Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we, therefore, devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a data set of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at https://github.com/khdlr/HED-UNet .

Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.

The contribution of Antarctica’s ice sheet to global sea-level rise depends on the very dynamic behavior of glaciers and ice shelves. One important parameter of ice-sheet dynamics is the location of glacier and ice-shelf fronts. Numerous remote sensing studies on Antarctic glacier and ice-shelf front positions exist, but no long-term record on circum-Antarctic front dynamics has been established so far. The article outlines the potential of remote sensing to map, extract, and measure calving front dynamics. Furthermore, this review provides an overview of the spatial and temporal availability of Antarctic calving front observations for the first time. Single measurements are compiled to a circum-Antarctic record of glacier and ice shelf retreat/advance. We find sufficient frontal records for the Antarctic Peninsula and Victoria Land, whereas on the West Antarctic Ice Sheet (WAIS), measurements only concentrate on specific glaciers and ice sheets. Frontal records for the East Antarctic Ice Sheet exist since the 1970s. Studies agree on the general retreat of calving fronts along the Antarctic Peninsula. East Antarctic calving fronts also showed retreating tendencies between 1970s until the early 1990s, but have advanced since the 2000s. Exceptions of this general trend are Victoria Land, Wilkes Land, and the northernmost Dronning Maud Land. For the WAIS, no clear trend in long-term front fluctuations could be identified, as observations of different studies vary in space and time, and fronts highly fluctuate. For further calving front analysis, regular mapping intervals as well as glacier morphology should be included. We propose to exploit current and future developments in Earth observations to create frequent standardized measurements for circum-Antarctic assessments of glacier and ice-shelf front dynamics in regard to ice-sheet mass balance and climate forcing.

The hydrological drought in Northern Italy in 2022 was, in large part, the consequence of a snow drought in the Italian Alps in the winter of 2021/22 and the resulting deficit of melt water runoff. In this communication, we assessed the snow-cover dynamics in nine Alpine Italian catchments using long time series of satellite-derived snow line elevation (SLE) measurements. We compared the SLE of the hydrological year 2021/22 to the long-term dynamics of 1985–2021. In early 2022, the SLE was located several hundred meters above the expected median values in all of the nine catchments. This resulted in deficits of snow-covered area of up to 83% in the Western Alps (catchment of Sesia, March 2022) and up to 61% in the Eastern Alps (Brenta, March 2022) compared to the long-term median. Although snow-cover data from optical satellite imagery do not contain information about snow depth and water content, in a preliminary qualitative analysis, the derived SLE dynamics show good agreement with the Standardized Snowpack Index (SSPI) which is based on the snow water equivalent (SWE). While the exact relationships between SLE, SWE, and runoff have to be explored further on the catchment basis, long-time series of SLE may have potential for use in drought early warning systems.

The safety band of Antarctica, consisting of floating glacier tongues and ice shelves, buttresses ice discharge of the Antarctic Ice Sheet. Recent disintegration events of ice shelves along with glacier retreat indicate a weakening of this important safety band. Predicting calving front retreat is a real challenge due to complex ice dynamics in a data-scarce environment that are unique for each ice shelf and glacier. We explore the extent to which easy-to-access remote sensing and modeling data can help to define environmental conditions leading to calving front retreat. For the first time, we present a circum-Antarctic record of glacier and ice shelf front change over the last two decades in combination with environmental variables such as air temperature, sea ice days, snowmelt, sea surface temperature, and wind direction. We find that the Antarctic Ice Sheet area decreased by −29 618 ± 1193 km² in extent between 1997–2008 and gained an area of 7108 ± 1029 km² between 2009 and 2018. Retreat concentrated along the Antarctic Peninsula and West Antarctica including the biggest ice shelves (Ross and Ronne). In several cases, glacier and ice shelf retreat occurred in conjunction with one or several changes in environmental variables. Decreasing sea ice days, intense snowmelt, weakening easterlies, and relative changes in sea surface temperature were identified as enabling factors for retreat. In contrast, relative increases in mean air temperature did not correlate with calving front retreat. For future studies a more appropriate measure for atmospheric forcing should be considered, including above-zero-degree days and temperature extreme events. To better understand drivers of glacier and ice shelf retreat, it is critical to analyze the magnitude of basal melt through the intrusion of warm Circumpolar Deep Water that is driven by strengthening westerlies and to further assess surface hydrology processes such as meltwater ponding, runoff, and lake drainage.

The frontal position of an ice shelf is an important parameter for ice dynamic modelling, the computation of mass fluxes, mapping glacier area change, calculating iceberg production rates and the estimation of ice discharge to the ocean. Until now, continuous and up-to-date information on Antarctic calving front locations is scarce due to the time-consuming manual delineation of fronts and the previously limited amount of suitable earth observation data. Here, we present IceLines, a novel data set on Antarctic ice shelf front positions to assess calving front change at an unprecedented temporal and spatial resolution. More than 19,400 calving front positions were automatically extracted creating dense inter- and intra-annual time series of calving front change for the era of Sentinel-1 (2014-today). The calving front time series can be accessed via the EOC GeoService hosted by DLR and is updated on a monthly basis. For the first time, the presented IceLines data set provides the possibility to easily include calving front dynamics in scientific studies and modelling to improve our understanding about ice sheet dynamics.