Abstract
Glacier melt is an important fresh water source. Seasonal changes can have impacting consequences on downstream water resources management. Today’s glacier monitoring lacks an observation-based tool for regional, sub-seasonal observation of glacier surface mass balance and a quantification of the associated meltwater release at high temporal resolution. The snowline on a glacier marks the transition between the ice and snow surface, and is, at the end of the summer, a proxy for the annual glacier mass balance. Using transient snowlines for model calibration to derive annual mass balance time series for glaciers on regional scale has shown great potential to better grasp the glacier response to climate change for remote regions. Thereby, it was shown that model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date, but glacier-specific snowline observation remained spare. Recent advances in remote sensing permit efficient and extensive snowline mapping. Different methods automatically discriminate snow over ice on high- to medium-resolution optical satellite images (Sentinel 2, Landsat). Other studies rely on lower ground resolution optical imagery (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS)) to retrieve snow cover fraction at pixel level and produce regional maps of snow cover extent. However, state-of-the-art methods using optical sensors still have important shortcomings, such as cloud-cover related issues. Images acquired by Synthetic Aperture Radar (SAR), which are almost insensitive to cloud coverage, have proofed suitable for transient snowline delineation. The combination of SAR and optical data in a complementary way carries a unique potential for a better monitoring of snow depletion on high temporal and spatial resolution. The aim of this work is to map snow cover over glaciers by combining Sentinel-1 SAR, Sentinel-2 multispectral and lower resolution MODIS images. We applied a change detection algorithm for generating maps of wet snow from Sentinel-1 images. A Support Vector Machine (SVM) classification algorithm was applied to the Sentinel-2 images by employing all the spectral bands with a spatial resolution equal to 10 or 20 meters as input features. From both the Sentinel-1 and Sentinel-2 snow maps, we calculated the fraction of snow cover in relation to the total glacier area. This fraction was then related to mean NDSI from MODIS for common observation dates. With a linear regression between the different products, we estimated a function to reproduce close-todaily snow cover area per glacier from the mean NDSI of MODIS images covering the period 2000 to present. Consecutively, we developed an approach that can automatically handle classification of multi-source and multi-resolution satellite image stacks. This provides a unique solution for continuous snowline mapping since the beginning of the century when sensor availability and quality was still limited. With the provided close-to-daily transient snow cover fractions for an individual glacier, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.