The Antarctic Peninsula Ice Sheet (APIS) has become a significant contributor to rising sea levels, and accurately estimating ice discharge from its outlet glaciers is essential for assessing the mass balance of the region. This study calculates ice discharge from APIS outlet glaciers north of 70°S using five commonly used ice-thickness reconstructions, employing a consistent surface velocity field and flux gates. Results indicate a total volumetric ice discharge ranging from 45 to 141 km3 per year for 2015–2017, with a mean of 87 ± 44 km3 per year. The substantial differences in results highlight the large uncertainty in current ice-discharge estimates, emphasizing the challenge of accurately modeling the ice-thickness distribution in this complex and data-scarce region.
Existing mass budget estimates for the northern Antarctic Peninsula (>70° S) are affected by considerable limitations. We carried out the first region-wide analysis of geodetic mass balances throughout this region (coverage of 96.4 %) for the period 2013–2017 based on repeat pass bi-static TanDEM-X acquisitions. A total mass budget of −24.1±2.8 Gt/a is revealed. Imbalanced high ice discharge, particularly at former ice shelf tributaries, is the main driver of overall ice loss.
The study emphasizes the importance of understanding marine-terminating glacier dynamics in glacier projections. Deep learning methods can automate the extraction of calving front positions from satellite imagery, reducing manual effort. The “CaFFe” dataset, which includes annotated calving fronts in Synthetic Aperture Radar (SAR) imagery, offers a standardized benchmark for evaluating deep learning techniques in this area. Researchers can use CaFFe to assess the performance of upcoming deep learning models and identify promising research directions. A leaderboard of models can be found at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.
Advancements in Deep Learning have enabled the automated identification of glacier calving fronts in satellite imagery. This study improves the accuracy of this process by incorporating a Conditional Random Field (CRF) into the post-processing of the neural network’s predictions. Experiments using the CaFFe dataset showed a 27-meter improvement in mean distance error. The code is available at https://github.com/EntChanelt/GlacierCRF.
Research on surge-type glaciers, although they constitute a small percentage of all glaciers, significantly contributes to understanding glacier flow mechanisms. Recent studies have utilized remote sensing techniques to unravel these processes, highlighting the potential of combining high-performance computing and earth observation. While modeling surge events has gained popularity, there is a lack of comprehensive spatial and temporal data on surge timing. To address this, an algorithm has been developed that not only detects surge-type glaciers but also determines the onset of surges. The algorithm relies on time series analysis of glacier surface velocity using Sentinel-1 data, involving seasonal and trend decomposition and outlier detection via the General Studentized Extreme Deviate Test. Cluster analysis is then applied to identify outlier clusters associated with glacier surges. The method’s effectiveness was demonstrated in the Svalbard archipelago between 2015 and 2021, where 18 glacier surges and their timing were successfully identified.
The concept of the sequential sampling is well known and still widely used in industrial automation radar applications due to its hardware simplicity, low cost and low power consumption. However, there is only a limited amount of publications that describe this concept and its variants in detail. This tutorial introduces the typical sequential sampling impulse radar concept step by step and presents several key characteristics, such as correlation properties and SNR considerations. In addition to the system theory, selected applications are presented to illustrate the attractiveness and elegance, but also the limits of this radar concept. The shown applications range from those in industrial automation to radar concepts in the areas of automotive radar, security scanners, biomedical radar systems and ground penetrating radar. The latter application is among others including the analysis of glaciers, e.g. to determine the ice thickness and stratification in order to provide a better understanding of the glacier and its behavior in a changing climate.
The corresponding paper can be found here:
A new benchmark database for the automatic mapping of glacier fronts was recently publish in Copernicus Earth System Science Data.
This work represents the first comprehensive database for training and testing machine learning algorithms for glacier calving front detection.
Autor: Nora Gourmelon