Processing Pipeline for Computing Time Series of 3D Glacier Surface Flow and Mass Balance

Abstract

To comprehend glacier dynamics for a region, a time-series study of glacier change is essential. Existing research often relies exclusively on glacier mass balance or surface displacements to understand how glaciers adapt to a warming climate. Moreover, the generation of large time series data often requires a substantial amount of computation and time. We address these limitations by building an efficient open-source pipeline for the mass processing of satellite images, generating extensive time series data for tracking glacier changes. This pipeline employs Sentinel-1 (S-1) interferometric wide swath SAR data to produce seasonal time series of glacier surface displacements and annual time series of glacier mass balance over prolonged durations. Our processing chain utilizes the ISCE framework for SAR data processing and autoRIFT software for performing offset tracking. It combines both ascending and descending S-1 images and utilizes offset tracking to compute displacements in both azimuthal and range directions. These estimates are then fine-tuned through the use of OT-SBAS. Our pipeline computes northward, eastward, and vertical flow velocities through weighted least squares, with weights designed to make the model more robust. Furthermore, it employs a machine-learning algorithm for pixel-wise segmentation of glaciers using optical data, facilitating the computation of areal changes in glaciers. The derived vertical and areal changes are then leveraged to compute glacier mass balance. Three valley-type glaciers (Bara Shigri, Chota Shigri, and Samudra Tapu) located in the Chandra basin, Himachal Himalaya, were selected to test out the proposed pipeline. The 3D surface displacement and mass balance time series were retrieved from 2017 to 2022. This software can be employed to monitor glaciers through the analysis of frequent revisit SAR data obtained from satellites like Sentinel-1 and the upcoming NISAR.