Preprints
https://doi.org/10.5194/tc-2021-346
https://doi.org/10.5194/tc-2021-346
 
14 Dec 2021
14 Dec 2021
Status: a revised version of this preprint is currently under review for the journal TC.

Ice ridge density signatures in high resolution SAR images

Mikko Johannes Lensu and Markku Henrik Similä Mikko Johannes Lensu and Markku Henrik Similä
  • Finnish Meteorological Institute(FMI), Marine Research, Erik Palménin aukio 1, 00560 Helsinki, Finland

Abstract. The statistics of ice ridging signatures was studied using a high (1.25 m) and a medium (20 m) resolution SAR image over the Baltic sea ice cover, acquired in 2016 and 2011, respectively. Ice surface profiles measured by a 2011 Baltic campaign was used as ground truth data for both. The images did not delineate well individual ridges as linear features. This was assigned to the random, intermittent occurrence of ridge rubble block arrangements with bright SAR return. Instead, the ridging signature was approached in terms of the density of bright pixels and relations with the corresponding surface profile quantity, ice ridge density, were studied. In order to apply discrete statistics, these densities were quantified by counting bright pixel numbers (BPN) in pixel blocks of side length L, and by counting ridge sail numbers (RSN) in profile segments of length L. The scale L is a variable parameter of the approach. The other variable parameter is the pixel intensity threshold defining bright pixels, equivalently bright pixel percentage (BPP), or the ridge sail height threshold used to select ridges from surface profiles, respectively. As a sliding image operation the BPN count resulted in enhanced ridging signature and better applicability of SAR in ice information production. A distribution model for BPN statistics was derived by considering how the BPN values change in BPP changes. The model was found to apply over wide range of values for BPP and L. The same distribution model was found to apply to RSN statistics. This reduces the problem of correspondence between the two density concepts to connections between the parameters of the respective distribution models. The correspondence was studied for the medium resolution image for which the 2011 surface data set has close temporal match. The comparison was done by estimating ridge rubble coverage in 1 km2 squares from surface profile data and, on the other hand, assuming that the bright pixel density can be used as a proxy for ridge rubble coverage. Apart from a scaling factor, both were found to follow the presented distribution model.

Mikko Johannes Lensu and Markku Henrik Similä

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-346', Anonymous Referee #1, 21 Feb 2022
    • AC1: 'Reply on RC1', Mikko Lensu, 01 Apr 2022
  • RC2: 'Comment on tc-2021-346', Anonymous Referee #2, 07 Mar 2022
    • AC2: 'Reply on RC2', Mikko Lensu, 01 Apr 2022

Mikko Johannes Lensu and Markku Henrik Similä

Data sets

Safewin 2011 airborne EM sea ice thickness measurements in the Baltic Sea. Haas, C., Casey, A., Lensu, M. https://doi.pangaea.de/10.1594/PANGAEA.930545

Mikko Johannes Lensu and Markku Henrik Similä

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Short summary
Ice ridges form to a compressing ice cover. From above they show as walls up to few meters in height and extending even kilometres across the ice. Below they may reach tens of meters under sea surface. Ridges should be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and shown to agree with field observations.