Launching in early 2024, the NASA-ISRO SAR (NISAR) mission will provide global data freely accessible enabling large scale surface deformation monitoring with synthetic aperture radar (SAR) acquired at L-band and S-band radar wavelengths. In preparation for calibration and validation of the NISAR L-band data, the NISAR Solid earth science team is systematically processing over 450 Japan Aerospace Exploration Agency (JAXA) ALOS-2 PalSAR-2 wide-swath (ScanSAR) L-band acquisitions covering the West Coast of the United States for measuring co-seismic, secular and transient displacements. The area spans California, Washington and Oregon.

We document one of several methodologies used to validate the NASA-ISRO Synthetic Aperture Radar (NISAR) mission requirements for solid earth deformation. NISAR’s deformation requirements cover steady-state, coseismic, and transient deformation processes and were designed to confirm that the mission is able to meet its solid earth science goals. We use independent observations of earth surface deformation from continuous Global Navigation Satellite System (GNSS) stations as ground truth for NISAR-observed deformation, and we provide a statistical framework to assess the quality of the associated NISAR data products. Our validation workflows have been developed as Jupyter Notebooks and are publicly available via GitHub/GitLab.

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

This paper presents an image formation algorithm for the focusing of SAR data with space-variant impulse response functions caused by eccentric orbits around small high-curvature surfaces, such as is encountered in stable orbits around Saturn’s moon Enceladus, a potential target for future SAR missions such as the Nightingale mission concept under development at JPL. Due to the extreme geometry, the range history shows a significant dependence on the target’s azimuth position within time scales significantly shorter than the synthetic aperture duration. Therefore, additional steps are needed in order to compensate for this effect and minimize image degradation. In this context, the present contribution evaluates the space variance of the geometry for SAR surveys over Enceladus and proposes a processing flow to account for it. Point target simulations using the proposed processing algorithm are shown to verify the approach.

We study the active Himalayan deformation via examining the interseismic, coseismic, and postseismic phases, exploring the strain on the Main Himalayan Thrust due to Indian and Eurasian Plate convergence. We examine postseismic deformations resulting from the 2015 M7.8 Gorkha Earthquake, employing InSAR data, ALOS-2 L-band, from the JAXA ALOS-2 SAR satellite. InSAR in the Himalayas is challenging due to the extreme topographic relief, steep slopes, vegetation and snow cover, and L-band is an advantage. We focus on the phase unwrapping, topographic, tropospheric, and ionospheric corrections to improve the InSAR time-series. The final interseismic results provide information on the evolution of the strain field. Initial results from the 2015-2019 Gorkha postseismic analysis are consistent with afterslip down-dip from the 2015 rupture and absence of afterslip on shallower megathrust. This approach sets the stage for future analyses of data from the forthcoming NISAR mission, with enhanced ionospheric and tropospheric corrections.

Over the last decades, repeat-pass SAR interferometry (InSAR) for deformation measurement and topographic mapping has revolutionized our understanding of many geophysical processes on Earth. A new mission concept, currently in development at the Jet Propulsion Laboratory (JPL) and Caltech, aims at using orbital repeat-pass InSAR for deformation and topography mapping of Saturn’s ice-covered and geologically active moon Enceladus. In this paper, we present an initial performance assessment of the system and the suggested SAR processing approach, along with simulated InSAR acquisitions using a DLR in-house End-to-End performance simulator.

We use repeat geodetic imaging of two major ice stream systems in Antarctica (Rutford and Evans Ice Streams) to explore the impact of large ocean tides under the Filchner-Ronne Ice Shelf on the dynamics of the ice shelf/ice stream system. We rely on dense time series of radar image acquisitions from multiple vantage points to construct the full 3D surface displacement time history over time scales of months to years. Our observations come from the COSMO-SkyMed constellation. For Evans Ice Stream, we also use observations from Sentinel lab. These time series allow us to evaluate rheological models of ice stream margins and to identify regions of ephemeral sub-shelf grounding. Understanding the sensitivity of such ephemeral pinning points to future thinning of the ice shelf can play an important role in forecasts of the rate of sea level rise over coming decades.

Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth’s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth’s surface. In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth’s surface properties due to a natural disaster. We quantify Earth surface change using time series of interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local behavior of coherence through time before the event. We apply this method to calculate estimates of damage for three earthquakes using multiyear time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with observed damage and quantitative improvement compared to using pre-to co-event coherence loss as a damage proxy.

Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.

In this article, we investigate the link between the closure phase and the observed systematic bias in deformation modeling with multilooked SAR interferometry. Multilooking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in interferometric synthetic aperture radar (InSAR) measurements. However, multilooking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multilooked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multilooked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol Trough, CA, USA. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baselines. We show that these biases can be identified and accounted for.

We study the distribution of slip on faults during earthquakes with integrated analysis of geodetic imaging and seismic data to learn about parameters that control how faults slip and potentially how damaging future earthquakes may be. We mapped complex fault ruptures for a number of large earthquakes in 2015–2020 using analysis of synthetic aperture radar (SAR) data from the Copernicus Sentinel-1A and Sentinel-1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency (JAXA). We used regular SAR interferometry, along-track or multiple-aperture interferometry, and pixel offset tracking to measure surface displacements and combined this with other geodetic and seismic data to infer slip on faults at depth.

In this paper we provide a brief review of the Earth observation requirements for a number key science applications for which spaceborne Synthetic Aperture Radar sensors can contribute with critical measurements. We outline the current state of the science and identify information gaps associated with each application, and subsequently, provide recommendations on how these gaps can be mitigated in the 2020's time-frame by coordination of current and already planned missions, and for the next decade, with a vision for a comprehensive constellation system that would address the outstanding scientific requirements.

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.

The Copernicus Sentinel-1A/B satellites operating at C-band in terrain observation by progressive scans (TOPS) mode bring unprecedented opportunities for measuring large-scale tectonic motions using interferometric synthetic aperture radar (InSAR). Although the ionospheric effects are only about one-sixteenth of those at L-band, the measurement accuracy might still be degraded by long-wavelength signals due to the ionosphere. We implement the range split-spectrum method for correcting ionospheric effects in InSAR with C-band Sentinel-1 TOPS data. We perform InSAR time series analysis and evaluate these ionospheric effects using data acquired on both ascending (dusk-side of the Sentinel-1 dawn-dusk orbit) and descending (dawn-side) tracks over representative midlatitude and low-latitude (geomagnetic latitude) areas. We find that the ionospheric effects are very strong for data acquired at low latitudes on ascending tracks. For other cases, ionospheric effects are not strong or even negligible. The application of the range split-spectrum method, despite some implementation challenges, largely removes ionospheric effects, and thus improves the InSAR time series analysis results.

The InSAR Scientific Computing Environment (ISCE) was first developed under the NASA Advanced Information Systems Technology as a flexible, extensible object-oriented framework for Interferometric Synthetic Aperture Radar (InSAR) processing. The ISCE framework uses Python 3 at the workflow level, controlling modules of compiled code for functional processing, and managing inputs, outputs, and other flow control services. The currently released version, called ISCE 2.1, is distributed to the research community through the Western North America InSAR Consortium under a research license. The ISCE team is working on the next generation of the code in order to prepare for the NASA-ISRO SAR (NISAR) mission operational processing. Innovations in this code include augmentation or conversion of the custom Python framework elements in ISCE with the Pyre framework, new workflows for interferometric and polarimetric stack processing, a more intuitive and graphically based user interface, and flow control for hybrid computing environments including CPU/GPU clusters, logging and error tracking facilities, and new more efficient computational modules that exploit graphical processor units (GPUs) when available. The ISCE 3.0 framework is designed to work in an operational environment as well as on a single user's laptop or compute cluster, with services to discover capabilities and scale computations accordingly.

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD.

Repeat pass interferometric synthetic aperture radar (InSAR) observations may be significantly impacted by the propagation delay of the microwave signal through the ionosphere, which is commonly referred to as ionospheric delay. The dispersive character of the ionosphere at microwave frequencies allows one to estimate the ionospheric delay from InSAR data through a split range-spectrum technique. Here, we extend the existing split range-spectrum technique to InSAR time-series. We present an algorithm for estimating a time-series of ionospheric phase delay that is useful for correcting InSAR time-series of ground surface displacement or for evaluating the spatial and temporal variations of the ionosphere's total electron content (TEC). Experimental results from stacks of L-band SAR data acquired by the ALOS-1 Japanese satellite show significant ionospheric phase delay equivalent to 2 m of the temporal variation of InSAR time-series along 445 km in Chile, a region at low latitudes where large TEC variations are common. The observed delay is significantly smaller, with a maximum of 10 cm over 160 km, in California. The estimation and correction of ionospheric delay reduces the temporal variation of the InSAR time-series to centimeter levels in Chile. The ionospheric delay correction of the InSAR time-series reveals earthquake-induced ground displacement, which otherwise could not be detected. A comparison with independent GPS time-series demonstrates an order of magnitude reduction in the root mean square difference between GPS and InSAR after correcting for ionospheric delay. The results show that the presented algorithm significantly improves the accuracy of InSAR time-series and should become a routine component of InSAR time-series analysis.

We present a method for inferring time-dependent three-component surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. Displacements are parameterized in time with a dictionary of displacement functions. The algorithm extends an earlier single-component (i.e., single line of sight) framework for time-series analysis to three spatial dimensions using combinations of multitemporal, multigeometry interferometic synthetic aperture radar (InSAR) and/or pixel offset (PO) maps. We demonstrate this method with a set of 101 pairs of azimuth and range PO maps generated for a portion of the Rutford Ice Stream, West Antarctica, derived from data collected by the COSMO-SkyMed satellite constellation. We compare our results with previously published InSAR mean velocity fields and selected GPS time series and show that our resulting three-component surface displacements resolve both secular motion and tidal variability.

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agency's (ASI's) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agency's (JAXA's) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITAR's (United Nations Institute for Training and Research's) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobe's WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

Mechanical properties of glacier beds impose fundamental constraints on glacier flow across a wide range of timescales [1]. Despite their importance in governing glacier dynamics, basal mechanics are not well understood, particularly where glaciers are underlain by deformable till [2]. While some till samples have been retrieved from beneath several glaciers and tested in laboratories in order to ascertain till rheology [3, 4], limitations on clast sizes imposed by apparatus dimensions and the difficulty of understanding and reproducing subglacial environments in the lab necessitate observations of the mechanical properties of in situ tills [5]. Such observations are sparse, owing to the inherent difficulty in attaining them, and this observational paucity has helped foment persistent uncertainties concerning the proper rheology of subglacial till and the rheological dependence on mechanical, thermal, and hydrological forcing [1, 2].