Uninhabited Aerial Vehicle Synthetic Aperture Radar Research Articles

Tensorial Independent Component Analysis-Based Feature Extraction for Polarimetric SAR Data Classification

For polarimetric synthetic aperture radar (PolSAR) data, various polarimetric signatures can be obtained by target decomposition techniques, which are of great help for characterizing the land cover. It is straightforward to combine these polarimetric features together and formulate them as a third-order polarimetric feature tensor. However, how to make full use of the abundant information provided by these polarimetric features remains a challenge. A feasible solution is applying feature extraction (FE) techniques on the high-dimensional polarimetric manifold to obtain a lower dimensional intrinsic feature set. Common FE methods, such as principal component analysis (PCA), independent component analysis (ICA), etc., use matrix linear algebra and require rearranging the original tensor into a matrix. This leads to the loss of the spatial information of the PolSAR data. In this paper, to jointly take advantage of the spatial and feature information, a novel FE scheme incorporating ICA with the tensor decomposition techniques is proposed. After applying the proposed FE method on the third-order polarimetric feature tensor, each PolSAR image pixel is represented by a low-dimensional intrinsic feature vector. Furthermore, these feature vectors are fed to the $k$ -nearest neighbor (KNN) classifier and support-vector-machine classifier for supervised classification. Simulated data, together with two measured data sets, i.e., Flevoland of Airborne Synthetic Aperture Radar (AIRSAR) and Quebec City of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), are utilized to evaluate the performance of the proposed method. For comparison purpose, several classical and advanced FE methods, such as PCA, ICA, Laplacian eigenmaps, and $\hbox{LRTA}_{\rm dr}-(K_{1},K_{2},p)$ , are also applied. The experimental results demonstrate the superiority of the proposed FE method in three folds: 1) The extracted features by the proposed method are more discriminative, characterized by the high separability in the scatterplots; 2) the classification accuracy is improved as much as approximately 7% compared with the complex Wishart classifier; and 3) the proposed method is computational efficient and has fast convergence.

Early melt season velocity fields of Langjökull and Hofsjökull, central Iceland

AbstractWe infer the horizontal velocity fields of the ice caps Langjökull and Hofsjökull, central Iceland, using repeat-pass interferometric synthetic aperture radar (InSAR). NASA’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR) acquired airborne InSAR data from multiple vantage points during the early melt season in June 2012. We develop a Bayesian approach for inferring three-dimensional velocity fields from multiple InSAR acquisitions. The horizontal components generally agree with available GPS measurements wherever ice motion is well constrained by InSAR observations. We provide evidence that changes in volumetric moisture content near the glacier surface induce phase offsets that obfuscate the vertical component of the surface velocity fields, an effect that could manifest itself on any glacier that experiences surface melt. Spatial patterns in the InSAR-derived horizontal speeds are broadly consistent with the results of a simple viscous flow model, and the directionality of the InSAR-derived horizontal flow field is nearly everywhere consistent with the ice surface gradient. Significant differences between the InSAR-derived horizontal speed and the speed predicted by the viscous flow model suggest that basal slip accounts for more than half the observed outlet glacier flow.

Web Services for Dynamic Coloring of UAVSAR Images

QuakeSim has implemented a service-based Geographic Information System to enable users to access large amounts of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data through an online interface. The QuakeSim Interferometric Synthetic Aperture Radar (InSAR) profile tool calculates radar-observed displacement (from an unwrapped interferogram product) along user-specified lines. Pre-rendered thumbnails with InSAR fringe patterns are used to display interferogram and unwrapped phase images on a Google Map in the InSAR profile tool. One challenge with this tool lies in the user visually identifying regions of interest when drawing the profile line. This requires that the user correctly interpret the InSAR imagery, which currently uses fringe patterns. The mapping between pixel color and pixel value is not a one-to-one relationship from the InSAR fringe pattern, and it causes difficulty in understanding general displacement information for QuakeSim users. The goal of this work is to generate color maps that directly reflect the pixel values (displacement) as an addition to the pre-rendered images. Because of an extremely uneven distribution of pixel values on an InSAR image, a histogram-based, nonlinear color template generation algorithm is currently under development. A web service enables on-the-fly coloring of UAVSAR images with dynamically generated color templates.

An Unsupervised Scattering Mechanism Classification Method for PolSAR Images

This letter concentrates on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the combinations of dominant and secondary scattering mechanisms. With three metrics extracted from the observed coherency matrix, an unsupervised classifier is proposed to classify PolSAR pixels into eight combinations of surface scattering, double-bounce scattering, and volume scattering. When applying the proposed method to simulated data, the Kappa coefficient is 0.891. It effectively classifies the dominant mechanism, and the Kappa coefficient is 0.127 higher than that of the H/ $\alpha$ method. Experiment using uninhabited aerial vehicle SAR data shows that the proposed method is able to identify secondary mechanism in forests and urban areas. This method is not only a good classifier free of specific polarimetric decomposition but also can serve as a preclassification step of sophisticated classification scheme.

Marsh Dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data

Landsat Thematic Mapper and Satellite Pour l'Observation de la Terre (SPOT) satellite based optical sensors, NASA Uninhabited Aerial Vehicle synthetic aperture radar (UAVSAR) polarimetric SAR (PolSAR), and field data captured the occurrence and the recovery of an undetected dieback that occurred between the summers of 2010, 2011, and 2012 in the Spartina alterniflora marshes of coastal Louisiana. Field measurements recorded the dramatic biomass decrease from 2010 to 2011 and a biomass recovery in 2012 dominated by a decrease of live biomass, and the loss of marsh as part of the dieback event. Based on an established relationship, the near-infrared/red vegetation index (VI) and site-specific measurements delineated a contiguous expanse of marsh dieback encompassing 6649.9ha of 18,292.3ha of S. alterniflora marshes within the study region. PolSAR data were transformed to variables used in biophysical mapping, and of this variable suite, the cross-polarization HV (horizontal send and vertical receive) backscatter was the best single indicator of marsh dieback and recovery. HV backscatter exhibited substantial and significant changes over the dieback and recovery period, tracked measured biomass changes, and significantly correlated with the live/dead biomass ratio. Within the context of regional trends, both HV and VI indicators started higher in pre-dieback marshes and exhibited substantially and statistically higher variability from year to year than that exhibited in the non-dieback marshes. That distinct difference allowed the capturing of the S. alterniflora marsh dieback and recovery; however, these changes were incorporated in a regional trend exhibiting similar but more subtle biomass composition changes.

Advantages to Geoscience and Disaster Response from QuakeSim Implementation of Interferometric Radar Maps in a GIS Database System

High-resolution maps of earth surface deformation are available in public archives for scientific interpretation, but are primarily available as bulky downloads on the internet. The NASA uninhabited aerial vehicle synthetic aperture radar (UAVSAR) archive of airborne radar interferograms delivers very high resolution images (approximately seven meter pixels) making remote handling of the files that much more pressing. Data exploration requiring data selection and exploratory analysis has been tedious. QuakeSim has implemented an archive of UAVSAR data in a web service and browser system based on GeoServer (http://geoserver.org). This supports a variety of services that supply consistent maps, raster image data and geographic information systems (GIS) objects including standard earthquake faults. Browsing the database is supported by initially displaying GIS-referenced thumbnail images of the radar displacement maps. Access is also provided to image metadata and links for full file downloads. One of the most widely used features is the QuakeSim line-of-sight profile tool, which calculates the radar-observed displacement (from an unwrapped interferogram product) along a line specified through a web browser. Displacement values along a profile are updated to a plot on the screen as the user interactively redefines the endpoints of the line and the sampling density. The profile and also a plot of the ground height are available as CSV (text) files for further examination, without any need to download the full radar file. Additional tools allow the user to select a polygon overlapping the radar displacement image, specify a downsampling rate and extract a modest sized grid of observations for display or for inversion, for example, the QuakeSim simplex inversion tool which estimates a consistent fault geometry and slip model.

Co-polarization channel imbalance determination by the use of bare soil

This paper describes a novel technique which determines the co-polarization channel imbalance by the use of natural bare soil, instead of a trihedral corner reflector (CR). In polarimetric synthetic aperture radar (PolSAR) remote sensing, the polarimetric calibration (PolCAL) is the key technique in quantitative earth parameter measurement. In general, the current PolCAL process can be separated into two parts. The first part tries to estimate the crosstalk and the cross-polarization (x-pol) channel imbalance components by the reflection symmetry and the reciprocity properties, without a CR. Then, at least one trihedral CR is required to determine the co-polarization (co-pol) channel imbalance; however, it is not always possible to deploy a CR in difficult terrain such as desert. In this paper, we utilize bare soil as a stable reference target, and four common natural constraints of bare soil are evaluated to determine the co-pol channel imbalance, without the use of a CR. It should be mentioned that we do not propose to replace the CR by a natural target, but we utilize the natural target to enhance the PolCAL accuracy when a CR is missing. The four constraints are: (1) the consistency of the polarimetric orientation angle (CPOA) between the PolSAR POA and the digital elevation model (DEM) derived POA; (2) the unitary zero POA (UZPOA) of a flat ground surface; (3) the zero helix (ZHEX) component of the ground surface; and (4) the unitary version of the previous zero helix (UZHEX). In the theoretical part of this paper, we demonstrate that the forth constraint is the most suitable in different scenes. We then propose a multi-scale algorithm to further improve the robustness of the co-pol channel imbalance determination. In the experimental part, we apply our new methods to simulated airborne SAR (AIRSAR) and real uninhabited aerial vehicle SAR (UAVSAR) data. Without the use of any CR, the recovered results show that the estimated amplitude and phase error of the co-pol channel imbalance are less than 0.5dB and 5°, respectively.

Determining the oil weighting factor of oil-water emulsion based on the fractional Weierstrass scattering model

The formation of oil-water emulsion often occurs when oil is spilled into the ocean. Oil weighting factor of oil-water emulsion is one of the most important parameters for emergent oil-spill microwave monitoring. A new method is proposed here to evaluate the oil weighting factor based on fractional Weierstrass scattering model. By using the proposed method, we analyze the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band fully polarimetric data acquired during 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico. The result shows that our method performs well in evaluating oil weighting factor of oil-covered area.

UAVSAR observations of triggered slip on the Imperial, Superstition Hills, and East Elmore Ranch Faults associated with the 2010 M 7.2 El Mayor‐Cucapah earthquake

AbstractThe 4 April 2010 M 7.2 El Mayor‐Cucapah earthquake that occurred in Baja California, Mexico and terminated near the U.S. Mexican border caused slip on the Imperial, Superstition Hills, and East Elmore Ranch Faults. The pattern of slip was observed using radar interferometry from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument collected on 20–21 October 2009 and 12–13 April 2010. Right‐lateral slip of 36 ± 9 and 14 ± 2 mm occurred on the Imperial and Superstition Hills Faults, respectively. Left‐lateral slip of 9 ± 2 mm occurred on the East Elmore Ranch Fault. The widths of the zones of displacement increase northward suggesting successively more buried fault motion to the north. The observations show a decreasing pattern of slip northward on a series of faults in the Salton Trough stepping between the El Mayor‐Cucapah rupture and San Andreas Fault. Most of the motion occurred at the time of the M 7.2 earthquake and the UAVSAR observations are consistent with field, creepmeter, GPS, and Envisat observations. An additional 28 ± 1 mm of slip at the southern end of the Imperial Fault over a <1 km wide zone was observed over a 1 day span a week after the earthquake suggesting that the fault continued to slip at depth following the mainshock. The total moment release on the three faults is 2.3 × 1023−1.2 × 1024 dyne cm equivalent to a moment magnitude release of 4.9–5.3, assuming shallow slip depths ranging from 1 to 5 km.

Bayou Corne, Louisiana, sinkhole: Precursory deformation measured by radar interferometry

Catastrophic sinkholes are formed through the collapse of natural or human-made subterranean caverns, and are common in areas with evaporite and carbonate rock. Despite their danger, advance warning of these events is rare. We report a measurement of precursory surface deformation of as much as 260 mm, derived with interferometric synthetic aperture radar (InSAR) and evident over a month before surface collapse, at the site of the Bayou Corne, Louisiana (USA) sinkhole that formed in August 2012. Data collected by the airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument were used for the study. Analysis of data acquired from two flight tracks with near-opposing imaging geometries reveal a deformation pattern consistent with compressive loading at the surface due to loss of support from a subterranean cavity collapse related to Texas Brine Oxy Geismar Well #3. The precursor deformation was nearly entirely horizontal, i.e., oriented along the surface, and manifested as movement of surface material toward the location where the sinkhole later formed. The sinkhole formed in the area with the largest gradient in surface strain, but did not cover the full extent of the precursory deformation detected with radar. This work suggests that InSAR data collected operationally for hazard monitoring could, in some cases, identify sinkhole development before surface collapse, and decrease subsequent danger to people and property.

Incidence Angle Normalization of Radar Backscatter Data

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40 °. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 °, and UAVSAR collects data over a wide range of incidence angles (25 °-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40° incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.

Using InSAR Coherence to Map Stand Age in a Boreal Forest

The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA’s L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R2 = 0.75, RMSE = 5.3) as compared to 1 ha (R2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

Polarimetric Analysis of Backscatter From the Deepwater Horizon Oil Spill Using L-Band Synthetic Aperture Radar

We analyze the fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired on June 23, 2010, from two adjacent, overlapping flight tracks that imaged the main oil slick near the Deepwater Horizon (DWH) rig site in the Gulf of Mexico. Our results show that radar backscatter from both clean water and oil in the slick is predominantly from a single surface scatterer, consistent with the tilted Bragg scattering mechanism, across the range of incidence angles from 26° to 60°. We show that the change of backscatter over the main slick is due both to a damping of the ocean wave spectral components by the oil and an effective reduction of the dielectric constant resulting from a mixture of 65-90% oil with water in the surface layer. This shows that synthetic aperture radar can be used to measure the oil volumetric concentration in a thick slick. Using the H/A/α parameters, we show that surface scattering is dominant for oil and water whenever the data are above the noise floor and that the entropy (H) and α parameters for the DWH slick are comparable to those from the clean water. The anisotropy, A, parameter shows substantial variation across the oil slick and a significant range-dependent signal whenever the backscatter in all channels is above the instrument noise floor. For slick detection, we find the most reliable indicator to be the major eigenvalue of the coherency matrix, which is approximately equal to the total backscatter power for both oil in the slick and clean sea water.

Determining the mixing of oil and sea water using polarimetric synthetic aperture radar

Knowledge of the characteristics of spilled oil in the ocean is important for cleanup operations, predictions of the impact on wildlife, and studies of the nature of the ocean surface and currents. Herein I discuss a method for evaluating the characteristics of oil in a marine environment using synthetic aperture radar (SAR) and present a new, simple classification, called the oil/water mixing index (Mdex), to quickly assess the results. I link the Mdex results to the Bonn Agreement for Oil Appearance Codes (BAOAC) for aerial observers and demonstrate the Mdex on Uninhabited Aerial Vehicle SAR (UAVSAR) data collected June 23, 2010 over the former site of the Deepwater Horizon (DWH) drilling rig. The Mdex map shows a more heterogeneous oil swath than do radar backscatter images and features within the oil are consistent with features present in previously published, near‐coincident optical imagery. The Mdex results indicate that most of the oil near the DWH was mixed with sea water to a minimum depth of a few millimeters, though some areas containing relatively thin films are observed.

Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests

In this paper, we study the radar retrieval of soil moisture as well as canopy parameters in a range of boreal forests. The retrieval is formulated as an optimization problem where the difference between data and prediction of a forward scattering model is minimized. The forward model is a discrete scatterer radar model, and the optimization algorithm is a global optimization scheme known as simulated annealing. The inversion method is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknown vegetation parameters. The inversion algorithm is then validated using the data acquired with the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in June 2010 in central Canada boreal forests in support of the prelaunch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission. The inversion results for synthetic data show that the absolute retrieval error in soil moisture and relative retrieval error in canopy height are small, while the relative output error in trunk density could be large. The inversion results for actual field data show a great accuracy in soil moisture retrieval for Old Jack Pine and Young Jack Pine forests but show large retrieval errors for many of the radar pixels in the Old Black Spruce site. This paper shows that L-band radar is capable of retrieving surface soil moisture in forests with a high biomass where the forest structure allows soil moisture information to be carried by scattering mechanisms.

An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes

We present an empirical assessment of the impact of temporal decorrelation on interferometric coherence measured over a forested landscape. A series of repeat-pass interferometric radar images with a zero spatial baseline were collected with UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), a fully polarimetric airborne L-band radar system. The dataset provided temporal separations of 45 minutes, 2, 7 and 9 days. Coincident airborne lidar and weather data were collected. We theoretically demonstrate that UAVSAR measurement accuracy enables accurate quantification of temporal decorrelation. Data analysis revealed precipitation events to be the main driver of temporal decorrelation over the acquisition period. The experiment also shows temporal decorrelation increases with canopy height, and this pattern was found consistent across forest types and polarization.

Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes.

Oil spill detection with fully polarimetric UAVSAR data

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle – synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.

The Glacier and Land Ice Surface Topography Interferometer: An Airborne Proof-of-Concept Demonstration of High-Precision Ka-Band Single-Pass Elevation Mapping

As part of the NASA International Polar Year activities, a Ka-band cross-track interferometric synthetic aperture radar (SAR) recently demonstrated high-precision elevation swath mapping capability. This proof-of-concept instrument was achieved by interfacing two Ka-band slotted-waveguide antennas in a cross-track geometry and Ka-band electronics with the Jet Propulsion Laboratory's L-band uninhabited aerial vehicle SAR. Deployed on the NASA Gulfstream III, initial engineering flights in March and April 2009 marked the first airborne demonstration of single-pass cross-track interferometry at Ka-band. Results of a preliminary interferometric assessment indicate height precisions that, for a 3 m × 3 m posting, range from 30 cm in the near range to 3 m in the far range and greater than 5 km of swath over the urban areas imaged. The engineering flights were followed by a comprehensive campaign to Greenland in May 2009 for ice-surface topography mapping assessment. Toward that end, coordinated flights with the NASA Wallops Airborne Topographic Mapper lidar were conducted in addition to establishing ground calibration sites at both the Summit Station of the National Science Foundation and the Swiss Camp of the Cooperative Institute for Research in the Environmental Sciences. Comparisons of the radar-derived elevation measurements with both in situ and lidar data are planned for a subsequent paper; however, at this stage, a single data example over rugged ice cover produced a swath up to 7 km with the desired height precision as estimated from interferometric correlation data. While a systematic calibration, including assessment and modeling of biases, due to penetration of the electromagnetic waves into the snow cover has not yet been addressed, these initial results indicate that we will exceed our system requirements.