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This paper compares and integrates different strategies to characterize thevariability of end-of-winter snow depth and its relationship to topography inice-wedge polygon tundra of Arctic Alaska. Snow depth was measured usingin situ snow depth probes and estimated using ground-penetrating radar (GPR)surveys and the photogrammetric detection and ranging(phodar) technique with an unmanned aerial system (UAS). We foundthat GPR data provided high-precision estimates of snow depth(RMSE  =  2.9 cm), with a spatial sampling of 10 cm alongtransects. Phodar-based approaches provided snow depth estimates in a lesslaborious manner compared to GPR and probing, while yielding a high precision(RMSE  =  6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depthand its correlation to micro- and macrotopography using the snow-free lidardigital elevation map (DEM) and the wavelet approach. We found that theend-of-winter snow depth was highly variable over short (several meter)distances, and the variability was correlated with microtopography.Microtopographic lows (i.e., troughs and centers of low-centered polygons)were filled in with snow, which resulted in a smooth and even snow surfacefollowing macrotopography. We developed and implemented a Bayesian approachto integrate the snow-free lidar DEM and multiscale measurements (probe andGPR) as well as the topographic correlation for estimating snow depth overthe landscape. Our approach led to high-precision estimates of snow depth(RMSE  =  6.0 cm), at 0.5 m resolution and over the lidar domain(750 m × 700 m)

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods