TICOI: an operational Python package to generate regular glacier velocity time series

Abstract

Glacier velocity is a crucial observation as it controls the mass redistribution and future evolution of the geometry of a glacier. While glacier annual velocities are now available in open data worldwide, sub-annual velocity time series are still highly uncertain and available at heterogeneous temporal resolutions. This hinders our ability to understand flow processes such as basal sliding and surges, as well as the integration of these observations into numerical models. The latest could help to better constrain future projections of sea level rise. We introduce an open source and operational Python package called TICOI (Temporal Inversion using linear Combinations of Observations, and Interpolation). TICOI fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains, using the temporal closure principle. In this article, we provide extensive examples of TICOI applications on the ITS_LIVE dataset and in-house velocity products, to generate monthly velocity time series. The results are evaluated against GNSS data collected on three glaciers with different dynamics in Yukon and western Greenland, including a surging glacier. Comparison with GNSS observations demonstrates a reduction in error by up to 50 % in comparison with the raw image-pair velocities and other post-processing methods. This increase in performance comes from the development of methodological strategies to enhance TICOI’s robustness to temporal decorrelation and abrupt non-linear changes. In addition, TICOI can retrieve monthly velocity using annual image-pair velocities only, when there is sufficient temporal redundancy. This package opens the door to the harmonization of various datasets, enabling the creation of standardized sub-annual velocity products.