We report the discovery of a previously undocumented subglacial lake beneath the Flade Isblink Ice Cap in North East Greenland. Using satellite Earth Observation data (ICESat-2 elevation data and Sentinel-1 Double Difference InSAR) in the period October 2018 to December 2024 and outputs from a regional climate model, we quantify the lake's role in regional hydrology. The subglacial lake's volume is characterised by an annual cycle of filling during the melt season and drainage in September-October, with the lake storing up to 63 ± 23% of the yearly runoff from its catchment area. In most years, lake drainage causes a 2-3-month lag between peak surface-meltwater production and downstream discharge into the nearby proglacial lake, Romer Sø. Lake drainage occurred in all years of our observation period except 2022, which had the lowest surface melt rates, suggesting that a minimum water-input threshold is required to initiate lake drainage. Additionally, our dataset does not show any evidence of a hydrological connection between the lake and the nearby, well-known subglacial lake on the high plateau of Flade Isblink. Our findings highlight how subglacial conditions may substantially modify the outflow of subglacial water to the ice margin with potential impacts on downstream hydrology and ecosystems. It further demonstrates the potential of integrating remote sensing with hydrological modelling to understand ice-sheet hydrology.

Abstract. We present an updated version of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) ice velocity product (https://doi.org/10.22008/FK2/K70OPK; Solgaard and Kusk (2026)), providing a continuous time series of Greenland Ice Sheet velocity mosaics from January 2016 to the present. The product is derived from Sentinel-1 synthetic aperture radar (SAR) data, gridded at 200 m spatial resolution and updated every 12 days using data spanning two consecutive Sentinel-1 repeat cycles (24 days). Data are typically released within 10 days of the final acquisition and include all valid 6- and 12-day image pairs within the 24-day window. This update includes several important improvements to the processing chain. The spatial resolution has been refined from 500 m to 200 m, justified by the implementation of an adaptive correlation template size approach for offset tracking, improving velocity retrievals and enhancing delineation of narrow outlet glaciers. We further implement a new mosaicking strategy, which reduces noise associated with ionospheric disturbances. Additional improvements include enhanced error handling and outlier rejection. The full processing workflow is described, including data selection, mosaicking, uncertainty estimation, and filtering procedures. Validation against in-situ GNSS measurements over the full time series shows that the standard deviation of the differences between satellite- and GNSS-derived velocities (with corresponding bias) is 22 m/yr (-0.3 m/yr) and 38 m/yr (-0.4 m/yr) for the easting and northing components, respectively. These values fall within expected ranges, although a substantial fraction of the discrepancy likely reflects uncertainty in the GNSS measurements. This interpretation is supported by validation over stable terrain, where substantially lower values are obtained: 9 m/yr (0.1 m/yr) and 15 m/yr (-0.1 m/yr) for the easting and northing components, respectively. Compared to the previous product version, uncertainties are higher due to a prolonged period when only one Sentinel-1 satellite was operational, resulting in increased noise and reduced temporal sampling. We quantify the impact of these conditions on spatial coverage. Overall, coverage is highest during winter, when radar coherence is strong and acquisitions are most comprehensive, whereas summer coverage is reduced due to surface melt. Despite these seasonal and mission-related constraints, the PROMICE ice velocity product provides consistent temporal sampling and broad spatial coverage, supporting investigations of ice-sheet-wide and glacier-specific dynamics and ice discharge on seasonal to multi-year timescales.

Many persistent scatterer interferometric SAR (PSInSAR) algorithms are based on the phase difference of connected persistent scatterer (PSs), which are usually spatially integrated to recover the deformation gradients with respect to a common reference point. In the presence of noise, this can lead to errors propagating throughout the whole PS network. In our work, we adapt the concept of connectivity originally proposed in the DInSAR context as a way to estimate the quality of the phase integration for each PS. We simulate PS networks under a variety of noise conditions and find that connectivity is consistently correlated with integration errors and provides complementary information compared to local quality parameters, such as temporal coherence. We discuss the use of connectivity to optimize the selection of spatial references and to discard measurements, which are more likely to be affected by integration errors. Finally, we validate our methodology on a real dataset from TerraSAR-X covering the Greater Copenhagen area.

Abstract Subglacial hydrology can exert an important control on ice flow by affecting friction at the ice‐bedrock interface. Here, we report on a series of subglacial drainage events along the Northeast Greenland Ice Stream (NEGIS), initiating as far inland as 500 km from the margin of Zachariae Isstrøm. The drainage events exhibit local transient uplift, followed by prolonged subsidence, measured by differential satellite synthetic aperture radar interferometry (DInSAR). In downstream regions, drainage events are associated with temporary acceleration in ice flow. The high spatiotemporal resolution of the DInSAR measurements allows for a detailed mapping of the drainage propagation pathway. We show that multiple drainage cascades have occurred along the same pathway over the years 2020–2022. Finally, the propagation speed of subglacial water flow is found to vary greatly along NEGIS, suggesting that fundamental differences could exist in the subglacial environment.

Synthetic aperture radar (SAR) satellite images are used increasingly more for Earth observation. While SAR images are useable in most conditions, they occasionally experience image degradation due to interfering signals from external radars, called radio frequency interference (RFI). RFI-affected images are often discarded in further analysis or preprocessed to remove the RFI. However, few on-ground radars can cause RFI in SAR images and such information can thus increase domain awareness greatly over both land and sea, where, e.g., localizing and characterizing RFI signals in the ocean could help classify otherwise overlooked ships. The aim of the current study is to detect and localize RFI signals automatically in Sentinel-1 level-1 images and further characterize the on-ground radar. The spatial structure of RFI signals vary greatly. A convolutional autoencoder (CAE) was therefore developed to reconstruct RFI-free Sentinel-1 images. Conversely, RFI-affected images could not be well reconstructed. Anomalous heatmaps were then developed to automatically detect and localize RFI anomalies in the images under varying environmental and geographical conditions, whereafter the external radar characteristics were extracted manually from Sentinel-1 level-0 data. We could consequently classify and localize RFI signals believed to originate from both stationary radars and ship-borne radars. We further argue that the calculated ship-borne radar characteristics correspond to those of air-surveillance radars. Empirically, the method showed better detection results than those of previous studies. Our study shows that more information can be extracted from certain detected objects, such as ships, from SAR images.

The dynamics of the Greenland Ice Sheet are affected by surface meltwater reaching the base of the ice, altering ice contact with the bedrock. Lack of understanding of this evolution hampers the ability to predict the effects of increasing temperatures on the Greenland Ice Sheet mass balance. Here we present a unique high-resolution study of ice velocity response to surface melting based on data from a COSMO-SkyMed satellite campaign over Upernavik Isstrøm (Northwest Greenland) for two months around the end of the 2014 melt season. We show that the velocity variations, due to both short-term (days) and seasonal variations in surface melt rates, are increasing in relative strength farther from the glacier terminus. Furthermore, we observe how ice dynamic response to frontal retreat, reaching several kilometres inland, can obscure the meltwater-induced velocity change close to the terminus. Future studies should consider the flow velocity dependence on the distance to the terminus, and local geometry, to distinguish subglacial hydrologic system changes from frontal processes and local basal conditions.

This article presents a methodology for localizing radio frequency interference (RFI) signals in Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 SAR satellites. RFI are caused by on-ground radars, and their detection and localization thus provide valuable information for decision makers. In this study, an unsupervised deep learning model based on a Convolutional Autoencoder is used to detect and localize RFI signals in SAR images. The CAE reconstructs the SAR images, excluding RFI signals and other large-scale anomalies. Anomalies are detected by comparing the original images with their reconstructions, and a secondary classification scheme is used to identify RFI signals among the detected anomalies. Results show that the proposed method detects and localizes RFI signals, even in complex regions. The automatic localization of RFI signals in SAR images can enhance various applications such as maritime domain awareness and border surveillance.

<p>We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) ice velocity product, which is a time series of ice velocity mosaics derived using offset tracking on Sentinel-1 SAR data.  The time series starting in January 2016 is continuously updated with a new mosaic every 12 days and is posted at 500 m grid resolution. Within PROMICE, the ice velocity product is used directly as input to estimate the solid ice discharge from the Greenland Ice Sheet as well as to study ice dynamic processes on seasonal and multi-annual time scales. Recently, we have made changes to the processing chain due to spurious cases of slow down detected in a few glaciers in Southeast Greenland. In this contribution, we discuss how this was resolved as well as other recent improvements to the product.</p>

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).

The Getz region of West Antarctica is losing ice at an increasing rate; however, the forcing mechanisms remain unclear. Here we use satellite observations and an ice sheet model to measure the change in ice speed and mass balance of the drainage basin over the last 25-years. Our results show a mean increase in speed of 23.8 % between 1994 and 2018, with three glaciers accelerating by over 44 %. Speedup across the Getz basin is linear, with speedup and thinning directly correlated confirming the presence of dynamic imbalance. Since 1994, 315 Gt of ice has been lost contributing 0.9 ± 0.6 mm global mean sea level, with increased loss since 2010 caused by a snowfall reduction. Overall, dynamic imbalance accounts for two thirds of the mass loss from this region of West Antarctica over the past 25-years, with a longer-term response to ocean forcing the likely driving mechanism.

The application of Sentinel-1 interferometry to ice velocity measurements has until recently been limited by the significant horizontal scene motion associated with ice flow, which causes phase discontinuities (and associated unwrapping problems) at burst boundaries in Terrain Observation by Progressive Scans (TOPS) interferograms. Coregistering with a multiyear averaged external velocity mosaic based on offset-tracking can account for the bulk of the ice motion, but residual discontinuities sometimes remain, for example, due to seasonal variations in the ice velocity, or due to error sources such as azimuth shifts caused by ionospheric propagation. The presented method extends the external velocity coregistration with a local, spatially varying, coregistration in the burst overlap regions. This is based on the extended spectral diversity principle, which can only be applied in the overlap regions, but offers superior accuracy and resolution compared with traditional coregistration methods. The method considerably reduces phase discontinuities at burst boundaries, and potential new phase discontinuities at the overlap region edges are suppressed by an azimuth tapering of the applied coregistration shifts. An example scene is presented, and the phase discontinuities before and after application of the method are evaluated. The method is seen to remove phase discontinuities, with no adverse effects.

We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) Ice Velocity product (https://doi.org/10.22008/promice/data/sentinel1icevelocity/greenlandicesheet, Solgaard and Kusk, 2021), which is a time series of Greenland Ice Sheet ice velocity mosaics spanning September 2016 through to the present. The product is based on Sentinel-1 synthetic aperture radar data and has a 500 m grid spacing. A new mosaic is available every 12 d and spans two consecutive Sentinel-1 cycles (24 d). The product is made available within ∼ 10 d of the last acquisition and includes all possible 6 and 12 d pairs within the two Sentinel-1A cycles. We describe our operational processing chain from data selection, mosaicking, and error estimation to final outlier removal. The product is validated against in situ GPS measurements. We find that the standard deviation of the difference between satellite- and GPS-derived velocities (and bias) is 20 m yr−1 (−3 m yr−1) and 27 m yr−1 (−2 m yr−1) for the components in an eastern and northern direction, respectively. Over stable ground the values are 8 m yr−1 (0.1 m yr−1) and 12 m yr−1 (−0.6 m yr−1) in an eastern and northern direction, respectively. This is within the expected values; however, we expect that the GPS measurements carry a considerable part of this uncertainty. We investigate variations in coverage from both a temporal and spatial perspective. The best spatial coverage is achieved in winter due to the comprehensive data coverage by Sentinel-1 and high coherence, while summer mosaics have the lowest coverage due to widespread melt. The southeast Greenland Ice Sheet margin, along with other areas of high accumulation and melt, often has gaps in the ice velocity mosaics. The spatial comprehensiveness and temporal consistency make the product ideal both for monitoring and for studying ice-sheet-wide and glacier-specific ice discharge and dynamics of glaciers on seasonal scales.

A major challenge in applying Sentinel-1 interferometry to measure ice sheet velocity is the significant horizontal scene motion associated with ice flow, which can cause phase discontinuities at burst boundaries in the TOPS interferograms, leading to phase unwrapping problems. We present a simple method that uses an external multi-year average velocity map to account for the bulk of the scene motion in the coregistration, allowing phase unwrapping even in scenes with significant azimuth motion. The method is applied to two regions in Greenland, and a validation using GPS is presented.

In recent years, the Sentinel-1 satellites have provided a data archive of unprecedented volume, delivering C-band Synthetic Aperture Radar (SAR) acquisitions over most of the polar ice sheets with a repeat-pass period of 6–12 days using Interferometric Wide swath (IW) imagery acquired in Terrain Observation by Progressive Scans (TOPS) mode. Due to the added complexity of TOPS-mode interferometric processing, however, Sentinel-1 ice velocity measurements currently rely exclusively on amplitude offset tracking, which generates measurements of substantially lower accuracy and spatial resolution than would be possible with Differential SAR Interferometry (DInSAR). The main difficulty associated with TOPS interferometry lies in the spatially variable azimuth phase contribution arising from along-track motion within the scene. We present a Sentinel-1 interferometric processing chain, which reduces the azimuth coupling to the line-of-sight phase signal through a spatially adaptive coregistration refinement incorporating azimuth velocity measurements. The latter are based on available ice velocity mosaics, optionally supplemented by Burst-Overlap Multi-Aperture Interferometry. The DInSAR processing chain is demonstrated for a large drainage basin in Northeast Greenland, encompassing the Northeast Greenland Ice Stream (NEGIS), and integrated with state-of-the-art offset tracking measurements. In the ice sheet interior, the combined DInSAR and offset tracking ice velocity product provides a spatial resolution of 50 × 50 m and 1-sigma accuracies of 0.18 and 0.44 m/y in the x and y components respectively, compared to GPS.

The Northeast Greenland Ice Stream (NEGIS) extends around 600 km upstream from the coast to its onset near the ice divide in interior Greenland. Several maps of surface velocity and topography of interior Greenland exist, but their accuracy is not well constrained by in situ observations. Here we present the results from a GPS mapping of surface velocity in an area located approximately 150 km from the ice divide near the East Greenland Ice-core Project (EastGRIP) deep-drilling site. A GPS strain net consisting of 63 poles was established and observed over the years 2015–2019. The strain net covers an area of 35 km by 40 km, including both shear margins. The ice flows with a uniform surface speed of approximately 55 m a−1 within a central flow band with longitudinal and transverse strain rates on the order of 10−4 a−1 and increasing by an order of magnitude in the shear margins. We compare the GPS results to the Arctic Digital Elevation Model and a list of satellite-derived surface velocity products in order to evaluate these products. For each velocity product, we determine the bias in and precision of the velocity compared to the GPS observations, as well as the smoothing of the velocity products needed to obtain optimal precision. The best products have a bias and a precision of ∼0.5 m a−1. We combine the GPS results with satellite-derived products and show that organized patterns in flow and topography emerge in NEGIS when the surface velocity exceeds approximately 55 m a−1 and are related to bedrock topography.

The Getz region is a marine-terminating sector of West Antarctica, characterised by a ~650 km long ice shelf that buttresses the inland ice sheet. The majority of the Getz drainage basin is grounded well below sea level, and while the ice shelf has thinned, its calving front has remained relatively stable since the early ’90s. Satellite observations have shown strong thinning of both the ice sheet and ice shelf over the past 25-years, and mass balance studies have shown that the sector is negatively imbalanced (−16.4 ± 4.0 Gt/year). In this study, we use satellite data to measure ice speed in the Getz region, over a 25-year period from 1994 to 2019. We use Synthetic Aperture Radar (SAR) data from historical missions including ERS-1, 2 and ALOS PALSAR, in combination with newer data from the Sentinel-1a & b satellite constellation, to generate annual velocity maps. The Sentinel-1 data extend the historical velocity record and provides a new high temporal resolution record, 6-day averaged solutions, of velocity change since 2017. We used satellite observations in combination with the BISICLES ice sheet model to fill gaps in the observational record, and to measure ice discharge and from the region. We find there are 14 distinct flow units that drain the Getz coastline, with average speeds ranging from 153 ± 7 to 1053 ± 194 m/yr around the grounding line. Our results show that all of these flow units have sped up during the study period, since 1994. At the grounding line, we measure an average speed increase of ~5 m/yr2, with some flow units accelerating by over 11 m/yr2. We find that the spatial pattern of change in ice speed is consistent with the regions of strongest surface lowering, which on some flow units occurs at rates of up to -2.4 m/yr. Our observations show that ice speedup is greatest where the ice is thickest (>700 m), and grounded most deeply. This long 25-year record of change also shows that on some ice streams, the rate of change in ice speed has not been constant throughout the study period. In some regions where ocean temperature measurements are also available, we find that increases in ice speed coincide with the periodic presence of circumpolar deep water, which may therefore be responsible for driving this change. In summary, this study provides a new record of change in ice speed for a rapidly evolving region of Antarctica. In the future, it will be important to use both ocean models and observations to improve our understanding of how ocean forcing is driving dynamic imbalance in the region. This will improve our understanding of the physical mechanisms driving change in Antarctica, helping us to better constrain the ice sheets future contribution to global sea level rise.

We use remotely sensed ice velocities in combination with observations of surface elevation and glacier area change to investigate the dynamics of Hagen Br ae, North Greenland in high detail over the last 35 years. From our data, we can establish for the first time that Hagen Br ae is a surge-type glacier with characteristics of both Alaskan- and Svalbard-type surging glaciers. We argue that the observed surge was preconditioned by the glacier geometry and triggered by englacially stored meltwater. At present, the glacier is in a transitional state between active and quiescence phases and is not building up to its pre-surge geometry. We suggest that the glacier is adjusting to the loss of its floating section, general thinning, and changes in fjord conditions that occurred over the study period which are unrelated to the surge behavior. The high temporal resolution of the ice velocity data gives insight to the sub-annual glacier flow.

Seasonal glacier ice velocities are important for understanding controlling mechanisms of ice flow. For many Greenlandic glaciers, however, these measurements are limited by low temporal resolution. We present seasonal ice velocity changes, melt season onset and extent, and ice front positions for 45 Greenlandic glaciers using 2015–2017 Sentinel‐1 synthetic aperture radar data. Seasonal velocity fluctuations of roughly half of the glaciers appear to be primarily controlled by surface melt‐induced changes in the subglacial hydrology. This includes (1) glaciers that speed up with the onset of surface melt and (2) glaciers with comparable late winter and early melt season velocities that show significant slowdown during most of the melt season and speedup during winter. In contrast, less than a quarter of the study glaciers show strong correspondence between seasonal ice speed and terminus changes. Our results pinpoint seasonal variations across Greenland, highlighting the variable influence of meltwater on year‐round ice velocities.

Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance.