U-DEM: High spatiotemporal resolution surface elevations of the Canadian Arctic

Abstract. High-resolution glacier elevation observations are essential for resolving rapid changes in glacier geometry, but existing satellite datasets face a trade-off between spatial and temporal resolution. Here, we present U-DEM, a machine-learned glacier elevation time series that combines complementary satellite datasets to provide surface elevations at 100 m spatial and 3-month temporal resolution. We apply the approach to glaciers and ice caps of the Canadian Arctic from 2014 to 2024. High-resolution glacier elevation patterns are learned from ArcticDEM strips and reconstructed through time using CryoSat-2 elevations and Sentinel-1 backscatter as temporally repeated inputs. Validation of U-DEM against independent ICESat-2 laser altimetry observations yields a root-mean-square error (RMSE) of 10.01 m, approximately 65 % lower than the underlying CryoSat-2 elevations, with improvements across all 15 study regions, including regions not represented during training. Comparing U-DEM with kinematic Global Navigation Satellite System (GNSS) measurements on the narrow White Glacier yields RMSE values of 1.70–3.35 m, comparable to those of the ArcticDEM strips. Application examples further demonstrate the potential of U-DEM for resolving glacier elevation changes across different spatial scales. Firstly, U-DEM resolves the migration of the dynamic balance line during the ongoing surge of Chapman Glacier. Secondly, U-DEM captures localized elevation variability associated with several active subglacial lakes that is poorly resolved by CryoSat-2. U-DEM thus demonstrates how machine learning can integrate satellite observations with complementary sampling characteristics to bridge the spatial-temporal resolution gap of individual sensors. The approach provides a framework for generating spatially detailed and temporally continuous records of glacier surface evolution from existing and future Earth observation missions.