Articles | Volume 15, issue 3
The Cryosphere, 15, 1551–1565, 2021
The Cryosphere, 15, 1551–1565, 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

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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,, 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, available at:, 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,, 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,, 2018. a
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.