Red snow algae bloom at the surface of snowfields worldwide, and their detection is relevant for ecological, biogeochemical and mass balance studies. In this study, we co-located RGB imagery acquired with a light-weight Uncrewed Aerial Vehicle (UAV) to 129 hyperspectral reflectance spectra from which the snow surface properties were retrieved, thereby enabling high-resolution aerial mapping of algal properties. We present maps of red snow algae abundance and albedo reducing effect over ∼ 9700 m2 of seasonal snowfields across Hardangervidda, Southern Norway, in July and August 2023. The average albedo reducing effect of the algae over the entire area was 0.012 ± 0.005, and attained 0.028 ± 0.004 on a snowfield of ∼ 710 m2. Across snow surfaces with visible blooms only, the algal albedo reducing effect was 0.045 ± 0.003, equivalent to an additional ∼ 3 mm of daily melting under local illumination conditions, and aggregating to 5,500 ± 2,300 kg of daily snowmelt. The intensity and spatial coverage of surface algal blooms were very variable between and within the individual snowfields. Analysis of the UAV imagery suggests that multiple small and distributed samples are at least twice more likely to yield representative estimates of the average snow algal concentration of a snowfield compared to fewer, larger samples. Our study demonstrates the potential of low-cost and easy to deploy UAVs for red snow algal monitoring at the cm to sub-cm scale, which can be used to better understand their spatial ecology and role in albedo reduction.
