Search

Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

During the past 20 years, multi-channel radar emerged as a key tool for deciphering an ice sheet's internal architecture. To assign ages to radar reflections and connect them over large areas in the ice sheet, the layer genesis has to be understood on a microphysical scale. Synthetic radar trace modelling based on the dielectric profile of ice cores allows for the assignation of observed physical properties' variations on the decimetre scale to radar reflectors extending from the coring site to a regional or even whole-ice-sheet scale. In this paper we rely on the available dielectric profiling data of the northern Greenland deep ice cores: NGRIP, NEEM and EGRIP. The three records are well suited for assigning an age model to the stratigraphic radar-mapped layers, and linking up the reflector properties to observations in the cores. Our modelling results show that the internal reflections are mainly due to conductivity changes. Furthermore, we deduce fabric characteristics at the EGRIP drill site from two-way-travel-time differences of along and across-flow polarized radarwave reflections of selected horizons (below 980 m). These indicate in deeper parts of the ice column an across-flow concentrated c-axis fabric. [ABSTRACT FROM AUTHOR]

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. [ABSTRACT FROM AUTHOR]

Differential synthetic aperture radar interferometry (DInSAR) allows for retrieval of ice velocity measurements of high resolution and accuracy. One of the main error sources in DInSAR is the phase unwrapping procedure. Unwrapping errors may be caused by several processes, including shear stresses associated with large motion gradients, which lead to loss of interferometric coherence. In many cases, unwrapping errors reach magnitudes corresponding to velocities of tens or even hundreds of meters per year. Traditional DInSAR implementations include pixel masking based on coherence thresholding; however, such a masking is not always sufficient. Consequently, the state-of-the-art for ice velocity retrievals involves either manual inspection of individual measurements or simply discarding measurements in regions where ice flow exceeds a predefined threshold. Here, we instead apply a masking based on thresholding of a pixel connectivity estimate with respect to a reference point, which aims to detect unwrapping errors based only on the estimated coherence pattern. The method is tested on both simulated and real data Sentinel-1 data from the Greenland Ice Sheet and effectively detects the majority of unwrapping errors (recall of 0.84 for the best performing threshold), although with a relatively low precision (0.52 for the best performing threshold). Importantly, higher magnitude unwrapping errors are associated with lower connectivity values, meaning that undetected errors have a significantly lower magnitude (median of 1.7 m/y, corresponding to a single phase cycle, compared with 40.5 m/y with no masking). [ABSTRACT FROM AUTHOR]

On 30 September 2017, an Air France Airbus A380-800 suffered a failure of its fourth engine while over Greenland. This failure resulted in the loss of the engine fan hub, fan blades and surrounding structure. An initial search recovered 30 pieces of light debris, but the primary part of interest, a ~220 kg titanium fan hub, was not recovered because it had a different fall trajectory than the light debris, impacted into the ice-sheet's snow surface, and was quickly covered by drifting snow. Here we describe the methods used for the detection of the fan hub and details of the field campaigns. The search area included two crevasse fields of at least 50 snow-covered crevasses 1 to ~30 m wide with similar snow bridge thicknesses. After 21 months and six campaigns, using airborne synthetic aperture radar, ground-penetrating radar, transient electromagnetics and an autonomous vehicle to survey the crevasse fields, the fan hub was found within ~1 m of a crevasse at a depth of ~3.3 to 4 m and was excavated with shovels, chain saws, an electric winch, sleds and a gasoline heater, by workers using fall-arrest systems. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted that meltwater in supraglacial lakes (SLs) can be buried during frozen season in the Greenland Ice Sheet (GrIS). Meltwater in buried lakes (BLs) can even persist through the winter, disturbing the englacial thermal regime and providing an important buffer against GrIS's contribution to sea-level rise. However, little is known about the inter-annual BL dynamics in the GrIS, and there is no quantitative statistic about the overall buried percentage. Here, we conduct a satellite-based study to automatically map the winter BLs over the GrIS during 2017–2022 using multi-source optical and synthetic aperture radar (SAR) images on the Google Earth Engine (GEE) platform. To eliminate the interferences from other weak microwave reflecting surfaces, summer SLs are first extracted from Landsat 8 and Sentinel-2 images to determine the potential BL searching areas on winter Sentinel-1 images. A self-adaptive thresholding algorithm is proposed to extract BLs within the dilated summer SLs using histogram-based morphological edge detectors. BLs extracted by the proposed method and visual interpretation show a substantial agreement with a precision of 0.82 and a Kappa coefficient of 0.70. On average, a total buried lake area of 182.27 km2 was observed each winter during the period 2017–2022. BLs were mainly distributed in the Center-West, South-West and North-East Basins, with the majority occurring at elevations between 800 and 1700 m. In 2019–2020, a sudden extension of BLs was observed over the GrIS, especially in the North-East Basin where abnormally high temperatures and surface runoff were recorded. In 2021–2022, a widespread distribution of BLs in the South-West Basin was observed after abnormal snowfall. Overall, about 13% of the GrIS summer SLs can persist through winter, suggesting the potential for meltwater hydrofracture in winter over large areas. [Display omitted] • Winter buried lakes in Greenland are mapped from multi-sensor images on GEE. • A total buried lake area of 182.27 km2 are observed each winter during 2017–2022. • About 13% of the summer supraglacial lakes in Greenland can persist through winter. • Enhanced surface melt in 2019–2020 resulted in the sudden extension of buried lakes. • Abnormal snowfall in early winter can favor the formation of buried lakes. [ABSTRACT FROM AUTHOR]

The article presents a study which analyses the behavior of superglacial lakes on the Greenland ice sheet using a new simulation model. The study uses a model that combines digital elevation model (DEM) derived from interferometric synthetic aperture radar (InSAR) data with prehistoric hydraulic flow equations to route and pond surface water runoff. Results show accurate simulation of maximum cumulative lake area with only 1.5% overestimate.

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10−5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10−6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors. [ABSTRACT FROM AUTHOR]

Englacial layers in Antarctica and Greenland are indicators of the dynamic, rheological, and subglacial configuration of the ice sheets. Airborne radar sounder data is currently the primary remote sensing solution for directly observing englacial layers and imaging englacial structures at the glacier-catchment to ice-sheet scale. Traditional along-track synthetic aperture (SAR) processing can limit the detectability and interpretability of englacial layer geometry due to destructive interference in the processed signal. This reflection loss can destroy layer signals and obfuscate automatic or manual tracing and interpretation.To address this challenge, we provide a reconstruction algorithm to address the problem of destructive phase interference during the radargram formation. We develop and apply a novel SAR processor optimized for layer detection that enhances the Signal-to-Noise ratio of specular reflectors of any steepness. The algorithm also enables the automatic estimation of layer slope. Notably, this approach does not require layers to be long or continuous to produce usable slope fields, overcoming limitations of previous approaches developed for shallower and slower-flowing areas with intact layers. We illustrate the application of this new processing approach to create 2D slope fields for inverse modelling to constrain bed slipperiness flow history in ice sheet models using data acquired in by the British Antarctic Survey (BAS) Polarimetric radar Airborne Science Instrument (PASIN) in the Institute Ice Stream catchment of West Antarctica. [ABSTRACT FROM AUTHOR]