Showing papers by "Howard A. Zebker published in 2021"

Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

Abstract: Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Abstract: Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross-comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.

A New Decorrelation Phase Covariance Model for Noise Reduction in Unwrapped Interferometric Phase Stacks

Abstract: The accuracy of geophysical parameter estimation made with interferometric synthetic aperture radar (InSAR) time-series techniques can be improved with rapidly increasing available data volumes and with the development of noise covariance matrices applicable to joint analysis of networks of interferograms. In this article, we present a new decorrelation phase covariance model and discuss its role in noise reduction in unwrapped interferometric phase stacks. We demonstrate with an example in which we average unwrapped interferogram phase stacks that span over a transient event how a noise covariance model can aid in noise reduction. Our model suggests that, for rapidly decorrelating surfaces (i.e., surfaces with much shorter correlation time than SAR acquisition intervals), it is preferable to incorporate all available interferograms from long observation windows. For slowly decorrelating surfaces (i.e., surfaces with longer correlation time than SAR acquisition intervals), our model suggests that a small subset of interferometric pairs is sufficient. We validate our model and three existing models of decorrelation phase covariance matrices in both Cascadia, a region with heavy vegetation cover, and Death Valley, a desert region with C-band Sentinel-1 A observations. Our proposed model matches observations with the smallest average discrepancy between theory and observations.

Validation of Permafrost Active Layer Estimates from Airborne SAR Observations

Abstract: In permafrost regions, active layer thickness (ALT) observations measure the effects of climate change and predict hydrologic and elemental cycling. Often, ALT is measured through direct ground-based measurements. Recently, synthetic aperture radar (SAR) measurements from airborne platforms have emerged as a method for observing seasonal thaw subsidence, soil moisture, and ALT in permafrost regions. This study validates airborne SAR-derived ALT estimates in three regions of Alaska, USA using calibrated ground penetrating radar (GPR) geophysical data. The remotely sensed ALT estimates matched the field observations within uncertainty for 79% of locations. The average uncertainty for the GPR-derived ALT validation dataset was 0.14 m while the average uncertainty for the SAR-derived ALT in pixels coincident with GPR data was 0.19 m. In the region near Utqiaġvik, the remotely sensed ALT appeared slightly larger than field observations while in the Yukon-Kuskokwim Delta region, the remotely sensed ALT appeared slightly smaller than field observations. In the northern foothills of the Brooks Range, near Toolik Lake, there was minimal bias between the field data and remotely sensed estimates. These findings suggest that airborne SAR-derived ALT estimates compare well with in situ probing and GPR, making SAR an effective tool to monitor permafrost measurements.

A Signal Model for PRF Dithering in Wide-Swath, Fine-Resolution InSAR

Abstract: Wide-swath radar imaging requires that the time interval to collect each radar pulse echo is large and can often exceed the interpulse period. As it is difficult to both transmit and receive from the same antenna simultaneously, there will be “blind ranges” when the receive and transmit times overlap. This leads to gaps in the radar echo and thus degradation of system performance. Today, most wide-swath systems address this by segmenting the swath in range, such as in the ScanSAR or Terrain Observation with Progressive Scan (TOPS) mode operation, so that each subswath is within the range ambiguity limit. Some groups have started experimenting with the variations in the sweepSAR technology, in which the receive antenna tracks the radar echo across the swath, realizing a complete wide-swath range scan but leading to the echo gaps. Here, we look at minimizing the effect of blind ranges by varying the radar pulse-repetition frequency (PRF) and interpolating across the gaps to preserve azimuth signal continuity. If the pulse times are selected properly, the main effect is to raise the noise floor of the echoes. Geocoded magnitude images, interferograms and correlation images, and deformation time series show strong robustness with respect to dithering, demonstrating that choosing essentially random PRFs allows for accurate generation of SAR and Interferometric Synthetic Aperture Radar (InSAR) data products while retaining wide-swath, fine-resolution coverage.

Adaptation of a Range-Doppler Algorithm to Multistatic Signals from Ultrasound Arrays

Abstract: Frequency-domain beamforming has become increasingly popular for fast processing of large synthetic aperture data in medical ultrasound. Here, we modify the Range Doppler Algorithm (RDA) to focus ultrasound signals from multistatic acquisitions. RDA, which was first proposed for fast beamforming of monostatic data in radar remote sensing, is suitable for fast processing of large datasets because all operations are done in one dimension at a time, allowing for an efficient and intuitive implementation. We demonstrate through simulation that multistatic RDA achieves similar image quality as traditionally used, multistatic delay-and-sum (DAS), while increasing the reconstruction speed by approximately a factor of three. We also show that the RDA and DAS images from the multistatic acquisition show reduced sidelobe levels compared to their counterparts from the monostatic acquisition. Demonstrated version of the multistatic RDA might be applicable beyond ultrasound medical imaging, such as for processing of synthetic aperture radar (SAR) data from satellite constellations.

Maximum Temporal Baseline for InSAR Time Series

Abstract: InSAR time series analysis is a technique for observing changes in range or phase of a radar resolution element from a sequence of radar acquisitions. General methods of retrieving temporal signals, such as small baseline subset analysis (SBAS) [1], allow for redundant measurement in order to reduce statistical variations from system noises. Recent work using significant redundancy shows that spatial and temporal aliasing leads to additional errors in measurement [2]. Here we show that it is necessary to optimally balance the amount of redundancy with he amount of aliasing to obtain the highest quality estimates of deformation, and further that a spatially variable level of averaging interferograms is needed for many analyses. Development of a practical algorithm remains before we are operationally able to fully exploit the capabilities of an InSAR system.

Nisar Requirements and Validation Approach for Solid Earth Science

Abstract: The joint NASA/ISRO SAR (NISAR) satellite mission is anticipated to provide routine L-band coverage of most of the Earth's land surface every 12-days for both ascending and descending orbits. In terms of impact on solid earth science (SES), the primary measurement will be Interferometric SAR (InSAR) observations of ground deformation in two satellite line-of-sight (LOS) directions. Key observation characteristics include acquisitions with small interferometric baselines to maximize interferometric coherence and decrease sensitivity to topography, wide bandwidth allowing for split-band processing to model out the impacts of the ionosphere, and joint L- and S-band observations in selected regions. We describe here the key measurement requirements for solid earth science, as well as our approach to validating these requirements once the mission is underway.

Habitability, Geodynamics, and the Case for Venus

Abstract: Primary author: Suzanne Smrekar Jet Propulsion Laboratory/California Institute of Technology, Pasadena CA ; Co-authors: Jeff Andrews-Hanna (U.AZ), Doris Breuer (DLR Berlin), Paul Byrne (NCSU), Debra Buczkowski (JHU/APL), Bruce Campbell (Nat. Air Space Museum), A. Davaille (CNRS/U. Paris-Saclay), Darby Dyar (Mt. Holyoke/PSI), G. Di Achille (INAF/Astro Obs. Teramo), Caleb Fassett (Marshall), Martha Gilmore (Wesleyan), Robert Grimm (SWRI), Jorn Helbert (DLR Berlin), Scott Hensley (JPL), Robert Herrick (U. Alaska), Luciano Iess (U.Roma), Lauren Jozwiak (JHU/APL), Tiffany Katiaria (JPL), Marco Mastrogiuseppe (U. Roma), Erwan Mazarico (Goddard), Nils Mueller (DLR Berlin), Daniel Nunes (JPL), Joseph O'Rourke (ASU); Patrick McGovern (LPI), Maria Raguso (Caltech), Joann Stock (Caltech), Constantine Tsang (SwRI), Thomas Widemann (Obs. Paris), Jennifer Whitten (Tulane), Thomas Widemann (LESIA), Howard Zebker (Stanford)

Performance of Correlation-Based Imaging with a Bistatic Configuration Toward Resilient Multistatic Imaging of Space Debris

Abstract: Radar imaging using the cross-correlation of receiver signals possesses several benefits over traditional matched-filter techniques that make it more suitable for imaging fast-moving objects without well-defined flight tracks, such as fast-moving space debris. Correlation-based imaging is implemented with a network of receivers, and requires no knowledge of the imaging pulse or the emitter and receiver locations and is able to compensate for unknown linear and rotational motion with a resolution on the order of the imaging wavelength, comparable to the performance of matched-filter imaging. Here, we compare the performance of a simplified bistatic configuration for correlation-based imaging and the traditional matched-filter method in a representative case study experiment, where a small aircraft is used as a target. We find that the bistatic case is effective even when there are motions that exceed the transmitter wavelength and is more resilient to unaccounted random motion than the monostatic case. However, as expected for a two-receiver configuration, the resolution is lower than the matched-filtering method even in the ideal case. Finally, we comment on extensions as well as implications for multistatic configurations.

Permafrost Dynamics Observatory: Retrieval of Active Layer Thickness and Soil Moisture from Airborne Insar and Polsar Data

Abstract: The Permafrost Dynamics Observatory (PDO) combines L-band interferometric synthetic aperture radar (InSAR) and P-band polarimetric synthetic aperture radar (PolSAR) to simultaneously estimate the seasonal thaw depth and soil moisture profile of the active layer in permafrost regions. L-band InSAR can measure seasonal subsidence due to thawing of the active layer and P-band PolSAR backscatter is sensitive to subsurface soil moisture. A joint retrieval scheme is developed as both subsidence and soil moisture are essential to accurate active layer thickness (ALT) estimation. The PDO joint retrieval has been applied to airborne L- and P-band SAR data acquired over Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. In this paper, we describe the forward models and joint inversion used in the PDO retrievals and compare the results with in-situ ALT and soil moisture data estimated from ground-penetrating radar (GPR).

Aliasing in InSAR and SBAS Time Series

Abstract: Interferometric synthetic aperture radar (InSAR) is a well-known imaging technique used by geophysicists to measure deformation of the Earth's surface over time. Accurate measurements of these displacements are crucial to correctly interpret subsurface processes. We show that when a closed loop of displacement gradients exceeding π radians occurs in a wrapped interferogram, the unwrapped solution will systematically underestimate the total displacement due to aliasing, even in the absence of noise. Common InSAR practices such as spatial filtering and averaging decrease resolution and thus increase the risk of aliasing. For time-varying processes, interferograms formed over longer time spans are more likely to under-sample the true displacement. Synthetic and real time series analyses using small baseline and subset (SBAS) [1] techniques show that the inclusion of aliased interferograms results in systematic and significant errors. These examples suggest that adaptive filters and varying the subset of interferograms used in SBAS throughout the scene will be important improvements to InSAR analyses.

Non-Gaussian Extensions for the Detection of Persistent Scatterers: Addressing the Limitations of Gaussian Models for InSAR Imagery

Abstract: It is well-known that the backscatter of high-resolution Synthetic Aperture Radar (SAR) imagery is non-Gaussian in nature. As a result, corresponding heavy-tailed models have been successfully incorporated for the design of improved SAR target detectors. However, Gaussian-based detectors are largely still applied for selection of persistent scatterers (PS) in Interferometric Synthetic Aperture Radar (InSAR) imagery, and implications for the performance of PS techniques have not been well-studied. Here, we extend an existing Gaussian model for PS to incorporate non-Gaussian behavior. We then implement the model for PS detection and compare its performance to its Gaussian counterpart, finding that the non-Gaussian model finds a slightly denser network of PS. Further work will focus on analyzing the characteristics of this disparity, including its relationship with terrain and system parameters such as wavelength and bandwidth, and compare the estimated deformation from the non-Gaussian detector compared to an existing Gaussian-based model. Understanding the limitations of Gaussian models will inform the design of improved PS detectors to produce more complete deformation maps and enable the broader application of InSAR for challenging applications, such as observing small strain rates in natural terrain.