Optimizing SAR for Glacier Mapping: A Resolution-Weighted Coherence and Backscatter Dataset of the European Alps

Accurate and up-to-date glacier outlines are essential for quantifying glacier retreat and enabling downstream applications, such as mass balance assessments and glacier modeling. While manual delineation of glaciers remains common, automated approaches offer faster and more consistent mapping. However, even sophisticated deep learning methods show limited performance on debris-covered glaciers, which are difficult to differentiate from periglacial terrain. Optimizing input features is a key strategy to overcome this barrier. Synthetic Aperture Radar (SAR) interferometric coherence complements optical data effectively, as moving ice surfaces typically exhibit lower coherence than stable terrain.

We present a new dataset covering glaciated areas of the European Alps from 2015 to 2025, consisting of annual composites of Sentinel-1 coherence and backscatter. We have developed an automated pipeline to process multiple TB of Sentinel-1 Single Look Complex (SLC) data. For each year, bursts acquired during minimal snow cover were selected and combined to ensure optimal visibility of glacier ice. Backscatter was radiometrically terrain-flattened, and both coherence and backscatter were geocoded to the UTM coordinate system. To mitigate the high spatial resolution variability inherent to SAR imaging in mountainous terrain, we applied the local resolution-weighting approach (Small, 2012). This method combines ascending and descending acquisitions, weighting contributions by the inverse of the local contributing area to preserve the highest resolution available.

The resulting dataset provides analysis-ready annual composites at 10 m grid spacing and will be made publicly available. This enables the development and benchmarking of more robust glacier mapping methods across the European Alps, particularly in challenging debris-covered areas.