East Antarctica Using InSAR, MODIS and ICESat, Antarct. Sci., 21, 515–532, https://doi.org/10.1017/S095410200999023X, 2009. a, b
Friedl, P., Weiser, F., Fluhrer, A., and Braun, M. H.: Remote Sensing of Glacier and Ice Sheet Grounding Lines: A Review, Earth-Sci. Rev., 201, 102948, https://doi.org/10.1016/j.earscirev.2019.102948, 2020. a, b
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shazad, M., Yang, W., Bamler, R., and Zhu, X. X.: A survey of uncertainty in deep neural networks, Artif. Intell. Rev., 56, 1513–1589, https://doi.org/10.1007/s10462-023-10562-9, 2023. a, b
Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., Doust, A. N., Feldman, M. J., Gilbert, K. B., Hodge, J. G., Hoyer, J. S., Lin, A., Liu, S., Lizárraga, C., Lorence, A., Miller, M., Platon, E., Tessman, M., and Sax, T.: PlantCV v2: Image analysis software for high-throughput plant phenotyping, PeerJ, 5, e4088, https://doi.org/10.7717/peerj.4088, 2017. a
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
Groh, A.: ESA Climate Change Initiative Antarctica_Ice_Sheet_cci+ (AIS_cci+) Product Validation and Intercomparison Report (PVIR), https://climate.esa.int/media/documents/ST-UL-ESA-AISCCI-PVIR-0001_signed.pdf (last access: 3 July 2025), 2021b. a
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, 2022a. 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, 4490–4493, https://doi.org/10.1109/IGARSS46834.2022.9884819, 2022b. a
Hogg, A. E., Shepherd, A., Gilbert, L., Muir, A., and Drinkwater, M. R.: Mapping ice sheet grounding lines with CryoSat-2, Adv. Space Res., 62, 1191–1202, 2018. a
Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665–674, https://doi.org/10.5194/tc-13-665-2019, 2019. a
Jacobel, R. W., Robinson, A. E., and Bindschadler, R. A.: Studies of the grounding-line location on Ice Streams D and E, Antarctica, Ann. Glaciol., 20, 39–42, 1994. a
Joughin, I., Shean, D. E., Smith, B. E., and Dutrieux, P.: Grounding line variability and subglacial lake drainage on Pine Island Glacier, Antarctica, Geophys. Res. Lett., 43, 9093–9102, 2016. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, https://arxiv.org/abs/1412.6980 (last access: 4 July 2025), 2017. a
Lakshminarayanan, B., Pritzel, A., and Blundell, C.: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, in: Advances in Neural Information Processing Systems, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., vol. 30, Curran Associates, Inc., https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf (last access: 3 July 2025), 2017. a
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z.: Deeply-supervised nets, in: Artificial intelligence and statistics, 562–570, Pmlr, 2015. a
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L.: Distribution-free predictive inference for regression, J. Am. Stat. A., 113, 1094–1111, 2018. a
Li, T., Dawson, G. J., Chuter, S. J., and Bamber, J. L.: Mapping the grounding zone of Larsen C Ice Shelf, Antarctica, from ICESat-2 laser altimetry, The Cryosphere, 14, 3629–3643, https://doi.org/10.5194/tc-14-3629-2020, 2020. 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
MacGregor, J. A., Anandakrishnan, S., Catania, G. A., and Winebrenner, D. P.: The grounding zone of the Ross Ice Shelf, West Antarctica, from ice-penetrating radar, J. Glaciol., 57, 917–928, 2011. a
Marsh, O. J., Rack, W., Floricioiu, D., Golledge, N. R., and Lawson, W.: Tidally induced velocity variations of the Beardmore Glacier, Antarctica, and their representation in satellite measurements of ice velocity, The Cryosphere, 7, 1375–1384, https://doi.org/10.5194/tc-7-1375-2013, 2013. a
McMillan, M., Shepherd, A., Nienow, P., and Leeson, A.: Tide model accuracy in the Amundsen Sea, Antarctica, from radar interferometry observations of ice shelf motion, J. Geophys. Res.-Oceans, 116, C11008, https://doi.org/10.1029/2011JC007294, 2011. a
Mouginot, J., Rignot, E., and Scheuchl, B.: Continent-wide, interferometric SAR phase, mapping of Antarctic ice velocity, Geophys. Res. Lett., 46, 9710–9718, 2019. a
Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023. a
Padman, L., King, M., Goring, D., Corr, H., and Coleman, R.: Ice-shelf elevation changes due to atmospheric pressure variations, J. Glaciol., 49, 521–526, 2003. a
Padman, L., Erofeeva, S., and Fricker, H.: Improving Antarctic tide models by assimilation of ICESat laser altimetry over ice shelves, Geophys. Res. Lett., 35, L22504, https://doi.org/10.1029/2008GL035592, 2008. a, b
Parizzi, A.: Potential of an Automatic Grounding Zone Characterization Using Wrapped InSAR Phase, in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 802–805, IEEE, Waikoloa, HI, USA, ISBN 978-1-72816-374-1, https://doi.org/10.1109/IGARSS39084.2020.9323199, 2020. a
Pattyn, F. and Morlighem, M.: The uncertain future of the Antarctic Ice Sheet, Science, 367, 1331–1335, https://doi.org/10.1126/science.aaz5487, 2020. a
Ramanath Tarekere, S.: Deep learning based automatic grounding line delineation in DInSAR interferograms, Zenodo [data set], https://doi.org/10.5281/zenodo.15228974, 2025. a
Ramanath Tarekere, S. and Krieger, L.: automatic_gll_delineationm GitLab [code], https://github.com/sinramtar/automatic-gl-delineation.git (last access: 4 July 2025), 2025. a
Ramanath Tarekere, S., Krieger, L., Heidler, K., and Floricioiu, D.: Deep Neural Network Based Automatic Grounding Line Delineation In Dinsar Interferograms, in: IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, 183–186, https://doi.org/10.1109/IGARSS52108.2023.10282372, 2023. a, b
Riedel, B., Nixdorf, U., Heinert, M., Eckstaller, A., and Mayer, C.: The response of the Ekströmisen (Antarctica) grounding zone to tidal forcing, Ann. Glaciol., 29, 239–242, 1999. a
Rignot, E.: Tidal Motion, Ice Velocity and Melt Rate of Petermann Gletscher, Greenland, Measured from Radar Interferometry, J. Glaciol., 42, 476–485, https://doi.org/10.3189/S0022143000003464, 1996. a
Rignot, E.: Observations of grounding zones are the missing key to understand ice melt in Antarctica, Nat. Clim. Change, 13, 1010–1013, https://doi.org/10.1038/s41558-023-01819-w, 2023. a
Rignot, E. and Jacobs, S. S.: Rapid Bottom Melting Widespread near Antarctic Ice Sheet Grounding Lines, Science, 296, 2020–2023, https://doi.org/10.1126/science.1070942, 2002. a
Rignot, E. and Thomas, R. H.: Mass Balance of Polar Ice Sheets, Polar Sci., 297, 1502–1506, https://doi.org/10.1126/science.1073888, 2002. a
Rignot, E., Bamber, J. L., Van Den Broeke, M. R., Davis, C., Li, Y., Van De Berg, W. J., and Van Meijgaard, E.: Recent Antarctic Ice Mass Loss from Radar Interferometry and Regional Climate Modelling, Nat. Geosci., 1, 106–110, https://doi.org/10.1038/ngeo102, 2008. a
Rignot, E., Mouginot, J., and Scheuchl, B.: Antarctic grounding line mapping from differential satellite radar interferometry, Geophys. Res. Lett., 38, L10504, https://doi.org/10.1029/2011GL047109, 2011. a, b
Rignot, E., Mouginot, J., and Scheuchl, B.: MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry, Version 2, NASA, https://doi.org/10.5067/IKBWW4RYHF1Q, 2016. a
Robel, A. A., Seroussi, H., and Roe, G. H.: Marine ice sheet instability amplifies and skews uncertainty in projections of future sea-level rise, P. Natl. Acad. Sci. USA, 116, 14887–14892, https://doi.org/10.1073/pnas.1904822116, 2019. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, 234–241, Springer, 2015. a
Scambos, T. A., Haran, T. M., Fahnestock, M., Painter, T., and Bohlander, J.: MODIS-based Mosaic of Antarctica (MOA) data sets: Continent-wide surface morphology and snow grain size, Remote Sens. Environ., 111, 242–257, 2007. a
Schoof, C.: Ice sheet grounding line dynamics: Steady states, stability, and hysteresis, J. Geophys. Res., 112, F03S28, https://doi.org/10.1029/2006JF000664, 2007. a
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020. a
Smith, A.: The use of tiltmeters to study the dynamics of Antarctic ice-shelf grounding lines, J. Glaciol., 37, 51–58, 1991. a
Stephenson, S. N.: Glacier Flexure and the Position of Grounding Lines: Measurements By Tiltmeter on Rutford Ice Stream, Antarctica, Ann. Glaciol., 5, 165–169, https://doi.org/10.3189/1984AoG5-1-165-169, 1984. a
Stephenson, S. N. and Doake, C. S. M.: Tidal flexure of ice shelves measured by tiltmeter, Nature, 282, 496–497, https://doi.org/10.1038/282496a0, 1979. a
Sutterly, T. C., Alley, K., Brunt, K., Howard, S., Padman, L., and Siegfried, M.: pyTMD: Python based tidal prediction software, Zenodo [code], https://doi.org/10.5281/zenodo.5555395, 2017. a
Tollenaar, V., Zekollari, H., Pattyn, F., Rußwurm, M., Kellenberger, B., Lhermitte, S., Izeboud, M., and Tuia, D.: Where the White Continent is blue: Deep learning locates bare ice in Antarctica, Geophys. Res. Lett., 51, e2023GL106285, https://doi.org/10.1029/2023GL106285, 2024. a
Uratsuka, S., Nishio, F., and Mae, S.: Internal and basal ice changes near the grounding line derived from radio-echo sounding, J. Glaciol., 42, 103–109, 1996. a
Vaughan, D. G.: Investigating Tidal Flexure on an Ice Shelf Using Kinematic GPS, Ann. Glaciol., 20, 372–376, https://doi.org/10.3189/1994AoG20-1-372-376, 1994. a
Wallis, B. J., Hogg, A. E., Zhu, Y., and Hooper, A.: Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method, The Cryosphere, 18, 4723–4742, https://doi.org/10.5194/tc-18-4723-2024, 2024. a
Weertman, J.: Stability of the Junction of an Ice Sheet and an Ice Shelf, J. Glaciol., 13, 3–11, https://doi.org/10.3189/S0022143000023327, 1974. a
Wessel, B.: TanDEM-X Ground Segment – DEM Products Specification Document, https://elib.dlr.de/108014/1/TD-GS-PS-0021_DEM-Product-Specification_v3.1.pdf (last access: 3 July 2025), 2016. a
Wessel, B., Huber, M., Wohlfart, C., Bertram, A., Osterkamp, N., Marschalk, U., Gruber, A., Reuß, F., Abdullahi, S., Georg, I., and Roth, A.: TanDEM-X PolarDEM 90 m of Antarctica: generation and error characterization, The Cryosphere, 15, 5241–5260, https://doi.org/10.5194/tc-15-5241-2021, 2021. a, b
Zhang, T. and Suen, C.: A fast parallel algorithm for thinning digital patterns, Communications of the ACM, 27, 236–239, 1984. a
Short summary
Grounding lines are geophysical features that divide ice masses on the bedrock and floating ice shelves. Their accurate location is required for calculating the mass balance of ice sheets and glaciers in Antarctica and Greenland. Human experts still manually detect them in satellite-based interferometric radar images, which is inefficient given the growing volume of data. We have developed an artificial-intelligence-based automatic detection algorithm to generate Antarctica-wide grounding lines.
Grounding lines are geophysical features that divide ice masses on the bedrock and floating ice...
