Using Deep Learning for Glacier Thickness Estimation at a Regional Scale

Mountain glaciers play a critical role for mountain ecosystems and society with major concerns related to their future evolution and related water resources. Modeling glacier future evolution allows anticipating climate change impacts and informing policy decisions. It relies on accurate ice thickness estimation at regional scales. This letter proposes a deep learning-based approach in a supervised learning framework for ice thickness estimation at a regional scale from surface ice velocity measurements and a digital elevation model (DEM). A neural network model built upon a ResNet architecture is proposed based on the trade-off between the model complexity and the prediction efficiency. Promising results are obtained from data including 1400 glaciers in the Swiss Alps, highlighting the potential of deep learning-based approach for large-scale ice thickness estimation. The incorporation of expert's knowledge into the neural network model further helps refine the model prediction and improve the model relevance. The ice volume difference between the reference issued from ground penetrating radar (GPR) measurements and the predictions by the proposed neural network model varies between 0.5% and 16% of the reference volume. Larger ice volume difference is mainly related to over-deepening of the bedrock resulting from past larger extent of the glacier, which information is not included in the data.