Articles | Volume 14, issue 9
Research article
15 Sep 2020
Research article |  | 15 Sep 2020

Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays

Wolfgang Dierking, Harry L. Stern, and Jennifer K. Hutchings

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Cited articles

Atkinson, K. E.: An Introduction to Numerical Analysis, 2nd Edn., New York, John Wiley & Sons, ISBN 978-0-471-50023-0, 1989. 
Berg, A. and Eriksson, L. E. B.: Investigations of a hybrid algorithm for sea ice drift measurements using synthetic aperture radar images, IEEE T. Geosci. Remote S., 52, 5023–5033,, 2014. 
Bevington, P. R. and Robinson, D. K.: Data reduction and error analysis for the physical sciences, 3rd Edn., Mc Graw Hill, ISBN 0-07-247227-8, 2003. 
Bouchat, A. and Tremblay, B.: Reassessing the quality of sea-ice deformation estimates derived from the RADARSAT Geophysical Processor System and its impact on the spatio-temporal scaling statistics, J. Geophys. Res.-Oceans, online first,, 2020. 
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673,, 2015. 
Short summary
Monitoring deformation of sea ice is useful for studying effects of ice compression and divergent motion on the ice mass balance and ocean–ice–atmosphere interactions. In calculations of deformation parameters not only the measurement uncertainty of drift vectors has to be considered. The size of the area and the time interval used in the calculations have to be chosen within certain limits to make sure that the uncertainties of deformation parameters are smaller than their real magnitudes.