Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features

An accurate parameterization of glacier calving is essential for understanding glacier dynamics and constraining ice-sheet models. The increasing availability and quality of remote sensing imagery open the prospect of a continuous and precise mapping of relevant parameters, such as calving front locations. However, it also calls for automated and scalable analysis strategies. Deep neural networks provide powerful tools for processing large quantities of remote sensing data. In this contribution, we assess the benefit of diverse input data for calving front extraction. In particular, we focus on Landsat-8 imagery supplementing single-band inputs with multispectral data, topography, and textural information. We assess the benefit of these three datasets using a dropped-variable approach. The associated reference dataset comprises 728 manually delineated calving front positions of 23 Greenland and two Antarctic outlet glaciers from 2013 to 2021. Resulting feature importance emphasizes both the potential integrating additional input information as well as the significance of their thoughtful selection. We advocate utilizing multispectral features as their integration leads generally to more accurate predictions compared with conventional single-band inputs. This is especially prevalent for challenging ice melange and illumination conditions. In contrast, the application of both textural and topographic inputs cannot be recommended without reservation, since they may lead to model overfitting. The results of this assessment are not only relevant for advancing automated calving front extraction but also for a wider range of glaciology-related land surface classification tasks using deep neural networks.