“You Can Do It” : Drone pilot mentoring on Rocky Mountain glaciers

Canada First Research Excellence FundNon-Peer ReviewedPersonal account by a research technician about interactions leading to acquisition of new data collection skills

The Influence of Surface Sediment Presence on Observed Passive Microwave Brightness Temperatures of First-Year Sea Ice during the Summer Melt Period

Knowledge on the influence of sea ice sediment on passive microwave brightness temperatures (TB) is currently limited, leading to potential inaccuracies in derived sea ice concentrations where this ice exists. We propose that sediment may influence TB in two ways: (i) by altering the surface dielectrics, or (ii) by generating differential melt rates across the ice surface, increasing surface roughness. This study will examine the second proposed hypothesis through a multi-platform analysis, combining in-situ passive microwave and unmanned aerial vehicle (UAV) data. UAV image analysis shows a negative relationship between surface elevation and sediment concentration. Comparing this with observed TB shows that horizontally polarized emissions are the most sensitive to rougher ice surfaces with 19 and 37 GHz TB decreasing rapidly with increased incidence angle. At a 55° incidence angle, 89 GHz offers the greatest potential for discriminating sea ice surfaces influenced by sediment presence, as TB are greater in both polarizations in comparison with non-sediment-laden ice. Results from this research provide evidence for a relationship between sea ice surface sediment and passive microwave signature, meriting future research in this field

Observations of Thin First Year Sea Ice Using a Suite of Surface Radar, LiDAR, and Drone Sensors

Arctic sea ice is rapidly transitioning into a perennial first year ice pack and this is being observed with satellite remote sensing. Satellite image interpretation requires accurate knowledge of the physical conditions and how they give rise to the microwave scattering response that is present within a single image pixel. This study addresses this issue through a focused remote sensing study of thin first year sea ice. We present results from an experiment that fused datasets from surface-based C- and Ku-band polarimetric scatterometers, LiDAR, and drone-based optical and thermal infrared sensors. We grew frost-flower-covered thin first year sea ice in a mesocosm facility and measured the time-series evolution of C- and Ku-band scattering response as it evolved into snow-covered sea ice. Drone surveys, LiDAR scans, and physical sampling provided complementary characterization of the ice. Results quantify the sensitivity of C- and Ku-band to the presence of frost flowers, the addition of snow, and the meteorological conditions. Drone surveys enhanced the characterization by rapidly performing observations over a larger representative region. In essence, they are helping to close the gap between surface-based sensing and satellite imagery. Furthermore, this study complements and enhances our understanding of the snow-covered sea ice system