Methodology:

Multispectral imagery were acquired using a MicaSense RedEdge camera mounted on a Steadidrone Mavik-M quadcopter flown at a height of 30 m above the ice surface with 60% overlap and 40% sidelap. Radiometric calbiration and geometric distortion correction applied in post-processing. Data converted from radiance to reflectance using calibrated reflectance panels. Images mosaiced using AgiSoft PhotoScan at 5 cm final ground resolution.

The orthomosaics were used in three ways: (i) converted to albedo using a narrowband-to-broadband approximation (Knap et al 1999, Int. J. Remote Sens.), (ii) classified into surface types, and (iii) digital elevation models derived photogrametrically in Agisoft PhotoScan at 5 cm ground resolution.

To classify images by surface type we used a supervised classification approach following Cook et al. (2020, The Cryosphere), trained on ground spectra collected at S6 with a FieldSpec Pro 3 (Analytical Spectral Devices, Boulder, USA) during the 2016 and 2017 field seasons at S6. Briefly, we used 171 directional reflectance measurements. The measurements were labelled by visual examination as snow ('SN'), water ('WA'), clean ice ('CI'), light algae ('LA'), heavy algae ('HA') and dispersed cryoconite ('CC'). After ground spectra were acquired we took destructive ground samples (see Tedstone et al 2020 TC for more details). We split the field dataset randomly into training (70%) and test (30%) sets. These data were used to train a Random Forest classifier. We trained the algorithm to predict surface type, utilising all 5 bands of data.

Narrowband-to-broadband approximations for albedo calculations were employed because empirical Bi-directional Reflectance Distribution Functions (BRDFs) are not available for the surface types that we mapped.

We used the photogrammetric DEMs to derive (i) study area slope angle and (ii) local topographic variability. To calculate the slope angle we applied a gaussian filter with a window of 0.25 m to remove very-high-frequency topographic features, then we calculated the average slope across each study area. To examine local topographic variability ('roughness') we applied a gaussian filter with a window of 4.95 m, then subtracted it from the DEM to yield a detrended surface.

Data collection:

Instrumentation:

MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter.

Data quality:

Good.