The Center for Oldest Ice Exploration (COLDEX) project is exploring Antarctica to find a continuous ice record from the present to 1.5 million years ago and document the mid-Pleistocene transition, which occurred ~1 million years ago. The current longest ice record is 800,000 years old. This work describes a new 600-900 MHz UHF radar to help address this challenge with a 1) much larger, 22.8 m or 57-wavelength, cross-track antenna array and 2) higher transmit power, than prior renditions. Survey flights on a Basler aircraft were conducted from the South Pole in 2022-2023 and in 2023-2024. In this work, we provide an overview of the design requirements, the radar system architecture, and antenna array implementation. We also present preliminary results from aerial surveys conducted in Antarctica.

As a part of the project "Melting at Thwaites grounding zone and its control on sea level" (MELT), we developed a compact 750-MHz ice-penetrating radar for phase-sensitive measurements to infer basal melt and ice layer displacement using multiple passes. The radar operates with 300-MHz bandwidth and a peak output power of 400 W. It is equipped with a small antenna array designed to fit within the limited space underneath the floor of a Twin Otter aircraft (without out-of-mold-line protuberances). The radar was installed and deployed onboard a Twin Otter airplane from the British Antarctic Survey (BAS) as a part of the ongoing International Thwaites Glacier Collaboration (ITGC). We conducted multiple radar survey flights during two consecutive austral summer seasons, collecting ~5 TB of raw data. In this paper, we present an overview of the radar system and antenna implementations. We provide a summary of our field operations and present sample data products to illustrate the instrument capabilities to sound ice as thick as ~1,400 m and map internal reflecting horizons near the grounding line of TG with sub-m vertical resolution. We also discuss the utility of our repeat pass data for inferring ice shelf basal melt rates.

We developed a multi-channel, ultra-wideband, microwave radar for swath mapping of snow layers on land, sea ice and ice sheets. The system operates in the 2-18 GHz band (up to 16-GHz bandwidth) with two nadir-looking transmitters and six receivers; and a dual-polarized, forward-looking transmitter/receiver pair. The system addresses the limitations in cross-track resolution found in prior single-channel instruments and will help improve snow thickness retrieval in areas of complex surface topography. This paper presents an overview of the radar electronics and antenna system and their installation on the NASA P-3B aircraft. We also present initial results from a short field campaign conducted in Greenland in the spring of 2022.

This paper details the preliminary efforts of applying the deep learning transformer architecture to automatically track annual layer stratigraphy in echogram images obtained from mapping near-surface ice layers using airborne radars. Following the success of the transformer architecture in the natural language processing and computer vision communities, we explore a variant termed Echogram Vision Transformer (EchoViT) on the radar echogram layer tracking (RELT) problem. The proposed approach divides the echogram images into patches using different schemes inspired by tokenization methods in natural language processing. We then apply a soft-attention mechanism to model interdependencies between the patches, capturing spatiotemporal stratigraphic information. Experiments conducted on the CREED dataset demonstrate the superiority of transformer-based architectures over existing convolutional-based architectures. Furthermore, the EchoViT fast-time and EchoViT slow-time patchifying schemes achieved precise tracking of the layers with submeter MAE of 3.39 and 3.55, respectively, while the use of cropped patches led to suboptimal results.

We present the design and field test results for a 600 to 900 MHz polarimetric ice penetrating radar that can be operated on the ground or from an airborne platform. This system is part of a development to build a dual band (VHF/UHF) polarimetric ice sounding radar suite. The VHF radar operates over 140-215 MHz and is essentially a modified version of the multi-channel 3D imaging system reported in [1]. The UHF radar, the focus of this work, is an adaptation of the CReSIS Accumulation Radar, which operates from 600 to 900 MHz [2]. The radar system uses a custom-designed, dual-polarized 4x4 antenna array with increased peak and average transmit power levels, which together provide additional sensitivity with respect to prior system renditions. The UHF radar incorporates a new receiver [3] that uses controlled analog compression via RF limiters to increase the instantaneous dynamic range. We designed the instrument setup to be towed by snowmobiles and operated at nominal speeds of 4 to 8 m/s. The relatively slow motion helps improve SNR through an increase in coherent averaging due to the longer dwell time. Although the focus of the field test is on ground-based work, the electronics are designed to also support airborne operation.

This work describes the design and development of a radar receiver with a large dynamic range by means of carefully designed compression. The receiver is designed for ice sounding applications on the Antarctic and Greenland ice sheets and is designed to be usable over a large frequency range (VHF and UHF) and with multiple analog-to-digital converters with only minor modifications. We present the receiver design, in which we have implemented an RF-power limiting feature so that the output power is monotonically increasing with respect to the input power over a large dynamic range. This allows the receiver to operate in the non-linear region to compress the high-power returns into the dynamic range of the analog to digital converter while still achieving good sensitivity (low noise figure) for low power signals. We discuss design considerations, hardware description, initial lab test results, the architecture of the design and results from recent field deployments. Lastly, we discuss the future work on the decompression mechanism to recover the uncompressed signals.

This paper presents enhanced data products from an airborne ultra-wideband frequency modulated (FM) microwave radar capable of measuring cm-scale of snow cover thickness from high altitude (>1.3 km above ground level, AGL). We use advanced radar hardware combined with a custom synthetic aperture radar (SAR) algorithm applied in post-processing to demonstrate a unique capability of retrieving snow cover depth values down to ~5 cm over a <35-m footprint from altitudes as high as 5,800 m AGL. The improved detection capabilities presented here will be advantageous to maximize mapping coverage while maintaining fine granularity during high altitude surveys.

Array manifold calibration reduces errors in swath maps derived from multichannel synthetic aperture radar (SAR) sounders that apply parametric and subspace based angle estimation techniques in 3-D tomography. Parametric calibration approaches that handle multiple targets simultaneously are attractive in the calibration of SAR sounder manifolds but require analytic expressions of the angle-dependent cross-track transfer function. In this paper, we confirm the utility of computational electromagnetic solvers in predicting airborne ice-penetrating SAR sounder cross-track manifolds, indicating a potential path forward in developing a parametric model of the cross-track manifold.

Increasing global temperatures are adversely affecting the polar ice sheets and contributing to sea level rise. The situation requires constant monitoring and analysis of the change in thickness of snow layers accumulated on top of ice sheets. The monitoring can be performed through radar sensors, but current methods aren't efficient enough to process the radar images since they are noisy, and lack quality annotations, which are required by state-of-the-art deep learning algorithms. In this work, we show that first learning the thickness of snow layers simulated through a physical model helps in building robust deep learning networks. Specifically we show that transfer learning from a network trained with physics-defined labels improves snow layer thickness estimates by 6-29% on the test set.

A chirped-pulse radar ice-sounder, operating at VHF band, was developed in 2019 for the search of the “oldest ice” in East Antarctica. This ground-penetrating radar system has a multiple-in-multiple-out (MIMO) configuration with a total of 8 channels. Each transmitter generates a 170-230 MHz chirp signal with a peak power of 1 kW. To support the half-duplex operation with such a high power, a customized T/R switch is designed and developed. To increase the power-aperture product, the radar is equipped with an 8-element linear antenna array, which has a length of 8 m. In this paper, the radar hardware architecture, high-power T/R switch design, antenna array design and sample field measurement results from Dome C Antarctica are presented.

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid ofin situsnow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.

Manifold calibration improves parametric angle estimator accuracy and resolution performance by reducing the mismatch between the model of an array’s response to directional sources and truth. This article presents nonparametric array manifold calibration for a multichannel ice-penetrating synthetic aperture radar (SAR) sounder used for imaging subglacial morphology with parametric angle estimation in tomography. In this study, we outline a methodology for identifying scatterers at known angles from multichannel imagery by aligning our measurements to an independent fine-resolution satellite-derived digital elevation model of the Arctic that extends beyond the swath of the SAR. We adopt a support statistic based on our partial knowledge of the array response to identify approximately single-source measurements in our scenes. This technique is a departure from traditional approaches to the sounder array characterization problem that require measurements of flat, specular surface reflections from a maneuvering platform. We aggregate observations of single sources and measure manifold corrections relative to our nominal model from the principal eigenvector of our array covariance. We demonstrate the application of three measured manifolds in tomography and compare performance to a nominal manifold that assumes isotropic radiators and known array geometry. We present radar-derived topography of exposed rock and sea ice in the Canadian Arctic Archipelago under the measured and nominal manifolds and report improved vertical accuracy realized with a measured manifold model assumed by the MUltiple SIgnal Classification angle estimators in 3-D image formation.

Understanding the structure of Earth’s polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth’s climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers – the subtle boundaries that indicate where one year’s ice accumulation ended and the next began – is much more challenging because the number of layers varies and the boundaries often merge and split. In this paper, we propose a novel deep neural network for solving a general class of tiered segmentation problems. We then apply it to detecting internal layers in polar ice, evaluating on a large-scale dataset of polar ice radar data with human-labeled annotations as ground truth.

Tree heights are important input for many inventory and ecosystem models. While optical and infrared sensors such as LiDAR are widely used in forest surveys, microwave radar has the unique advantage of being able to operate in bad weather or poor visibility conditions. In this work, we employed a LiDAR combined with a compact ultra-wideband frequency modulated continuous wave (FMCW) radar onboard a Single Otter aircraft to collect data over forested areas in Alaska. While the primary focus of the mission was to map the surface elevation and snow thickness of Alaskan glaciers as a part of NASA Operation IceBridge (OIB), we recorded LiDAR and radar returns during the transit flights to analyze backscattering signatures from tree-covered areas, thereby assessing the potential application of our radar to forest and vegetation remote sensing. We analyzed these measurements and estimated the tree heights along the flight trajectory. The very good agreement between the tree height profiles from the two instruments shows promising results for wide area forestry studies using microwave radar.

Array manifold calibration improves parametric direction of arrival angle estimator performance by reducing mismatch between the model of the array transfer function and truth. This paper demonstrates the estimation and application of a measured manifold for a multichannel ice penetrating synthetic aperture radar (SAR) used to image subglacial features with tomography. We apply SAR tomography to produce digital elevation models of exposed rock and sea ice in the Canadian Arctic Archipelago using both nominal and measured manifolds in angle estimation. We compare reconstructed topography to a fine resolution satellite-derived digital elevation model of the Arctic and demonstrate improvements realized when a measured manifold is used in 3D image formation.

Ice thickness estimation is an important aspect of ice sheet modelling. In this work, we use convolutional neural networks (CNN) with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images captured over northwest Greenland. We experiment with some state-of-the-art CNNs to obtain a mean absolute error of 1.251 pixels of thickness estimation over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.

Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multiclass semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.

We designed a Ka-band prototype radar altimeter operated at a center frequency of 35 GHz with a 6 GHz bandwidth. The instrument was intended for fine-resolution verification and validation of space borne altimetry datasets. We installed it onboard the NASA C-130 aircraft in conjunction with two other wideband microwave instruments and collected airborne altimetry data over Greenland land ice and arctic sea ice during the 2015 NASA Operation IceBridge arctic campaign. Apart from the major application of verification and calibration of satellite-based measurements, data from this instrument can be used to derive snow grain size because of the dominant effect of volume scattering in radar signatures. In this paper, we briefly describe the system design and the installation on the NASA C-130, discuss the observed penetration depths of Ka-band signals into the snowpack, present sample results of optical-equivalent snow grain size estimates from radar measurements over the dry snow zone using a simplified snowpack model. We show that the observed penetration depths and the snow grain size estimates from the airborne Ka-band radar retrievals agree well with the model and in-situ snow-pit measurements.

Differential Interferometric Synthetic Aperture Radar (DIn-SAR) processing techniques applied to ice penetrating radar enable precise measurement of the vertical displacement of englacial layers within an ice sheet. This technique has primarily been applied using ground based ice-penetrating radar due to the ability to achieve a near-zero spatial baseline. We investigate this technique on data from the Multichannel Coherent Radar Depth Sounder (MCoRDS), an airborne ice penetrating radar, and produce initial results from a high accumulation region near Camp Century in northwest Greenland. We estimate the vertical displacement by compensating for the spatial baseline using precise trajectory information and estimates of the cross-track layer slope from direction of arrival analysis. The measurement accuracy is still being investigated.

Airborne sounders with cross-track antenna arrays coherently combine measurements from multiple sensors to isolate nadir echoes from co-range lateral surface clutter. The array size, which is limited by the constraints of the platform, determines the degree to which clutter may be suppressed. Array processing techniques offer strategic advantages for suppressing interference in the cross-track dimension but require a well constrained model of the array's response to directional sources (referred to as the array manifold). We propose and test an empirical array manifold characterization using a fine-resolution digital elevation model of the Arctic and multibeam data collected by a Center for Remote Sensing of Ice Sheets (CReSIS) sensor, the Multichannel Coherent Radar Depth Sounder (MCoRDS), during NASA's Operation Ice-Bridge. The results outline our first efforts to estimate the manifold and evidence improvements observed in combined images.

This paper presents preliminary results using a fully connected neural network (NN) to automatically track the internal layers of snow radar echograms using an iterative “row-block-column” approach. Snow radar images, when accurately tracked, provide relevant information for estimating snow accumulation rates in polar regions which is a key measurement needed to understand and predict the impact of climate warming in Greenland and Antarctica. A multiclass NN was designed and trained with a training set of 121,408 columns of simulated snow radar data and learns to automatically track the internal layers with an accuracy of 92.8%, a RMSE of 0.24 pixels, and with 98% of pixel errors less than or equal to 1 pixel.

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. In this research, we evaluated the performance of synthetically generated snow radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. Our experiments show a very good similarity between real and synthetic snow radar images.

Pragmatic Deep Learning techniques in recent years have greatly influenced our approaches to data analysis. However, in many real-world problems, even when a large dataset is available, Deep Learning methods have shown less success, for the lack of large labeled dataset, presence of noise, or missing data. In this work, our goal is to track internal ice layers in radar images gathered with various sensors in different years. We will show that transfer learning will not generally work well. However, if the Deep Learning model gets trained on noisy images, there would be a significant improvement. Unlike spatial Transfer Learning, our experiments show that temporal Transfer Learning can provide considerably better results.

Artificial intelligence (AI) techniques have displayed impressive success in many practical fields. Deep neural networks (DNNs) owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of large labeled dataset, presence of noise in data, or missing data. In the present work, we intend to examine the application of deep learning methods on radar data gathered from polar regions. Our goal is to track internal ice layers in radar imagery. In such data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we will show that selecting the right model and training the model on the radar imagery from the base, is going to yield far better results. We also discuss another possible learning approach that can save us time for data annotation.

Snow in mountainous areas provides freshwater resources for lower basins and coastal areas. Snow layering at mountain summits contains information about local seasonal snow accumulation and climate history. Knowledge of snow depths is required to estimate the snow water equivalent. However, monitoring spatial distribution and temporal changes in snow depth and accumulation over remote mountains and glaciers in wide areas is challenging because of limited accessibility for in-situ measurements. As a part of NASA Operation IceBridge missions, we took airborne radar measurements of snow over Alaskan mountains, icefields and glaciers in late May of 2018, with a compact frequency-modulated continuous wave radar system, operating from 2 GHz to 8 GHz and installed on a Single Otter aircraft. In this paper, we describe the radar instrument, its installation onto the platform, the data collection and processing activities, and report the major results from these surveys. We observed seasonal snow depth between ~0.3 m to ~15 m for elevations above sea level from ~1726 m to ~3624 m. We successfully mapped snow accumulation layers to depths exceeding ~85 m below the surface at high-elevation summits of Mount Wrangell and Bona. The traced snow depth profiles over glaciers and accumulation layers at mountain summits point to the utility of these data to the study of water resource management, hydrology modelling, and regional climate change.

We present a compact, multi-channel, wideband VHF radar system for fine-resolution measurements of the base and interior of large ice sheets. Data from this radar will be used in the identification of potential drill locations to retrieve ice core samples more than 1 million years old in East Antarctica. The radar is a lightweight instrument equipped with four >1-kW peak power transmit channels, eight independent digital receivers, and an array of high-gain antennas. We developed the system as part of a collaborative effort between the United States of America, Japan and Norway. The instrument was used for surface-based surveys near Dome-Fuji onboard a tracked vehicle during the 2018/2019 Austral Summer, covering 2,000 line-km. This paper presents an overview of the radar system, accompanied by laboratory and field test results that demonstrate the system's ability to map the internal ice sheet structure and basal conditions with outstanding detail.

This paper presents recent updates to the CReSIS radar sensor package and the platforms supporting these sensors. These sensors cover a wide frequency range (14 MHz to 38 GHz). The specific frequency bands are chosen to balance between bandwidth available and signal penetration. The wide frequency range is also used for measuring different phenomenology. CReSIS has integrated these radar systems, including antennas, on a wide variety of fixed wing crewed aircraft, several UAV platforms, and for ground-based applications. The software for processing the radar data is now open source and an overview of the capabilities and how to access and use the software are presented. Finally, example data products which explore the new capabilities of the sensors and platforms are given.

We present improvements to existing implementations of the Viterbi and TRW-S algorithms applied to ice-bottom layer tracking on 2D and 3D radar imagery, respectively. Along with an explanation of our modifications and the reasoning behind them, we present a comparison between our results, the results obtained with the original implementations, and those obtained with other proposed methods of performing ice-bottom layer tracking.

This paper proposes a deep convolutional neural network approach to detect Ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the Ice surface and provide us with valuable information from the underlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a deep convolutional network to produce the images of surface and bottom ice boundary. Our network take advantage of undecimated wavelet transform to provide the higest level of information from radar images, as well as multilayer and multi-scale optimized architecture. In this work, radar images from 2009-2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the art accuracy.

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.

Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change. But converting these noisy readings into useful observations is generally done by hand, which is impractical at a continental scale. In this paper, we propose a computer vision-based technique for extracting 3D ice-bottom surfaces by viewing the task as an inference problem on a probabilistic graphical model. We first generate a seed surface subject to a set of constraints, and then incorporate additional sources of evidence to refine it via discrete energy minimization. We evaluate the performance of the tracking algorithm on 7 topographic sequences (each with over 3000 radar images) collected from the Canadian Arctic Archipelago with respect to human-labeled ground truth.

The basal topography of most of the glaciers that drain the ice caps of the Canadian Arctic Archipelago is largely unknown. To measure the basal topography, NASA Operation IceBridge flew a radar depth sounder in a wide swath mode with three transmit beams to image the glacier beds during three flights over the archipelago in 2014. We describe the measurement setup of the radar system, the algorithms used to process the data to produce a 3D image of the glacier bed, show digital elevation model (DEM) results of the beds, and provide a basic assessment of the tracking algorithm used to extract the DEM.

We have developed an unmanned aerial system consisting of a compact sounding radar operating in the frequency bands of 14 and 35 MHz integrated into a fixed-wing UAV for remote surveys of glaciers and ice-sheets. The system is capable of collecting coherent sounding measurements along multiple parallel tracks. With the use of differential GPS for precise trajectory determination, we demonstrate multipass SAR array processing. The system was recently deployed by CReSIS personnel in the spring of 2016 to survey the Russell glacier in Greenland. This paper reports on the instrumentation including the integration of the radar, antennas, and aircraft; the survey flights in Greenland; and results from measurements collected at 35 MHz.

Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. Ice thickness is a key factor in making predictions about the future of massive ice reservoirs and can be estimated by calculating the exact location of the ice surface and bottom in radar imagery. Identifying the locations of ice boundaries is typically performed manually which is a very time consuming procedure. Here we propose a novel approach which automatically detects the complex topology of ice surface and bottom boundaries based on charged particle concept. Here we first applied anisotropic diffusion to remove the noise and enhance the image. At the second step, we detected the contours in the image based on Coulomb's electrostatic law and the assumption that each pixel is an electrically charged particle. The final ice surface and bottom are detected based on the projection profile of the contours. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and show promising results with respect to hand-labeled ground truth.

In the above paper[1], there is an error inTable I. The value “30” in the bottom row, fifth column should be “350.” The corrected table is provided here.

Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. The melting of polar ice sheets and mountain glaciers has considerable influence on sea level rise in a changing climate. Ice thickness is a key factor in making predictions about the future of massive ice reservoirs. The ice thickness can be estimated by calculating the exact location of the ice surface and subglacial topography beneath the ice in radar imagery. Identifying the locations of ice surface and bottom is typically performed manually, which is a very time-consuming procedure. Here, we propose an approach, which automatically detects ice surface and bottom boundaries using distance-regularized level-set evolution. In this approach, the complex topology of ice surface and bottom boundary layers can be detected simultaneously by evolving an initial curve in the radar imagery. Using a distance-regularized term, the regularity of the level-set function is intrinsically maintained, which solves the reinitialization issues arising from conventional level-set approaches. The results are evaluated on a large data set of airborne radar imagery collected during a NASA IceBridge mission over Antarctica and show promising results with respect to manually picked data.

In this paper, we present the development of a multi-channel VHF/UHF ultra-wideband airborne radar sounder and imager for measurements of polar ice sheets. The radar was developed at the Center for Remote Sensing of Ice Sheets (CReSIS) for operation onboard the German Alfred Wegener Institute (AWI) Basler BT-67 aircraft. The system operates from 150 to 600 MHz corresponding to a vertical resolution of 33 cm in free space. The radar is equipped with three 4-m long 8-element antenna subarrays installed under the fuselage and both wings to support 8 transmit and 24 receive channels. The radar waveform from each transmit channel can be configured individually to enable real-time transmit beamforming for wide-swath ice bed imaging of up to 10 km wide. The radar system was deployed to Greenland in the spring of 2016 as a part of the joint AWI/CReSIS test campaign to conduct measurements over glaciers. Sample radar data from this field campaign are presented to illustrate the capability of the radar.

Snow accumulation to the ice sheet offsets ice losses near the margin, and characterizing ice sheet accumulation rate is necessary for understanding ice sheet mass balance and predicting future sea level rise. Ice penetrating radar systems enable the measurement of ice sheet properties beneath the surface, including internal layering. This study concentrates on mapping the depth of internal layers, and linking the layers to a chronology that allows snow accumulation rates over particular time periods to be determined. This study focuses on one particular ice penetrating radar system: Snow Radar from the Center for Remote Sensing of Ice Sheet (CReSIS). The measurement error from the radar data process has been evaluated and quantified. A difference about 0.017 m caused by manual process in annual accumulation was identified between the radar derived data and true values. The chronology of Snow Radar detected layers is validated to be annual using nearby ice core data and the results of a regional climate model.

Prof. Richard Moore introduced me to FM-CW radars on my first day at the University of Kansas as a graduate student in 1979 and asked me to put together a radar using laboratory test equipment. I put it together, but it did not provide the results we wanted for detecting buried pipes. This was mainly because of the lack of suitable inexpensive RF and digital technologies at that time. Prof. Moore was a strong advocate for using ultra-wideband FM-CW radars. We are able to implement what he taught me because of recent advances in RF microwave and digital technologies, allowing us to develop the ultra-wideband radars Prof. Moore envisioned for remote sensing of snow and ice. We developed ultra-wideband radars for airborne measurements over ice and snow. One of these radars operates over a frequency range of 150-600 MHz for sounding ice sheets, imaging the ice-bed interface, and mapping internal layers in polar firn and ice; additional radars operate over the frequency ranges of 2-8 and 12-18 GHz for airborne measurements of the thickness of snow over sea ice and land and surface elevation measurements, respectively.

Satellite radar altimeter (RA) measurements are important for continued monitoring of rapidly changing polar regions. In 2010, the European Space Agency launched CryoSat-2 carrying SIRAL, a Ku-band RA with objectives of determining the thickness and extent of sea ice and the topography of the ice sheets. One difficulty with Ku-band radar surveys over snow and ice is unknown penetration of RA signal into snow cover. Improving our understanding of the interactions of RA signals with snow and ice is needed to produce accurate elevation products. To this end, we developed a low-power, ultrawideband (12-18 GHz) RA for airborne surveys to provide fine resolution measurements capable of detecting both scattering from the surface and layers within sea ice and ice sheets. These measurements provide a means of identifying the dominant scattering location of lower resolution RA measurements comparable to satellite-based instruments. We generated two products: a full-bandwidth waveform (FBW) to identify scattering targets at fine resolution and a reduced-bandwidth waveform (RBW) to represent conventional RA measurements. Retrackers are used to generate height estimates over various surface conditions for comparisons. Over ice sheets, the leading-edge tracker provided consistent ice-surface elevation measurements between the FBW and RBW results; however, there were significant differences between the results from the centroid tracker. Over sea ice, the location of the dominant return between the results from snow-covered sea ice is highly variable. This paper provides an overview of RA surveys in polar regions, a description of the CReSIS system, and a discussion of the results.

A wideband multi-channel airborne sounding and imaging radar for cryospheric remote sensing applications has been recently developed by the Center for Remote Sensing of Ice Sheets (CReSIS). The radar is designed to measure ice thickness, image the ice-bed interface, and map internal layers in ice sheets and glaciers. This newly-developed radar uses the wide bandwidth for high-resolution imaging and cross-track array processing for suppression of surface clutter. The radar was integrated onto a BT-67 aircraft and completed its first field deployment in Antarctica during the 2013/2014 Austral Summer season. This paper focuses on the development and deployment of the radar. A few sample results from the field survey in Antarctica are also presented to demonstrate the high resolution features of the radar.

This paper presents a radar sensor package specifically developed for wide-coverage sounding and imaging of polar ice sheets from a variety of aircraft. Our instruments address the need for a reliable remote sensing solution well-suited for extensive surveys at low and high altitudes and capable of making measurements with fine spatial and temporal resolution. The sensor package that we are presenting consists of four primary instruments and ancillary systems with all the associated antennas integrated into the aircraft to maintain aerodynamic performance. The instruments operate simultaneously over different frequency bands within the 160 MHz-18 GHz range. The sensor package has allowed us to sound the most challenging areas of the polar ice sheets, ice sheet margins, and outlet glaciers; to map near-surface internal layers with fine resolution; and to detect the snow-air and snow-ice interfaces of snow cover over sea ice to generate estimates of snow thickness. In this paper, we provide a succinct description of each radar and associated antenna structures and present sample results to document their performance. We also give a brief overview of our field measurement programs and demonstrate the unique capability of the sensor package to perform multifrequency coincidental measurements from a single airborne platform. Finally, we illustrate the relevance of using multispectral radar data as a tool to characterize the entire ice column and to reveal important subglacial features.

The near surface layer signatures in polar firn are preserved from the glaciological behaviors of past climate and are important to understanding the rapidly changing polar ice sheets. Identifying and tracing near surface internal layers in snow radar echograms can be used to produce high-resolution accumulation maps. This process is typically performed manually, which requires time-consuming, dense hand-selection and interpolation between sections, for each echogram. We have developed an approach for semi-automatically estimating near surface internal layers and have applied it to snow radar echograms acquired from Antarctica. Our solution utilizes an active contour (“snakes”) model to find high-intensity edges likely to correspond to layer boundaries, while simultaneously imposing constraints on smoothness of layer depth and parallelism among layers.

The National Aeronautics and Space Administration (NASA) initiated a program called Operation IceBridge for monitoring critical parts of Greenland and Antarctica with airborne LIDARs until ICESat-II is launched in 2016. We have been operating radar instrumentation on the NASA DC-8 and P-3 aircraft used for LIDAR measurements over Antarctica and Greenland, respectively. The radar package on both aircraft includes a radar depth sounder/imager operating at the center frequency of 195 MHz. During high-altitude missions flown to perform surface-elevation measurements, we also collected radar depth sounder data. We obtained good ice thickness information and mapped internal layers for both thicker and thinner ice. We successfully sounded 3.2-km-thick low-loss ice with a smooth surface and also sounded about 1-km or less thick shallow ice with a moderately rough surface. The successful sounding required processing of data with an algorithm to obtain 56-dB or lower range sidelobes and array processing with a minimum variance distortionless response algorithm to reduce cross-track surface clutter. In this paper, we provide a brief description of the radar system, discuss range-sidelobe reduction and array processing algorithms, and provide sample results to demonstrate the successful sounding of the ice bottom interface from high altitudes over the Antarctic and Greenland ice sheets.

This work addresses the use of compressive sensing to compress real Synthetic Aperture Radar (SAR) raw data. Due to the low computational resources of the acquisition platforms and the steadily increasing resolution of SAR systems, huge amounts of data are collected and stored, which cannot generally be processed on board and must be transmitted to the ground to be processed and archived. Although compressive sensing (CS) has been proposed and studied by a lot of researchers, almost none of them touches the real application of it. While, in this paper, we test the sparsity of the real SAR raw data (obtained by University of Kansas in Greenland, 2010), compress it using compressive sensing, and then recover the original signal using several CS recovery algorithms (Basis Pursuit, Matching Pursuit and Orthogonal Matching Pursuit), and compare these methods' performance. Simulation results are presented to prove the successful application of CS to real SAR raw data. When proper sparsity matrix is chosen, the real SAR data could be transformed to sparse signal. Using our designed algorithm, the positions and the exact values of the SAR raw data can be almost perfectly recovered with a very low MSE at a compression ratio of 1/8. This is of great significance to help us perform further research in the applications of CS to real SAR raw data.

We developed and deployed, in July 2005, a wideband 8-channel synthetic aperture radar (SAR) at Summit Camp, Greenland (72.5783° N and 38.4596° W). The radar was designed to map internal layers, measure topography, and generate backscatter maps - all in a single pass. Information on ice thickness, bed topography and basal conditions is essential to the refinement of glaciological models of ice sheets, which are used to predict ice-sheet behavior (especially mass balance) and to select deep ice-core sites. This work focuses on the use of fine-resolution 3D imaging algorithms for combining all 8-channels to form cross-track image slices through the ice. As compared with traditional 2D depth sounding, these 3D images allow for the characterization of bed topography with very fine resolution. They also allow for the generation of strip-map SAR images with absolute geocoding without ground control points (these are unavailable at the bottom of the ice), and the ability to analyze the ice-sheet volume in 3D. Topography, backscattering, and 3D ice volume results are illustrated here.

Information on the bedrock topography below the Greenland and Antarctic ice sheets is vital to developing models of future sea-level rise. To measure the topography, advanced data acquisition and processing techniques, including Synthetic Aperture Radar (SAR), are required. This work investigates the optimal beamwidth that would enable SAR processing to maximize the signal to noise ratio of the target. Platform height above the ice surface and bedrock roughness determine the optimal beamwidth. We found that for data collected at a “typical” altitude of 867 m, the optimal beamwidth is about 8°. In the high-altitude case, we found that beamwidth did not have a significant effect on the signal-to-noise ratio. This is probably related to scattering from the ice surface.

We are developing a multifrequency multistatic synthetic aperture radar (SAR) for determining polar ice sheet basal conditions. To obtain data for designing and optimizing radar performance, we performed field measurements with a network-analyzer-based system during the 2003 field season at the North Greenland Ice Core Project camp (75.1 N and 42.3 W). From the measurements, we determine the ice sheet complex transfer function over the frequency range from 110-500 MHz by deconvolving out the system transfer function. Over this frequency range, we observe an increase in total loss of 8/spl plusmn/2.5 dB using a linear regression to the log-scale data. With the ice sheet transfer function and an ice extinction model, we estimate the return loss from the basal surface to be approximately 37 dB. These measurements have broad applicability to interpreting radar-sounding data, which are widely used in glaciological studies of the polar ice sheets. These data have also been used in the link budget for the design considerations of the multifrequency multistatic SAR system.

Ice sheet models are necessary to understand ice sheet dynamics and to predict their behavior. Of the primary inputs to these models, basal conditions are the least understood. By observing the forward and backscatter across a wide frequency range (over two octaves) the basal conditions can be established with a high level of confidence. For this purpose, we developed a multistatic synthetic aperture radar system that operates on three frequency bands (75-85 MHz, 140-160 MHz, and 330-370 MHz). The radar system is designed to use pulse compression techniques and coherent integration to obtain high loop sensitivity (203 dB) necessary to overcome radio frequency losses in ice. The system will be tested at Summit, Greenland (72deg34'N, 38deg29'W) during July 2004