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

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 insitu and satellite observations. Unoccupied Aerial Vehicle (UAV)mounted surface-penetrating radars provide a solution for highresolution snow surveys, but their data are often difficult to interpret because of variable flight conditions which, combined with the complicated EM 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 (https://github.com/Thorkage/Radar-Pathfinder). 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 snow depth measurements shows an accuracy of 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 Ultra Wide-band Snow Sensor (UWiBaSS) from NORCE Research but we show it to be transferable across different UAV-mounted and groundbased radar systems. Case studies from Svalbard, the Alps, and polar sea ice illustrate its robustness across diverse snow conditions. By providing a reliable, efficient, and operational interface detection method, Pathfinder advances UAV-borne radar as a practical tool for snow depth and stratigraphy mapping, supporting both scientific research and in-the-field decision-making.