There is a growing trend towards making Synthetic Aperture Radar (SAR), SAR Interferometry (InSAR), and their applications more accessible to end users. Directly delivering geocoded, co-registered, and flattened SLC data (GSLC) eliminates the need for the complex geocoding and co-registration procedures which require professional domain knowledge and software. GSLC products dramatically simplify InSAR processing flows, making InSAR products easily available to a wide range of users. However, challenges still exist for SAR/InSAR analysis using GSLC datasets. In this paper, we analyze the feasibility of using GSLC for deformation measurements based on offset tracking, both in theory and practice. We find that correct GSLC offset tracking requires the input GSLCs to be 1) unflattened, 2) deramped, and 3) adequately sampled. We also show that the direct result from GSLC offset tracking is a projection of displacement in the slant range and azimuth directions. We can transform the offset measurement from GSLCs to the deformation field, but the current transformation relation is not precise enough. This research may help deepen the understanding of GSLCs and their applications.

NASA-ISRO Synthetic Aperture Radar (NISAR) mission with a near-global coverage of land and cryosphere regions with 12 days repeat will operationally produce level-1 and level-2 science products from L-band radar by the NASA data system at Jet Propulsion Laboratory. The products will be available to the users through the NASA’s Distributed Active Archive Center (DAAC) at Alaska Satellite Facility (ASF). In this paper we present the algorithms to process, calibrate and assess the quality of the NISAR products after launch. We simulate NISAR raw data from different science modes and with NISAR L-band configurations (such as left looking, squinted beam and dithered data and the same transmit chirp and receive configuration as the L-band instrument) and process them through the NISAR standard processor to from level-1 and level-2 products. We quantify the quality of formed images and their derived level-2 products such as geocoded SLC, covariance and interferometry products over point targets. We further evaluate the performance of the NISAR processor and algorithms using real L-band data acquired by ALOS-1 and ALOS-2 and reformatted to NISAR format.

Launching in early 2024, the NASA-ISRO SAR (NISAR) mission is upon us and will bring an unprecedented amount of SAR data to the international SAR science community. To handle its 50PB of SAR data per year, NISAR uses novel approaches to data processing, management, and distribution. NISAR also offers a unique product portfolio that is adding several analysis ready data products to the typical SAR fare.This paper summarizes innovative approaches developed by the Alaska Satellite Facility and the NASA Jet Propulsion Laboratory to make NISAR’s massive data set accessible to the community. We summarize developed concepts for data discovery and distribution, and highlight tools and services that enable working with SAR data directly at the archive. We close with a range of education and training efforts developed across the SAR community that will help familiarize users with NISAR processing flows.

For NASA-ISRO Synthetic Aperture Radar (NISAR) mission processing, Quality Assurance (QA) plots, metrics, and summary statistics will be produced by the NISAR Science Data System (SDS) in human- and machine-readable formats for each level-1 (L1) and level-2 (L2) science data product and made freely and openly available for users. It is expected that NISAR will generate millions of individual science data products, with each product ranging in size from a few hundred MB to over 40 GB, for approximately 100 PB added to the NASA data archives over the nominal 3 year mission. This volume will be the largest of any NASA mission to date; it creates a need for automated checks and for each product to be digested into smaller, summary statistics and plots for quick review. This need will be addressed by the QA software, which will be run at scale as part of nominal NISAR mission processing on each L1/L2 data product. QA will generate small, standalone, human- and/or machine-parsable files that will be freely available from the Alaska Satellite Facility Distributed Active Archive Center (ASF DAAC) alongside the primary L1/L2 NISAR products. This paper introduces the QA output files, their content and formats, and the usage of QA in the context of NISAR mission processing. In the era of big data, these smaller files could be a significant firststep towards analyzing broad trends across the large, primary L1/L2 NISAR datasets.

This paper presents the Radar Echo Emulator (REE), a SAR simulation tool with high fidelity and flexibility that is being used to assess and quantify the radar instrument functionality, performance, and overall impulse response for various modes of the NASA-ISRO synthetic aperture radar (NISAR) mission. NISAR is a complex multi-channel multi-polarization wide-band wide-swath high-resolution SAR instrument based on SweepSAR architecture [1]. Its radar instruments support several configurable radar modes to fulfill variety of science applications [2]. The paper provides a brief overview of the REE tool as a general SAR simulator while primarily focusing on its SweepSAR and digital beamforming (DBF) application via an example of repeat-pass four-channel L-band split-spectrum NISAR simulation over an extended Amazon rainforest-like scene [3], [4]. An example of the ionosphere effect with split-spectrum analysis and an example with radio frequency interference (RFI) contamination are also presented.

NASA-ISRO SAR (NISAR) Mission is equipped with L- and S-band Synthetic Aperture Radar (SAR) to produce high-quality imagery products to detect changes on earth surface, and to estimate and monitor different geophysical quantities such as soil moisture and water surface extent. Both L-band and S-band are susceptible to Radio Frequency Interference (RFI) in many geographic regions. One of the main sources of RFI are powerful electromagnetic signals from ground-based radar and communication platforms. In addition, there are unregulated electromagnetic emitters that could also operate in NISAR passband. RFI observed using existing L-band acquisitions, e.g., from ALOS PALSAR, appear to be modulated wideband and narrow band signals in the range frequency spectrum. RFI often causes haze-like image artifacts on focused L-band SAR images. [1]. In addition, RFI degrades the quality of the Single Look Complex (SLC) images and interferometric coherence, and can bias the estimation of physical quantities such as ionospheric delay or soil moisture from SAR images. Hence effective RFI detection and mitigation algorithms are desired to address RFI contamination in NISAR products. One of the RFI mitigation algorithms developed is Slow-Time Eigenvalue Decomposition (ST-EVD) which is a Principal Component (PC) based approach that removes RFI Eigenvalues through projection. An adaptive data-driven thresholding algorithm, Slow-Time Eigenvalue Slope Thresholding (ST-EST) is being developed to detect RFI contamination severity and provide ST-EVD with mitigation thresholds.

The effect of small-scale ionospheric TEC variations (SSTV) on InSAR is assessed using ground GNSS data. The spectrum of standard deviation of differential TEC (SDT) is derived using GNSS data to characterize SSTV at spatial scales between 1 km and 50 km. Validation of this technique is conducted by comparing the GNSS SDT measurements with ALOS PalSAR data over Chili. The comparison shows consistent magnitude and variation trend between the GNSS and SAR data. The effects of SSTV on NISAR and the future Surface Deformation Change (SDC) mission are assessed using global SDT measurements during seven years from 2013 to 2019. The period covers high, medium, and low solar EUV radiation activities that affect TEC values and variations. Our analysis indicates that the effect of SSTV is substantial and can be on the order of 2 cm at high latitudes and 0.4 cm at middle and low latitudes.

Now, more than ever, there is a need for higher-quality products for remote-sensing end users. Following recent advancements in synthetic aperture radar (SAR) processing algorithms, we provide a full assessment of the radiometric dependence of geocoded C-band SAR backscatter processed with radiometric terrain correction (RTC) over differing target types. In particular, we compare the flatness of RTC-normalized backscatter coefficient gamma-naught with respect to local incidence angle over 46 Sentinel-1 (S1) datasets representing about 20 land classes in the Copernicus Global Land Service (CGLS) Land Cover 100m classification using the Observational Products for End-Users from Remote Sensing (OPERA) RTC-S1 product workflow and the ISCE3 framework. We also calculate the mean and median radar backscatter over areas of foreslope and backslope. Our results suggest that the dependence of gamma-naught on the local topography depends strongly on the land type and only exhibits near-constant behavior in certain forest land types. Evidence also suggests that as we move further away from densely tree-covered areas, the dependence of gamma-naught on the local incidence angle becomes stronger.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory (JPL) will provide a near-global Radiometric Terrain Corrected synthetic aperture radar (SAR) backscatter from Sentinel-1 (RTC-S1) product. The OPERA RTC-S1 product will deliver map-projected burst-based radar images with a geographic scope that includes all land masses excluding Antarctica, and with temporal sampling coincident with the availability of Sentinel-1 interferometric wide (IW) single-look complex (SLC) data. This paper presents the OPERA RTC-S1 product, providing details about its layers, static layers, and metadata; describing the product’s processing workflow, based on the ISCE3 framework and using the same algorithms developed for the NASA-ISRO Synthetic Aperture Radar (NISAR) mission; and outlining the algorithm verification and the product validation plan. We also present a global mosaic of preliminary OPERA RTC-S1 products generated from a global end-to-end test run from a Sentinel-1A orbit cycle. The OPERA RTC-S1 product will be publicly distributed through the Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC) free of charge, with a release date scheduled for September 2023 with forward stream production.

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.

This article presents a projection algorithm based on the representation of radar samples as area elements, rather than point elements as traditionally done in previous works. Each area element in the geographic grid (geogrid) is associated with a set of samples in the radar grid that intersect completely or partially the area element according to the topography and the radar geometry. Accurate geocoding with adaptive multi-looking is achieved by successively assigning the weighted average of the radar samples to the corresponding geogrid elements. Analogously, the slant-range projection of geocoded data is improved by projecting the geogrid pixels onto the radar grid according to their projected area. When our slant-range projection approach is used within previously-published radiometric terrain correction (RTC) algorithms, the processing time is significantly reduced, performing 4.2 to 6.5 times faster over multi-looked data and up to 16.7 over single-look data. We demonstrate the strength of the area projection algorithm for RTC and geocoding using UAVSAR and Sentinel-1 data, and evaluate the results in the context of the upcoming NISAR mission.

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 mathematical formulation for the phase due to the errors in digital elevation models (DEMs) in synthetic aperture radar (SAR) interferometry (InSAR) time series obtained by the small baseline (SB) or the small baseline subset method. We show that the effect of the DEM error in the estimated displacement is proportional to the perpendicular baseline history of the set of SAR acquisitions. This effect at a given epoch is proportional to the perpendicular baseline between the SAR acquisition at that epoch and the reference acquisition. Therefore, the DEM error can significantly affect the time-series results even if SB interferograms are used. We propose a new method for DEM error correction of InSAR time series, which operates in the time domain after inversion of the network of interferograms for the displacement time series. This is in contrast to the method of Berardino (2002) in which the DEM error is estimated in the interferogram domain. We show the effectiveness of this method using simulated InSAR data. We apply the new method to Fernandina volcano in the Galapagos Islands and show that the proposed DEM error correction improves the estimated displacement significantly.

In this letter, since these methods are able to process signals locally, two spatial frequency analyses including windowed Fourier transform and wavelet transform are used to reduce synthetic aperture radar interferometric phase noise.