Since the launch of the European Space Agency's (ESA's) Sentinel-1 mission, the synthetic aperture radar (SAR) user community has grown exponentially, adding a large number of users to the field who have only limited experience with the peculiarities of SAR and interferometric SAR (InSAR) data. This growth has been fueled by the increasing availability of globally acquired, free and open SAR data and by a growing number of freely accessible, open source processing tools, such as the Sentinel Application Platform, InSAR Scientific Computing Environment, GMTSAR, and the Alaska Satellite Facility's cloud-based Hybrid Pluggable Processing Pipeline and OpenScienceLab platforms. Anticipated to launch in early 2025, the NASA-Indian Space Research Organisation SAR (NISAR) mission will amplify this trend by bringing an unprecedented amount of new, free and open SAR data to the international SAR science community. As the first mission to provide L-band data freely, on a global scale, and in a regularly sampled manner, NISAR will further diversify the SAR application portfolio, likely accelerating community growth. Regional access to simultaneous L- and S-band data facilitated by NISAR will furthermore enable new SAR technology development that may lead to exciting new scientific discoveries. Focusing on efforts made by NASA and the broader U.S. SAR community, this article summarizes innovative approaches to making the massive datasets of Sentinel-1 and NISAR more accessible to and usable by the SAR community. We summarize novel concepts for data discovery and distribution and highlight tools and services that enable working with SAR data directly at the archive. We also discuss recent education and training efforts developed across the U.S. SAR community to help new users of SAR build capacity to use this exciting dataset. Here, we introduce a set of independent, yet interrelated, projects aimed at developing educational materials and open source science workflows for SAR users with different experience levels.

The NASA-ISRO Synthetic Aperture Radar (NISAR) Mission experienced some technical issues in observatory level testing that required mitigations to be carried out on the reflector system, preventing a launch in 2024 as previously planned. The reflector has been reconditioned to address these issues, and NISAR is now on target for launch in early 2025. After launch, the spacecraft is planned to undergo commissioning for period of 90 days, after which science operations will begin. NISAR has two radar instruments - an L-band (24 cm wavelength) radar provided by NASA, and an S-band (9.4 cm wavelength) radar provided by ISRO - each of which can be operated individually or simultaneously. Each radar has a swath width of greater than 240 km for all modes at a variety of resolutions and polarimetric states. Due to precise orbit control and pointing, each radar also will produce repeat-pass interferometric measurements over all science targets. During the science phase, NISAR will collect about 35 Terabits of L-band radar image data each day, observing all land and ice-covered surfaces of Earth on the ascending and descending portions of each orbit every 12 days, and collecting about 5 Terabits of S-band radar image data each day over India and surrounding areas, Antarctica, and distributed global scientific areas of interest. Nearly all S- band acquisitions are collected simultaneously with L-band acquisitions, creating a unique globally distributed time- series data set. The commissioning plan calls for early engineering mode acquisitions around one month after launch, some of which may be usable to form images, followed by a period of orbit adjustment and system timing and pointing calibration. To prepare for science operations, the NISAR project has worked with the science team to develop a list of observational areas where early data can be acquired to demonstrate the preliminary quality of the data and to illustrate the science themes NISAR is addressing: solid Earth sciences, ecosystems sciences including global soil moisture, and cryosphere sciences, as well as many applications. In addition, cloud-based tools for image processing and diagnostic analysis, usable by the project and science team members alike, will be available to examine these early data sets.

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.

Active-layer thickness (ALT) is estimated for a study area in northern Alaska's continuous-permafrost zone using satellite data from Sentinel-1 (radar) and ICESat-2 (lidar) for the period 2017 to 2022. Synthetic aperture radar (SAR) interferograms were generated using the Short Baseline Subset (SBAS) approach. Displacement time series over the thaw season (June–September) are fit well with a linear model (root mean square error (RMSE) scatter is less than 7 mm) and show maximum seasonal subsidence of 20–60 mm. ICESat-2 products were used to validate the interferometric synthetic aperture radar (InSAR) displacement time series. ALT was estimated from measured subsidence using a widely used model exploiting the volume difference between ice and water, reaching a maximum depth in our study area of 1.5 m. Estimated ALT is in good agreement with in situ and other remotely sensed data but is sensitive to assumed thaw season onset, indicating the need for reliable surface temperature data. Our results suggest the feasibility of long-term permafrost monitoring with satellite InSAR. However, the C-band (∼55 mm center wavelength) Sentinel radar is sensitive to vegetation cover and, in our studies, was not successful for similar monitoring in the heavily treed discontinuous-permafrost zone of central Alaska.

An open-science tool built to support NASA missions is making synthetic aperture radar, once the domain only of subject matter experts, more accessible for nonspecialists and real-world applications.

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Northern high-latitude river ice provides critical natural infrastructure for winter travel, commerce, hunting, fishing, and recreation in rural areas with little or no road access. Open water zones (OWZs) in river ice are dangerous for such travel and are most common during early winter. Changes in the occurrence and duration of OWZs may also indicate more widespread shifts in ice regimes across Alaska and other northern regions. To aid in detecting open water hazards and broader changes in winter conditions, we developed a supervised classification with a principal component analysis (PCA) using both polarizations of Sentinel-1 C-band synthetic aperture radar (SAR) dual-polarized data for rivers in early winter to discriminate between ice cover and open water. Previous SAR river ice classifications have focused on one or two river reaches often with an emphasis on moving ice during spring break-up, hampering generalization of these results to other rivers and seasons. To address this limitation, we used 12 reaches from eight rivers for training and validation with the aim to combine data from different river types to create an ice classification that could be applied to northern high-latitude rivers from October through January. The classification was trained using shore-based time-lapse photos, aerial photos, and on-ice observations, and validated with shore-based time-lapse photos and independent citizen scientists' photo observations. Overall accuracy for the classification ranged from 65 to 93% with a corresponding range of 0.31–0.84 Cohen's Kappa statistic (K̂). We report some ambiguity between open water and smooth ice, especially in slower-flowing parts of rivers. We conclude that VV and VH thresholds can therefore be customized to increase accuracy, depending on specific river attributes such as river morphology, silt/sediment load, and channel flow velocity. This classification, which allows for mapping long river reaches in low-light winter conditions, can be performed on historical Sentinel-1 imagery to determine areas that display open water year after year. Once customized to a particular river, it can be automated to provide current open water zone maps to Alaskan and other rural northern communities worldwide to aid safer travel on ice.

Excess ground ice formation and melt drive surface heave and settlement, and are critical components of the water balance in Arctic soils. Despite the importance of excess ice for the geomorphology, hydrology and biogeochemistry of permafrost landscapes, we lack fine-scale estimates of excess ice profiles. Here, we introduce a Bayesian inversion method based on remotely sensed subsidence. It retrieves near-surface excess ice profiles by probing the ice content at increasing depths as the thaw front deepens over summer. Ice profiles estimated from Sentinel-1 interferometric synthetic aperture radar (InSAR) subsidence observations at 80 m resolution were spatially associated with the surficial geology in two Alaskan regions. In most geological units, the estimated profiles were ice poor in the central and, to a lesser extent, the upper active layer. In a warm summer, units with ice-rich permafrost had elevated inferred ice contents at the base of the active layer and the (previous years’) upper permafrost. The posterior uncertainty and accuracy varied with depth. In simulations, they were best (<0.1) in the central active layer, deteriorating (>0.2) toward the surface and permafrost. At two sites in the Brooks Foothills, Alaska, the estimates compared favorably to coring-derived profiles down to 35 cm, while the increase in excess ice below the long-term active layer thickness of 40 cm was only reproduced in a warm year. Pan-Arctic InSAR observations enable novel observational constraints on the susceptibility of permafrost landscapes to terrain instability and on the controls, drivers and consequences of ground ice formation and loss.

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.

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.

Satellite remote sensing time series have frequently been leveraged to track crop phenology changes throughout the growing season worldwide. These time series, primarily derived from optical sensors, can provide insights on changes that occur throughout the growing season compared to previous years. Additionally, time series can help monitor crop yields and overall production and can provide information about what is planted in a specific field. Optical remote sensors rely upon atmospheric and sky conditions, often causing gaps in the time series when images cannot be used because of cloud cover. The increasing availability of observations from synthetic aperture radar (SAR) allows for worldwide and repeat year-round collections. This study uses acquisitions from several different SAR missions (2018-2022) to map cropland extent using the coefficient of variation (CV) method in agricultural regions around the world, focusing predominantly on major global producers of corn, wheat, and rice.

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.

&lt;p&gt;Major geological hazards can devastate essential infrastructure and result in widespread injury and death. Understanding the underlying processes that can lead to these hazards and providing analysis-ready datasets in a timely fashion is crucial for hazard monitoring and disaster response and recovery efforts. In support of NASA&#039;s vision, we are committed to an open-source science initiative enabling the transparency, inclusivity and accessibility, and reproducibility of&amp;#160; Earth observation data &amp;#8211; all fundamental to the pace and quality of scientific progress. Under a NASA ACCESS effort, we have: 1) significantly lowered the latency of delivering displacement products, i.e. the Sentinel-1 Geocoded Unwrapped (S1-GUNW) products, and 2) enabled the expansion of the displacement data archive to over one million S1-GUNW products, currently making ARIA one of the largest open InSAR archives spanning continental scales across most major active tectonic and volcanic regions (Sangha et al., 2022). The scientific analysis of these products is streamlined via the open-source ARIA-tools, which simplifies the download and preparation of S1-GUNWs for time-series analysis through the open-source MintPy software (Yunjun et al., 2019). The derived datasets can support science applications as well as timely science-driven decision-making efforts, particularly, after or during disaster and recovery periods.&lt;/p&gt; &lt;p&gt;Here we demonstrate how our updated infrastructure, driven by an open-source Hybrid Pluggable Processing Pipeline (HyP3) cloud architecture, can be leveraged to support open science and disaster response applications ranging from analysis of volcanic unrest and earthquakes, to characterizing broader-scale tectonic processes.&lt;/p&gt;

Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

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.

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.

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility – all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid Image Analysis (ARIA) project’s Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

The 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi-instantaneous ice-wedge melt, much larger areas were characterized by subtle, spatially variable post-flood changes. Surface deformation from 2015 to 2019 estimated from ALOS-2 and Sentinel-1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice-rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice-rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice-rich locations showed increases in observed greenness and wetness. Conversely, many ice-poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost-affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets.

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data.