Pathfinder: Efficient Snow Depth, Stratigraphy, and Uncertainty Determination From Radar Measurements

Abstract

Snow strongly influences the climate system through its albedo and insulating properties, while also representing a critical freshwater resource. Yet, its spatial and temporal variability remain poorly constrained due to limitations of in situ and satellite observations. Unoccupied aerial vehicle (UAV)-mounted surface-penetrating radars provide a solution for high-resolution snow surveys, but their data are often difficult to interpret because of variable flight conditions, which, combined with the complicated microwave interactions with snow, result in complex noise and signal patterns. We present Pathfinder, an open-source algorithm for automatic detection of snow interfaces in radar echograms. The method formulates interface tracking as a path-finding problem, combining cost maps derived from reflection strength, ridge detection, and horizontal continuity, and solves it using an efficient dynamic-programming scheme. Pathfinder retrieves the air–snow and snow–ground (or snow–ice) interfaces, and can additionally identify internal layers when present. Validation against coincident in situ, probe-derived snow depth measurements shows an accuracy of snow depth retrievals with R$^{2}$ = 0.96 and RMSE = 8 cm. The algorithm is computationally efficient, enabling real-time application during UAV surveys. Pathfinder was developed for the ultrawide-band snow sensor (UWiBaSS) from NORCE Research but we show it to be transferable across different UAV-mounted and ground-based radar systems. Case studies from Svalbard, the Alps, and polar sea ice demonstrate its robustness across diverse, but mostly dry snow conditions. Pathfinder advances UAV-borne radar as a practical tool for snow depth and stratigraphy mapping by providing an efficient, and operational interface detection method. Consequently, it can support both scientific research and in-the-field decision-making.