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Supraglacial lakes form on the Greenland ice sheet in the melt season (May to October) when meltwater collects in surface depressions on the ice. Supraglacial lakes can act as a control on ice dynamics since, given a large enough volume of water and a favourable stress regime, hydrofracture of the lake can occur, which enables water transfer from the ice surface to the bedrock, where it can lubricate the base. The depth (and thus volume) of these lakes is typically estimated by applying a radiative transfer equation (RTE) to optical satellite imagery. This method can be used at scale across entire ice sheets but is poorly validated due to a paucity of in situ depth data. Here we intercompare supraglacial lake depth detection by means of ArcticDEM digital elevation models, ICESat-2 photon refraction, and the RTE applied to Sentinel-2 images across five lakes in southwest Greenland. We found good agreement between the ArcticDEM and ICESat-2 approaches (Pearson's r=0.98) but found that the RTE overestimates lake depth by up to 153 % using the green band (543–578 nm) and underestimates lake depth by up to 63 % using the red band (650–680 nm). Parametric uncertainty in the RTE estimates is substantial and is dominated by uncertainty in estimates of reflectance at the lakebed, which are derived empirically. Uncertainty in lake depth estimates translates into a poor understanding of total lake volume, which could mean that hydrofracture likelihood is poorly constrained, in turn affecting ice velocity predictions. Further laboratory studies to constrain spectral radiance loss in the water column and investigation of the potential effects of cryoconite on lakebed reflectance could improve the RTE in its current format. However, we also suggest that future work should explore multi-sensor approaches to deriving lake depth from optical satellite imagery, which may improve depth estimates and will certainly result in better-constrained uncertainties. [ABSTRACT FROM AUTHOR]

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations. [ABSTRACT FROM AUTHOR]