Articles | Volume 15, issue 6
The Cryosphere, 15, 2803–2818, 2021
The Cryosphere, 15, 2803–2818, 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.

Related authors

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468,,, 2019
Short summary

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856,,, 2021
Short summary
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450,,, 2021
Short summary
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529,,, 2021
Short summary
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186,,, 2021
Short summary
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822,,, 2021
Short summary

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: (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: (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,, 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: (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,, 2012. a, b
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.