Mapping and analyzing ice shelf damage using multisensor imagery and machine learning

The floating ice shelves surrounding Antarctica play a crucial role in regulating ice sheet mass loss by providing mechanical buttressing, which regulates ice discharge into the ocean. The recent collapses of Larsen-A and B along with the rapid retreat of Thwaites were followed by accelerated ice discharge, underlining the importance of understanding the full dynamic of this process. Enhanced damage through fracturing has recently been shown to play a critical role in ice shelf weakening, reducing its buttressing capacity and potentially accelerating their collapse. Despite its significance, the processes governing ice shelf damage remain poorly understood. Damage manifests itself as large crevasses, rifts, and shearing regions clearly visible on satellite imagery. Historically, the mapping of fractures has been challenging due to the labor-intensive nature of manual delineation. Rapid advancements in machine learning, however, have revolutionized damage mapping, enabling the automatic detection of damage features. Although SAR backscatter imagery from ESA's Sentinel-1 has been the primary source of data in recent studies, it suffers from limited temporal coverage (2013-present), which does not capture the entire damage dynamic of ice shelves that destabilized in the early 2000s. Other available products, also  exhibited significant discrepancies with modeled changes in ice viscosity, suggesting that critical features of ice damage are not fully captured (e.g. basal fracturing). To address these gaps, this study presents a novel methodology leveraging multisensor optical imagery and supervised/semi-supervised machine learning algorithms to identify damage features. A U-Net algorithm was trained on manually annotated images from 10 acquisitions from the USGS/NASA's Landsat satellite, across diverse Antarctic ice shelves. These annotations represented various types of damage to ensure broad applicability. The model was then refined using a human-in-the-loop approach with additional Landsat and Sentinel imagery datasets, enhancing prediction accuracy. We demonstrate the capability of our model to map comprehensively the evolution of damage in the Amundsen Sea Embayment, one of Antarctica's most vulnerable regions, from the 1990s to the present.  The results are compared with existing damage products derived from machine learning and radon transform methods using Sentinel-1 SAR images, on the period 2013 to present. We map the dynamic evolution of surface and basal fractures, along with their morphological characteristics such as maximum length and area, and compare this evolution with dynamical changes over the same time period. We complement our analysis by comparing our result to damage modeling using an ice flow model on the Pine Island ice shelf. We use the Shallow Shelf Approximation within the Elmer/ice model to invert for damage and ice viscosity evolution since 1992, by assimilating a long record of satellite-derived surface flow velocity and thickness. We finally analyze the spatial correlations between modeled and observed damaged and draw conclusions on the features of importance regarding ice sheet stability through time. We demonstrate the potential of multisensor optical imagery, which offers broader temporal coverage dating back to the 1970s, to address critical gaps in understanding ice shelf damage and its evolution.