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 mass balance and a quantification of 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. It was shown that glacier mass balance model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date. 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. Other studies rely on lower ground resolution optical imagery 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.
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. With the provided close-to-daily transient snow cover fractions on glacier level, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.
The accurate monitoring and understanding of glacier dynamics are of high relevance for climate science and water-resources management. The glacier parameters are typically estimated by data assimilation methods which inject field measurements into the numerical simulations with the aim of improving the physical model estimates. However, these methods often are not able to capture and model the complexity of the estimation problem. To solve this problem, this paper proposes a method that integrates remote sensing (RS) data, in-situ observations and a physical-based model to accurately estimate the Glacier Mass Balance (GMB). The RS data are used to represent the physical properties of the glaciers by characterizing their topography and spectral properties. Instead of assimilating the observations into the model, the in-situ measurements are used to perform a data-driven correction of the GMB estimates derived from the physically-based simulations in the informative RS feature space. The method is applied to the Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) hydro-climatological model. In the experimental analysis, the multispectral images used to define the feature space are high-resolution Sentinel-2 images. The method is validated on three glaciers in Tyrol (Hintereis, Kasselwand and Varnagt glaciers), in 2015 and 2016. The obtained results show the effectiveness of the method in improving the GMB estimates.
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