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

• Published in: Remote Sensing
• Main Authors: John Yackel, Torsten Geldsetzer, Mallik Mahmud, Vishnu Nandan, Stephen E. L. Howell, Randall K. Scharien, Hoi Ming Lam
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2019
• Subjects: snow thickness; first-year sea ice; scatterometer; backscatter (σ°) variance; ASCAT; QuikSCAT; Arctic; Canadian Arctic Archipelago; Arctic Archipelago; Sea ice
• Online Access: https://doi.org/10.3390/rs11040417

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.