Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images

Abstract

Abstract. In this paper we introduce a parameter for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric (CP) synthetic aperture radar (SAR) images. The parameter is denoted as the "CP ratio". In model simulations we investigated the sensitivity of the CP ratio to the dielectric constant, ice thickness, ice surface roughness, and radar incidence angle. From the results of the simulations we deduced optimal sea ice conditions and radar incidence angles for the ice thickness retrieval. C-band SAR data acquired over the Labrador Sea in circular transmit and linear receive (CTLR) mode were generated from RADARSAT-2 quad-polarization images. In comparison with results from helicopter-borne measurements, we tested different empirical equations for the retrieval of ice thickness. An exponential fit between the CP ratio and ice thickness provides the most reliable results. Based on a validation using other compact polarimetric SAR images from the same region, we found a root mean square (rms) error of 8 cm and a maximum correlation coefficient of 0.94 for the retrieval procedure when applying it to level ice between 0.1 and 0.8 m thick.

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

“…In this case, the ratio is independent of roughness and dependent on the permittivity of the medium, thus the dielectric constants of water and ice. A number of studies have observed a decrease in the co-polarization ratio with increased thin ice thickness (Drinkwater et al 1991;Onstott 1992;Nghiem and Bertoia, 2001;Geldsetzer and Yackel 2009;Zhang et al 2016). Correlations between ice thickness and the co-polarization ratio are attributed to the changing dielectric constants of the ice surface due to desalination during growth (Zhang et al 2016).…”

Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective

“…The σ RR feature is categorized into Group 2 where depolarization due to volume scattering dominates. Further, 1 − m, ρ (RH,RV) , and γ (RR,RL) respond to depolarization likely due to multiscattering from rough surfaces, which is Group 3, while γ (RH,RV) is in Group 4 where it responds to polarization differences in resonant Bragg scattering and also in the Fresnel coefficients (see [24,25] for more information). Finally, the independent group, where the features are likely to give additional information that may be complementary to the other features [9].…”

Morphological approaches to understanding Antarctic Sea ice thickness

“…A detailed review of the state of sea ice classification using SAR data can be found in Zakhvatkina et al (2019). There are few studies linking SIT to SAR images: Kim et al (2012) found a weak linear relationship between SIT and depolarization of return radar signatures may exist for deformed ice surfaces; Shi et al (2014) used a linear model with various SAR parameters to predict SIT; and Zhang et al (2016) found that the thickness of undeformed first-year ice < 0.8 m could be exponentially related to a ratio of the polarimetric scattering returns. Deep learning techniques show great promise for sea ice problems and can have a large variety of input data.…”