Reconstructing Glacier Dynamics in Complex Terrain with ICESat-2 and Gaussian Process Interpolation

Abstract. We apply Gaussian Process Regression (GPR) to ICESat-2 along-track height change data to generate spatially continuous glacier height change fields across two glaciated regions with complex topography and dynamic behaviour: Larsen-B (Antarctic Peninsula) and central Southern Svalbard. For Larsen-B, GPR-derived height change rates from 2021–2024 average −0.61 ± 0.02 m a–1, corresponding to a volume loss of 4.55 ± 0.17 km³ a–1, similar to independent estimates from TanDEM-X. In Svalbard, we observe widespread thinning (−1.57 ± 0.03 m a–1) and detect clear signals of surging glaciers. GPR's multi-dimensional, uncertainty-aware framework enables accurate interpolation across data gaps and supports the detection of localized dynamic events, such as surges. Sensitivity tests show that interpolation errors increase with gap size, slope, and extrapolation distance. Our findings demonstrate that GPR is well-suited for enhancing the spatial and temporal resolution of altimetry-based glacier monitoring, enabling improved estimates of height and volume change while reconstructing spatially localized phenomena such as surge activity and other transient glacier dynamics.