A Deep Unsupervised Learning Approach for Monitoring Snow Facies Over Ice Sheets Using TanDEM-X Bistatic Data

Diagenetic snow facies represent distinct zones of snow and ice characterized by unique snow physical properties and attributes. These facies serve as indicators of changes in the surface mass balance of ice sheets, making them particularly relevant for monitoring the response of snow and firn cover to climatic changes. In this study, we propose a novel, fully unsupervised deep learning method based on convolutional neural networks (CNNs) to monitor snow zones on ice sheets using a decade of single-pass, bistatic interferometric synthetic aperture radar (InSAR) TanDEM-X data over Greenland. To do so, we develop an innovative iterative training approach to effectively manage a large variety of InSAR acquisition geometries with the goal of optimizing a robust, geometry-invariant model. The proposed approach achieves an average classification accuracy of 93.4% across varying acquisition geometries, segmenting the Greenland ice sheet into five distinct partitions. By analyzing these partitions in terms of elevation, snow density, and cumulative melt data, we link them to classical diagenetic snow facies and uncover trends correlated with climate events over the past decade, estimating a loss of over 454000 km2 in dry snow extent on the Greenland ice sheet due to the 2012 extreme melt event, with a continuing trend of expanding percolation facies. The proposed method is adaptable to current and future single-pass synthetic aperture radar (SAR) missions, such as the ESA 10th Earth Explorer Harmony mission, enhancing data robustness against varying acquisition geometries and demonstrating the potential of spaceborne single-pass InSAR missions for long-term monitoring of ice sheet dynamics.