Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking.

Dynamic climate changes are especially noticeable in polar regions, where glaciers are retreating landward at an increased rate. This study emphasizes the application of the feature tracking method for analyzing the ice flow velocity across the entire Svalbard archipelago from 2015 to 2024. We conducted our analysis using the Geogrid and autoRIFT platforms, complemented by Sentinel-1 imagery. To validate our ice velocity products, we employed the Glacier Feature Tracking (GLAFT) aproach. Our analysis culminated in the development of an extensive glacier repository for the archipelago, which includes velocity data spanning the study period. These results facilitated the identification of glaciers demonstrating surging behavior. Additionally, the study introduces a novel approach to analyzing glacier flow velocities, focusing on their acceleration characteristics.

Determining the grounding lines of ice shelf glaciers (the border at which the ice begins to float in the ocean) is obligatory in precise measuring and understanding of ice sheet mass balance and glacier dynamics. Awareness of its migration range (grounding zone) is also crucial when estimating the impact of glacier/ice sheet waters on the ocean water level. Currently, the most precise large-scale method is based on the viscoelastic tidal movement of the ice shelf identified on a 4-pass DInSAR results. In some places, however, the measurements are impossible or significantly difficult due to the decorrelation between scenes. According to our preliminary results, it may be possible to use unwrapped phase interferograms as a new/supportive method for detecting ground lines. Combined with algorithms for automatic delineation, it can become a powerful solution for obtaining results with unprecedented frequency.The latest results revealed that for many glaciers the grounding zone width is two orders of magnitude larger than expected. This contradicts existing physical models, which are based on zero ice melt and fixed grounding line position. Irregular interactions between ice and seawater might have a strong impact on glacier evolution and projections if implemented in physical models. We employed a long-time series of Sentinel-1 differential radar interferometry from 2017 to 2021 to detect the variability in grounding line position on Orville Coast, the region of the western Ronne Ice Shelf. The research carried out over a long period and with high frequency allowed a more detailed study of changes occurring in the grounding zone. Observation from a broader perspective gave us the opportunity to detect seasonality and a persistent trend. We compared changes in grounding line migration with external factors e.g. ocean tides. This might provide a better understanding of the behavior of the ice sheet and glaciers, which are currently undergoing such rapid changes.

The paper presents the potential of combining satellite radar data and neural networks for quasi-automatic detection of glacier grounding lines. The conducted research covered five years and was carried out in the area of the Amery Ice Shelf. It has a very complex shoreline, so its grounding-line location is uncertain. Thus, it has always been the subject of much research. The main objective of our work was to find out if Synthetic Aperture Radar data combined with a deep learning implementation would enable rapid detection of ice shelf grounding lines over large areas. For this purpose, 290 radar images from the Sentinel-1 satellite covering 46 000 km2 were used. Processed by the Differential Interferometry of Synthetic Aperture Radar four-pass method, the images formed a time-consistent series between 2017 and 2021. As a result of performed calculations, a total length of 1280 km of grounding line was determined. They were validated by comparing with other independent data sources based on manual measurements. It has been demonstrated that the combination of satellite radar data and automated data processing allows for obtaining high-precision results continuously in a very short time. Such an approach allows monitoring of grounding line position in the long term with intervals of less than one week. It enables analysis of the dynamics changes with unprecedented frequency and the identification of patterns.

This paper analyzes seasonal grounding line migration from a 4.5-year perspective and with a high (6 days) data-sampling rate. We used a series of high-resolution (60 m) Sentinel-1 double-difference interferograms obtained in the years 2017–21 to monitor variability in the grounding line position on the Orville Coast, on the western part of the Ronne Ice Shelf. We confirmed that the integration of the double-difference interferogram method with a neural network specifically trained on this type of data allows a successful detection of the grounding line position. Despite challenges related to maintaining the coherence and reducing data noise, we were able to generate and analyze time series of grounding line positions, test the assumption of maximum migration ranges and attempt pattern recognition in temporal grounding line migration. Our results represent a pioneering approach to seasonality and trend assessment in grounding line behavior. We believe that our findings can help detect the patterns of and the reasons for glacier behavior in the grounding zone. This information may be crucial in monitoring the mass loss of glaciers, especially in light of ongoing significant climate change.

The study of glacier surges, which involve periodic increases in flow velocity even to several orders of magnitude, is crucial for a better understanding of glacier dynamics in Arctic regions. Knowledge of the causes, course, and mechanisms of this phenomenon can help determine the role of glaciers as indicators of global climate change. It is also very important for safety and risk management reasons. A relatively new method of studying glacial surges is the analysis of backscatter changes in SAR images, which indicate deformations and variable properties of the glacier surface during the active phase. However, unambiguous identification of the phenomenon requires analysis of changes in the surface velocity of the glacier.In this study we analyze a time series of Sentinel-1 mission data for Svalbard for the years 2016-2025 to track the long-term dynamics of glaciers. We detect backscatter anomalies that can be associated with surge activity. We then compare the results with surface velocity data obtained by offset tracking on SAR imagery. Based on the results, we conclude that the two methods are complementary. The methodology used can be applied in further studies of glacier surges and expand the knowledge of surge mechanisms that are not yet fully understood.