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Cross-correlation of image-pairs derived from both optical and synthetic aperture radar satellite imagery is the most common technique for measuring glacier flow velocity and quantifying the dynamics and discharge of glaciers. While the technique has been shown to be effective on polar ice sheets, the accuracy of satellite-derived velocities in temperate alpine regions is poorly constrained. Flow velocities were measured in situ using an RTK-GPS along four profiles on Taku, Matthes, Vaughan-Lewis, and Llewellyn Glaciers in southeast Alaska from 2016 through 2018. These GNSS-measured velocities were correlated against spatially coincident and contemporaneous satellite-derived velocity datasets, including both versions 1 and 2 of ITS_LIVE and velocities determined by offset tracking of SAR data in the Sentinel Application Platform (SNAP) and GAMMA (RETREAT dataset). Significant gaps in velocity maps derived from optical imagery (Landsat/Sentinel-2) were observed and determined to be due to low coherence rather than cloud contamination. Cross-correlation of SAR data (Sentinel-1) in SNAP and RETREAT achieved better accuracy compared to optical, although a strong dichotomy in performance was observed. SAR-derived velocities in the accumulation zone and transient snowline area showed overall poor correlation to GNSS-measured velocities that were likely due to significant shifts in the backscatter amplitude of the homogenous, snow-covered surface, although both SAR-derived SNAP and RETREAT velocities were anomalously accurate where GNSS velocities were below 0.10 m/day along the glacier margins. SNAP and RETREAT achieved the most accurate results in the study in the ablation zone of the Llewellyn Glacier where stable backscatter targets on the glacier surface (crevasses, supraglacial debris) facilitated high coherence in the cross-correlation procedure. SAR data are likely the most suitable for the derivation of satellite-derived velocities on temperate alpine glaciers, particularly in slow-moving and ablation zones, but should be subject to scrutiny for fast-flowing glaciers and those with an active hydrologic surface system. [ABSTRACT FROM AUTHOR]

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]

Climate change has reduced global ice mass over the last 2 decades as enhanced warming has accelerated surface melt and runoff rates. Glaciers have undergone dynamic processes in response to a warming climate that impacts the surface geometry and mass distribution of glacial ice. Until recently no single technique could consistently measure the evolution of surface flow for an entire glaciated region in three dimensions with high temporal and spatial resolution. We have improved upon earlier methods by developing a technique for mapping, in unprecedented detail, the temporal evolution of glaciers. Our software computes north, east, and vertical flow velocity and/or displacement time series from the synthetic aperture radar (SAR) ascending and descending range and azimuth speckle offsets. The software can handle large volumes of satellite data and is designed to work on high-performance computers (HPCs) as well as workstations by utilizing multiple parallelization methods. We then compute flow velocity–displacement time series for glaciers in southeastern Alaska during 2016–2021 and observe seasonal and interannual variations in flow velocities at Seward and Malaspina glaciers as well as culminating phases of surging at Klutlan, Walsh, and Kluane glaciers. On a broader scale, this technique can be used for reconstructing the response of worldwide glaciers to the warming climate using archived SAR data and for near-real-time monitoring of these glaciers using rapid revisit SAR data from satellites, such as Sentinel-1 (6 or 12 d revisit period) and the forthcoming NISAR mission (12 d revisit period). [ABSTRACT FROM AUTHOR]

Spring runoff measurements of Arctic watersheds are challenging given the remote location and the often dangerous field conditions. This study combines remote sensing techniques and field measurements to evaluate the applicability of synthetic aperture radar (SAR) to defining spring breakup of the braided lower Kuparuk River, North Slope, Alaska. A statistical analysis was carried out on a time series (2001 - 10) of SAR images acquired from the European Remote Sensing Satellite (ERS-2) and the Canadian RADARSAT satellite, as well as on measured runoff. On the basis of field information, the SAR images were separated into pre-breakup, breakup, and post-breakup periods. Three variables were analyzed for their suitability to bracket the river breakup period: image brightness, variance in brightness over the river length, and a sum of rank order change analysis. Variance in brightness was found to be the most reliable indicator. A combined use of that variance and sum of rank order change appeared promising when enough images were available. The temporal resolution of imagery served as the major limitation in constraining the timing of the hydrologic event. Challenges associated with spring runoff monitoring and the sensitive nature of SAR likely resulted in an earlier detection of surficial changes by the remote sensing technique compared to the field runoff observations. Given a sufficient temporal resolution, SAR imagery has the potential to improve the spatiotemporal monitoring of Arctic watersheds for river breakup investigations. [ABSTRACT FROM AUTHOR]

The 3D Elevation Program (3DEP) has created partnership opportunities to increase the collection of high-resolution elevation data across the United States, eventually leading to complete coverage of high-resolution, three-dimensional (3D) information from light detection and ranging (lidar) data across the entire country (interferometric synthetic aperture radar in Alaska). While 3DEP data are collected at different times and by varying producers, the assumption is that the use of the 3DEP Lidar Base Specification will provide standardized and consistent data across data collections. Another assumption is that the integration of lidar data into the seamless digital elevation models increases the accuracy of the derived products. This study tests these assumptions and updates some of the accuracy metrics that were done on previous versions of the standard products. [ABSTRACT FROM AUTHOR]

A refined, small baseline subset (SBAS) interferometric synthetic aperture (InSAR) method has been developed to estimate time-series surface deformation through fusion of multi-interferogram processing. Using a synthetic data set that takes into account two time-varying deformation sources, topography-induced errors, atmospheric delay anomalies, orbital errors and temporal decorrelation, all of which are based on realistic ERS-1/ERS-2 SAR image acquisition dates and baseline configuration over the Seguam volcano, Alaska, we assess the accuracy of the refined SBAS technique. Detailed comparison between SBAS-derived products, including time-series deformation maps, atmospheric delays and baseline errors with those of synthetic values, attest the robustness of the refined SBAS technique. The root mean square error of the mean deformation rate between the simulated and SBAS-retrieved is about 0.66 mm year–1. Thus, the measurement accuracy of the refined SBAS method would be around 1 mm year–1. [ABSTRACT FROM AUTHOR]

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed. [ABSTRACT FROM AUTHOR]

Seasonal landfast sea ice stretches along most Arctic coastlines and serves as a platform for community travel and subsistence, industry operations, and as a habitat for marine mammals. Landfast ice can feature smooth ice and areas of m-scale roughness in the form of pressure ridges. Such ridges can significantly hamper trafficability, but if grounded can also serve to stabilize the shoreward ice. We investigate the use of synthetic aperture radar interferometry (InSAR) to assess the formation and movement of ridges in the landfast sea ice near Utqiaġvik, Alaska. The evaluation is based on the InSAR-derived surface elevation change between two TanDEM-X bistatic image pairs acquired during January 2012. We compare the results with backscatter intensity, coastal radar data, and SAR-derived ice drift and evaluate the utility of this approach and its relevance for evaluation of ridge properties, as well as landfast sea ice evolution, dynamics, and stability. [ABSTRACT FROM AUTHOR]