Spatiotemporal Variations of Glacier Surface Facies (GSFs) in Svalbard: An Example of Midtre Lovénbreen

Abstract

Glacier surface facies (GSFs) are visible glaciological regions that can be distinguished and mapped at the end of summer using optical satellite data. GSF maps act as visual metrics of glacier health when assessed independently or correlated with in situ mass balance measurements. The literature suggests that the spatiotemporal distribution of all accumulation and ablation facies are important inputs to 3D mass balance models because the GSF trends enhance the precision of models. For example, the progressive increase in the area and distribution of melting ice and decrease in the area and distribution of glacier ice, as estimated by satellite data, may signal potential mass loss without significant change in the overall area of the ablation zone. Tracking the evolution of GSFs in Svalbard is important for the predictive assessment of the cryosphere in the Arctic. This will further facilitate robust methods for monitoring GSFs on a planetary scale. In this context, we present a regional spatiotemporal analysis of GSFs of Midtre Lovénbreen, Ny Ålesund, Svalbard. We used openly available Landsat 8 Operational Land imager (OLI) and Sentinel 2A imagery taken in 2017–2022 to track the occurrence and variations of GSFs via machine learning. The current results suggest that ablation facies such as melting ice and dirty ice are increasing over time. Sentinel 2A provides finer resolution but is limited by its temporal coverage. Although Landsat is suitable for long-term trend analysis, its coarser resolution can lead to errors such as over/underestimation of smaller patches of facies on relatively smaller glaciers. As the spectral properties of GSFs are consistent over time, a robust set of spectra depicting variations in physical appearance of facies may be used to train machine learning algorithms, thereby improving efficacy. In forthcoming studies, our objective is to expand the temporal scope spanning decades and to trace facies evolution over longer time series.

Keywords

• glacier surface facies
• machine learning
• Landsat
• Sentinel
• Arctic