Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1551-2021
https://doi.org/10.5194/tc-15-1551-2021
Research article
 | 
26 Mar 2021
Research article |  | 26 Mar 2021

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Stephan Paul and Marcus Huntemann

Related authors

Southern Weddell Sea surface freshwater flux modulated by icescape and atmospheric forcing
Lukrecia Stulic, Ralph Timmermann, Stephan Paul, Rolf Zentek, Günther Heinemann, and Torsten Kanzow
Ocean Sci., 19, 1791–1808, https://doi.org/10.5194/os-19-1791-2023,https://doi.org/10.5194/os-19-1791-2023, 2023
Short summary
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023,https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative
Stephan Paul, Stefan Hendricks, Robert Ricker, Stefan Kern, and Eero Rinne
The Cryosphere, 12, 2437–2460, https://doi.org/10.5194/tc-12-2437-2018,https://doi.org/10.5194/tc-12-2437-2018, 2018
Short summary
Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea
Andreas Preußer, Günther Heinemann, Sascha Willmes, and Stephan Paul
The Cryosphere, 10, 3021–3042, https://doi.org/10.5194/tc-10-3021-2016,https://doi.org/10.5194/tc-10-3021-2016, 2016
Short summary
Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery
S. Paul, S. Willmes, and G. Heinemann
The Cryosphere, 9, 2027–2041, https://doi.org/10.5194/tc-9-2027-2015,https://doi.org/10.5194/tc-9-2027-2015, 2015
Short summary

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023,https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023,https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023,https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023,https://doi.org/10.5194/tc-17-3695-2023, 2023
Short summary
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-123,https://doi.org/10.5194/tc-2023-123, 2023
Revised manuscript accepted for TC
Short summary

Cited articles

Ackerman, S., Frey, R., Strabala, K., Liu, Y., Gumley, L., Baum, B., and Menzel, P.: MODIS Atmosphere L2 Cloud Mask Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, Greenbelt, USA, https://doi.org/10.5067/MODIS/MOD35_L2.006, 2015. a
Adams, S., Willmes, S., Schröder, D., Heinemann, G., Bauer, M., and Krumpen, T.: Improvement and Sensitivity Analysis of Thermal Thin-Ice Thickness Retrievals, IEEE T. Geosci. Remote, 51, 3306–3318, 2013. a, b
Allaire, J. and Chollet, F.: keras: R Interface to “Keras”, r package version 2.3.0.0, available at: https://CRAN.R-project.org/package=keras, last access: 29 October 2020. a, b
Atkinson, P. M. and Tatnall, A. R. L.: Introduction Neural networks in remote sensing, Int. J. Remote Sens., 18, 699–709, https://doi.org/10.1080/014311697218700, 1997. a, b, c, d
Aulicino, G., Sansiviero, M., Paul, S., Cesarano, C., Fusco, G., Wadhams, P., and Budillon, G.: A New Approach for Monitoring the Terra Nova Bay Polynya through MODIS Ice Surface Temperature Imagery and Its Validation during 2010 and 2011 Winter Seasons, Remote Sens., 10, 366, https://doi.org/10.3390/rs10030366, 2018. a
Download
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.