Articles | Volume 15, issue 6
The Cryosphere, 15, 2803–2818, 2021
https://doi.org/10.5194/tc-15-2803-2021
The Cryosphere, 15, 2803–2818, 2021
https://doi.org/10.5194/tc-15-2803-2021

Research article 18 Jun 2021

Research article | 18 Jun 2021

An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator

Joan Antoni Parera-Portell et al.

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Cited articles

Ackerman, S. A., Frey, R. A., Strabala, K., Liu, Y., Gumley, L. E., Baum, B., and Menzel, P.: Discriminating clear-sky from clouds with MODIS – Algorithm theoretical basis document, Tech. Rep., MODIS Cloud Mask Team and Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, USA, available at: https://modis-atmos.gsfc.nasa.gov/sites/default/files/ModAtmo/MOD35_ATBD_Collection6_0.pdf (last access: 8 October 2020), 2010. a, b, c, d, e
AMAP: Snow, Water, Ice and Permafrost, Summary for Policy-makers, Tech. Rep., Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, available at: https://www.amap.no/documents/doc/Snow-Water-Ice-and-Permafrost.-Summary-for-Policy-makers/1532 (last access: 8 October 2020), 2017. a
Brodzik, M. J. and Stewart, J. S.: Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5 [Data set], NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/3KB2JPLFPK3R, 2016. a
Brown, O. B. and Minnett, P. J.: MODIS Infrared Sea Surface Temperature Algorithm Theoretical Basis Document Version 2.0, Tech. Rep., University of Miami, Florida, USA, available at: https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf (last access: 8 October 2020), 1999. a
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012, 2012. a, b
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Short summary
We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.