Snow Thickness Estimation on First-Year Sea Ice from Late Winter Spaceborne Scatterometer Backscatter Variance

Abstract

Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in σ° from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to σ° detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily σ° compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in σ° at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter σ° variances likely indicate regions of similar snow thickness.

Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer

“…There is currently no efficient and cost-effective method for using satellites to measure on-ice snow depth at the fine scales (<~300 m) desired to improve sea-ice roughness maps for community use. However, snow depth has been estimated at larger scales using passive microwave data (Stroeve and others, 2006; Rostosky and others, 2018), Ku and Ka band altimeters (Lawrence and others, 2018) and spaceborne scatterometer data (Yackel and others, 2019) or at small temporal scales using airborne or in situ studies (e.g., using Operation IceBridge data) (Kurtz and Farrell, 2011; Newman and others, 2014; Lawrence and others, 2018). Models like SnowModel can reproduce FYI snow distributions given significant inputs for a region, including data on meteorology; sea-ice topography; sea-ice presence, depth and age; sea-ice mass balance; as well as snow depth mean, std dev.…”

InSAR Monitoring of Arctic Landfast Sea Ice Deformation Using L-Band ALOS-2, C-Band Radarsat-2 and Sentinel-1

“…Throughout most of the winter, sea ice near Cambridge Bay is covered by snow. Snow thickness on the FYI may affect microwave interaction [35], which affects the accuracy of measurement. In this study, we did not study such an effect.…”

“…These locations were 5-10 km away from the shore and observed with consistent coherence from time series interferograms from both ALOS-2 and Sentinel-1 images. According to the record, ice thickness near our P1-P4 locations was at 185 ± 5 cm in 23-25 April 2014 and at 166 ± 16 cm in 17-19 May 2018 [35]. No location near the harbor was selected for InSAR monitoring because of its small size and proximity to land, making it easily affected by the resampling and filtering process.…”

Morphological approaches to understanding Antarctic Sea ice thickness

“…In particular, the C-band advanced scatterometer (ASCAT) has been used to distinguish melt-freeze transitions on sea ice as well as surfacebased classification (Mortin et al, 2014;Lindell and Long, 2016). It is also possible to coarsely relate these surfaces types to snow depths, using the variance of the backscatter (e.g Yackel et al, 2019), although these are typically done with linear regressions which may not capture the complexity in snow depth variability.…”