Data-driven classification algorithms have proved to do well for automatic target recognition (ATR) in synthetic aperture radar (SAR) data. Collecting data sets suitable for these algorithms is a challenge in itself as it is difficult and expensive. Due to the lack of labeled data sets with real SAR images of sufficient size, simulated data play a big role in SAR ATR development, but the transferability of knowledge learned on simulated data to real data remains to be studied further. In this letter, we show the first study of Transfer Learning between a simulated data set and a set of real SAR images. The simulated data set is obtained by adding a simulated object radar reflectivity to a terrain model of individual point scatters, prior to focusing. Our results show that a Convolutional Neural Network (Convnet) pretrained on simulated data has a great advantage over a Convnet trained only on real data, especially when real data are sparse. The advantages of pretraining the models on simulated data show both in terms of faster convergence during the training phase and on the end accuracy when benchmarked on the Moving and Stationary Target Acquisition and Recognition data set. These results encourage SAR ATR development to continue the improvement of simulated data sets of greater size and complex scenarios in order to build robust algorithms for real life SAR ATR applications.

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.

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.

This paper presents the research and development of two terahertz imaging systems based on photonic and electronic principles, respectively. As part of this study, a survey of ongoing research in the field of terahertz imaging is provided focusing on security applications. Existing terahertz imaging systems are reviewed in terms of the employed architecture and data processing strategies. Active multichannel measurement method is found to be promising for real-time applications among the various terahertz imaging techniques and is chosen as a basis for the imaging instruments presented in this paper. An active system operation allows for a wide dynamic range, which is important for image quality. The described instruments employ a multichannel high-sensitivity heterodyne architecture and aperture filling techniques, with close to real-time image acquisition time. In the case of the photonic imaging system, mechanical scanning is completely obsolete. We show 2-D images of simulated 3-D image data for both systems. The reconstruction algorithms are suitable for 3-D real-time operation, only limited by mechanical scanning.

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 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.