Integrating Models and Remote Sensing Data for Distributed Glacier Mass Balance Estimation

• Main Authors: Iwona Podsiadlo, Claudia Paris, Mattia Callegari, Carlo Marin, Daniel Gunther, Ulrich Strasser, Claudia Notarnicola, Lorenzo Bruzzone
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Data integration; glacier mass balance (GMB); hydroclimatological model; machine learning; parameter estimation; remote sensing (RS)
• Online Access: https://ieeexplore.ieee.org/document/9212596/

This article presents an approach to improve the estimation of the glacier mass balance (GMB) of six selected alpine glaciers in the European Alps. This is achieved by combining three complementary data sources: hydroclimatological model, remote sensing (RS) data, and ground measurements. The hydroclimatological model provides spatially distributed mass balances. RS supplies spatially distributed surface characteristics. The ground point measurements provide the mass balance at the local scale. The combination of these data sources allows us to improve the spatial resolution of the model output and its GMB estimates. We used the alpine multiscale numerical distributed simulation engine model (AMUNDSEN), which considers the processes of accumulation and ablation of snow and ice for the area of the entire glacier (with a given spatial and temporal resolution). In the proposed integration approach, we first compute the deviations between the GMB simulation (afforded by the hydroclimatological model) and the ground measurements. Then, the RS data are used to define a feature space (which objectively characterizes the glacier surface properties). The method estimates the adjustment required to the model, for each unlabeled sample, leveraging on its neighboring labeled samples in the feature space. This allows us to apply similar adjustment to samples sharing similar glacier surface conditions. Experimental results show that the proposed integration approach achieves an average root-mean-square error of 460 mm (compared to 732 and 661 mm obtained by the hydroclimatological model and the standard regression models, typically used for parameters estimation).