What question did this study set out to answer?

This study aims to analyze the late-summer snowline altitude of glaciers in the Canadian Arctic Archipelago over a decade.

March 13, 2026Open Access

Expanding Temporal Glacier Observations Through Machine Learning and Multispectral Imagery Datasets in the Canadian Arctic Archipelago: A Decadal Snowline Analysis (2013–2024)

Key Points

This study aims to analyze the late-summer snowline altitude of glaciers in the Canadian Arctic Archipelago over a decade.
Utilized 9920 satellite images from Landsat 8/9 and Sentinel-2 for snowline mapping.
Applied machine learning to categorize glacier surface types into snow and bare ice.
Employed the elevation-binning method and Snow-Elevation Histogram Analysis (SEHA) to extract snowline altitude.
Analyzed positive degree days (PDD) data from multiple weather stations to characterize melt-season conditions.
Validated satellite-derived snowline altitude against equilibrium-line altitude observations.
All analyzed glaciers demonstrated a seasonal snow cover area cycle, with extremes noted in 2020.
Snowline altitude correlates positively with summer temperatures, with specific sensitivity rates identified for individual glaciers.
An advanced elevation-bin method provided more accurate snowline altitude estimates than traditional methods, with lower bias and RMSE.
Meteorological data shows significant warming trends, indicating heightened melt and loss of accumulation zones in warmer years.

Abstract

Glaciers in the Canadian Arctic Archipelago (CAA) contribute significantly to sea-level rise, yet sparse in situ data limit regional climate assessments. This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six CAA glaciers, utilising 9920 Landsat 8/9 and Sentinel-2 scenes. Glacier surface cover types (snow and bare ice) were mapped via machine learning, and SLA was extracted using elevation-binning and Snow-Elevation Histogram Analysis (SEHA). Elevation data were obtained from ArcticDEM v3; positive degree days (PDD) from Eureka, Pond Inlet, and Pangnirtung were used to characterize melt-season forcing. Satellite-derived SLA was validated against equilibrium-line altitude (ELA) observations from White Glacier. All glaciers exhibit a characteristic seasonal SCA cycle: maximum extent in June, minimum in August, and partial recovery in September, with extreme anomalies in 2020. Annual peak SLA correlates positively with summer warmth; sensitivities to PDD were 2.56, 0.67, and 0.83 m (°C d)−1 for White, Highway, and Turner glaciers, respectively. Hypsometry strongly modulates climatic sensitivity: glaciers with limited high-elevation area (e.g., BylotD20s, Turner) frequently lose their accumulation zones in warm years. At White Glacier, SLA replicates interannual ELA variability with high correlation and lower error using the elevation-bin method (mean bias +53 m; RMSE 177 m) compared with SEHA (+165 m; 339 m). Meteorological records indicate significant summer and winter warming at Eureka, with increasing PDD; precipitation trends are spatially variable. A regionally calibrated, quality-assured elevation-bin method produces objective and transferable SLA time series, suitable for ELA estimation in data-sparse Arctic settings. The SLA–PDD relationship and hypsometry-dependent responses highlight increasing stress on accumulation zones under continued warming. Reporting SLA uncertainty and image quality, alongside expanded field observations, will enhance Arctic-wide glacier monitoring.

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