Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size over Arctic Sea Ice

Abstract

Snow cover on Arctic sea ice is crucial to understanding sea ice evolution and the heat budget in the rapidly changing Arctic Ocean. Despite advancements in remote sensing technology, accurately estimating snow depth, grain size, and density on a large scale remains challenging. This study examines the influence of snow density and grain size on passive microwave satellite observation data. Data was collected from December 2012 to February 2016 using AMSR2 brightness temperature data, and snow depth was estimated using ice-tethered ice mass balance (IMB) data from the Cold Regions Research and Engineering Laboratory (CRREL). A two-stream approximation radiative transfer model that accounts for the scattering effect of the snow layer over Arctic sea ice was used. The study found that the slope in the linear relationship between snow density and the brightness temperature indices (such as gradient ratio (GR) and brightness temperature ratio (BTR)) is highly sensitive to the density and grain size of the snow layer. Using this dependence, the temporal changes in physical state of the snow layer was inferred from the observation over the winter season. The radiative transfer model simulations revealed that the grain size of the snow layer decreases during winter as the snow density increases, likely due to the counterbalancing effect of new snow on the temporal variability in grain size increasing caused by snow metamorphism.