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7 October 2019 Integration of hydro-climatological model and remote sensing for glacier mass balance estimation
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Abstract
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Iwona Podsiadlo, Claudia Paris, Francesca Bovolo, Mattia Callegari, Ludovica De Gregorio, Daniel Günther, Carlo Marin, Thomas Marke, Milad Niroumand-Jadidi, Claudia Notarnicola, Ulrich Strasser, Marc Zebisch, and Lorenzo Bruzzone "Integration of hydro-climatological model and remote sensing for glacier mass balance estimation", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115513 (7 October 2019); https://doi.org/10.1117/12.2533232
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