SAR image wave spectra to retrieve the thickness of grease-pancake sea ice using viscous wave models

TL;DR

This paper develops a method to estimate the thickness of grease-pancake sea ice using SAR-derived wave spectra and viscous wave models, aiding climate monitoring in polar regions.

Contribution

It introduces a novel approach combining SAR wave spectra with viscous wave models to accurately retrieve GPI thickness, validated with satellite and in-situ data.

Findings

GPI thickness estimates agree with SMOS measurements.

The method effectively distinguishes GPI thickness in different sea regions.

Viscous wave models improve wave dispersion analysis in sea ice.

Abstract

Young sea ice composed of grease and pancake ice (GPI), as well as thin floes, considered to be the most common form of sea ice fringing Antarctica, is now becoming the 'new normal' also in the Arctic. Investigations to determine how an increase in GPI is affecting the climate in the far north and globally, require specific tools to monitor the GPI's thickness distribution. Directional wave spectra from satellite SAR imagery are used to determine the change in wave dispersion as a wave train enters GPI fields. The ice cover thickness is then estimated by fitting the dispersion data with two models of wave propagation in ice cover ocean: the Keller's model and the close-packing (CP) model. For both models, an empirical constitutive equation for GPI viscosity as a function of the ice thickness is derived and discussed. Examples of GPI thickness retrievals are shown for a Sentinel-1 C band SAR image taken in the Beaufort Sea on 1 November 2015, and three CosmoSkyMed X band SAR images taken in the Weddell Sea on March 2019. The estimated GPI thicknesses are consistent with concurrent SMOS measurements and the available local samplings.