- 1Antartic Research Centre, Victoria University of Wellington, Wellington, New Zealand (julia.martin@vuw.ac.nz)
- 2WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
- 3Finish Meteorological Institute FMI, Helsinki, Finland
How do snow distribution patterns influence the surface temperature of snow on sea ice? Despite its crucial role in influencing sea-ice energy balance, snow on Antarctic sea ice remains poorly understood.
To address this knowledge gap, we used an Uncrewed Aerial Vehicle (UAV) and ground-based measurements to produce a Digital Elevation Model (DEM) of the snow topography and map the snow surface temperature over relatively uniform landfast sea ice in McMurdo Sound, Ross Sea, Antarctica during our field season in November-December 2022.
A key methodological innovation in this study is an algorithm that corrects thermal drift caused by Non-Uniformity Correction (NUC) events in the DJI Matrice 30T thermal camera. The new algorithm minimizes temperature jumps and distortion in the imagery, ensuring consistent and accurate high-resolution (9 cm/px) snow surface temperature maps.
As expected, largest surface temperature anomalies were associated with sediment deposition on the snow surface, which was identified by low red band values in UAV optical imagery. Additionally, we found that the small-scale topography on a seemingly flat snow field significantly influences the incoming solar radiation (insolation) at the point scale. Using a model that accounts for topographical effects on insolation, we found that assuming uniform insolation over our study area (200x200 m) underestimated insolation variability due to relatively small-scale surface topography. The modeled mean insolation for the overflown study site, which accounts for surface topography, is 592 ± 90 Wm-2(2 Standard Deviations), whereas the mean measured insolation at the point scale is 593 ± 20 Wm-2. This shows that assuming a flat surface fails to represent the full range of insolation and may impact non-linear energy balance processes.
Our results improve our understanding of snow's spatial distribution, how it influences snow surface temperatures and how it may influence the sea-ice energy balance.
How to cite: Martin, J., Dadic, R., Anderson, B., Pirazzini, R., Vargo, L., and Wigmore, O.: How Flat is Flat? Investigating the Spatial Variability of Snow Surface Temperature and Topography on Landfast Sea Ice Using Drone-Based Mapping , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4593, https://doi.org/10.5194/egusphere-egu25-4593, 2025.