Articles | Volume 13, issue 9
https://doi.org/10.5194/tc-13-2421-2019
https://doi.org/10.5194/tc-13-2421-2019
Research article
 | 
17 Sep 2019
Research article |  | 17 Sep 2019

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Anne Braakmann-Folgmann and Craig Donlon

Related authors

Enhanced neural network classification for Arctic summer sea ice
Anne Braakmann-Folgmann, Jack C. Landy, Geoffrey Dawson, and Robert Ricker
EGUsphere, https://doi.org/10.5194/egusphere-2025-2789,https://doi.org/10.5194/egusphere-2025-2789, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Anticipating CRISTAL: An exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023
Jack C. Landy, Claude de Rijke-Thomas, Carmen Nab, Isobel Lawrence, Isolde A. Glissenaar, Robbie D. C. Mallett, Renée M. Fredensborg Hansen, Alek Petty, Michel Tsamados, Amy R. Macfarlane, and Anne Braakmann-Folgmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2904,https://doi.org/10.5194/egusphere-2024-2904, 2024
Short summary
Mapping the extent of giant Antarctic icebergs with deep learning
Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond
The Cryosphere, 17, 4675–4690, https://doi.org/10.5194/tc-17-4675-2023,https://doi.org/10.5194/tc-17-4675-2023, 2023
Short summary
Tracking changes in the area, thickness, and volume of the Thwaites tabular iceberg “B30” using satellite altimetry and imagery
Anne Braakmann-Folgmann, Andrew Shepherd, and Andy Ridout
The Cryosphere, 15, 3861–3876, https://doi.org/10.5194/tc-15-3861-2021,https://doi.org/10.5194/tc-15-3861-2021, 2021
Short summary

Related subject area

Discipline: Snow | Subject: Remote Sensing
Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025,https://doi.org/10.5194/tc-19-2315-2025, 2025
Short summary
Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis
Haorui Sun, Yiwen Fang, Steven A. Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025,https://doi.org/10.5194/tc-19-2017-2025, 2025
Short summary
Mapping seasonal snow melting in Karakoram using SAR and topographic data
Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek
The Cryosphere, 19, 1621–1639, https://doi.org/10.5194/tc-19-1621-2025,https://doi.org/10.5194/tc-19-1621-2025, 2025
Short summary
Do we still need reflectance? From radiance to snow properties in mountainous terrain: a case study with the EMIT imaging spectrometer
Niklas Bohn, Edward H. Bair, Philip G. Brodrick, Nimrod Carmon, Robert O. Green, Thomas H. Painter, and David R. Thompson
The Cryosphere, 19, 1279–1302, https://doi.org/10.5194/tc-19-1279-2025,https://doi.org/10.5194/tc-19-1279-2025, 2025
Short summary
Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
The Cryosphere, 18, 6027–6059, https://doi.org/10.5194/tc-18-6027-2024,https://doi.org/10.5194/tc-18-6027-2024, 2024
Short summary

Cited articles

Aaboe, S., Breivik, L.-A., Soerensen, A., Eastwood S., and Lavergne, T.: Global sea ice edge and type user's manual-v13. Technical report/SAFOSI/CDOP2/MET-Norway/TEC/MA/205, EUMETSAT OSI SAF-Ocean and Sea Ice Satellite Application Facility, 2016. a, b
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, https://doi.org/10.5194/tc-4-373-2010, 2010. a, b, c
Blanchard-Wrigglesworth, E., Webster, M. A., Farrell, S. L., and Bitz, C. M.: Reconstruction of Snow on Arctic Sea Ice, J. Geophys. Res.-Oceans, 123, 3588–3602, https://doi.org/10.1002/2017JC013364, 2018. a
Download
Short summary
Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow depth on sea ice from satellite microwave radiometer measurements at several frequencies using neural networks (NNs). We evaluate our results with airborne snow depth measurements and compare them to three other established snow depth algorithms. We show that our NN results agree better with the airborne data than the other algorithms. This is also advantageous for sea ice thickness calculation.
Share