Integration of hydro-climatological model and remote sensing for glacier mass balance estimation

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, Lorenzo Bruzzone

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.
Original languageEnglish
Title of host publicationSPIE Remote Sensing 2019
Number of pages9
Publication statusPublished - 7 Oct 2019
Externally publishedYes
EventSPIE Remote Sensing 2019 - Strasbourg, France
Duration: 9 Sept 201912 Sept 2019
Conference number: 25

Publication series

NameProceedings of SPIE - the international society for optical engineering
ISSN (Print)0277-786X


ConferenceSPIE Remote Sensing 2019


  • Biophysical parameter estimation
  • Regression
  • Remote Sensing
  • Glacier Mass Balance
  • Hydrological model
  • n/a OA procedure


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