Wildfires pose severe threats to lives, critical infrastructure, and ecosystems, particularly in urban-wildland interface settings where rapid situational awareness is essential for response and early recovery. This study presents a comparative multisensor satellite workflow for rapid wildfire damage assessment, demonstrated using the 2025 Pacific Palisades Fire (California) as a case study. We integrate Sentinel-2 optical imagery with ALOS-2 (L-band) and Sentinel-1 (C-band) synthetic aperture radar (SAR) data to map burned areas and characterize structural impacts. Burned regions are delineated using optical spectral indices, while SAR coherence and texture features support urban damage characterization, including conditions where optical observations are hindered by smoke or clouds. Results indicate that ALOS-2 achieved higher classification performance (84.64% accuracy, Kappa = 0.73) compared with Sentinel-1 (78.31% accuracy, Kappa = 0.61), underscoring the added value of Lband SAR for post-fire impact mapping. The event affected 92.99 km2, including 92.05 km2 of forest and 0.94 km2 of residential areas. Beyond technical performance, the comparison highlights operational trade-offs relevant to disaster response, where higher-accuracy products must be balanced against data accessibility and acquisition frequency. Overall, the proposed multisensor analysis supports rapid post-fire assessment and provides actionable information for emergency response and evidence-based recovery planning in fire-prone regions.

Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from < 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions.

The decreasing sea level of the Caspian Sea is having a serious impact on coastal ecosystems and biodiversity. This study, conducted over a decade from 2014 to 2023, provides a comprehensive analysis of the coastal transformations in the Gizil-Aghaj State Reserve, Azerbaijan, using remote sensing technologies. By utilizing a combination of optical and radar satellite data, we mapped the evolving interplay between land and sea. Our research reveals a significant coastline shift, with the Caspian Sea receding to expose an additional 218 km2 of land. This significant change was most apparent in the northeastern area, corresponding with regions experiencing substantial land subsidence. As the Caspian Sea's level decreases and the land sinks simultaneously, it's reasonable to expect that the shoreline would remain stable. In contrast to areas with land subsidence, places where the land is uplifting, along with the Caspian Sea's decreasing level, are likely to experience noticeable changes in their shoreline, suggesting a more dynamic and changing coastal area. These findings are crucial for understanding the fluctuations in the Caspian Sea level, likely influenced by a combination of natural geological processes, human activities, and broader climatic trends. The subsidence observed in some areas may be due to tectonic movements or human activities such as resource extraction. In difference, the uplift seen in other areas, where there is evidence of building up over time, might be influenced by both anthropogenic factors and natural tectonic processes. Moreover, our study highlights the intricate relationship between coastal dynamics, vertical land movements, and environmental changes. It highlights the critical need for integrated and multi-dimensional monitoring approaches to address these complex interactions. These results not only contribute to a deeper understanding of the Gizil-Aghaj State Reserve's coastal ecosystem but also offer valuable perspectives on the Caspian Sea's response to climate change. Such insights are crucial for developing adaptive strategies for coastal management and conservation in an era marked by environmental uncertainties and changes.

National Aeronautics and Space Administration Advanced Rapid Imaging Analysis (ARIA) Damage Proxy Maps (DPMs) are developed by the NASA Jet Propulsion Laboratory (NASA JPL) to identify potentially damaged areas based on interferometric coherence loss in Synthetic Aperture Radar (SAR) data. DPMs are typically based on data from Sentinel-1 satellites that orbit every 12 days, meaning that results can be provided within 1-2 weeks of an event. Although DPMs have been qualitatively validated as being able to detect surface effects of earthquakes, quantitative validations of their ability to differentiate damaged from undamaged areas and different types and levels of surface effects are lacking. We propose a framework for quantitative validation and apply it to surface fault rupture data from the 2019 Ridgecrest Earthquake sequence. The quantitative analyses take two forms: (1) the statistical distribution of a DPM index ( I DPM ) for different fault displacement ranges through box and whisker plots, and (2) empirical fragility functions that relate I DPM to probabilities of displacements exceeding certain thresholds. These relationships are developed for DPM1 (derived from one pre-event pair and one co-event pair of SAR images) and DPM2 (derived from multiple pre-event and co-event pairs of SAR images). We show that both DPM types perform similarly well for distinguishing between no surface displacement and some surface displacement. The predictive power of I DPM metrics, as measured by a dispersion term in the fragility model, shows the best performance for low surface fault displacements and DPM2-based indices. Recall and precision performance metrics show favorable performance of fragility models for identifying locations of fault displacement but increasing rates of false positives as fault displacement increases (i.e. predictions of displacements exceeding a threshold that did not occur). While this validation study was performed for a single earthquake sequence involving a specific area of California, these results demonstrate both the capabilities and limitations of I DPM as a rapid, post-event predictive tool for identifying locations and severity of ground displacements in environments similar to those in the Ridgecrest area (flat terrain, limited vegetation).

Measurements of both horizontal and vertical surface displacements allow for rigorous estimation of the moment deficit and the fault locking along subduction zones, including continental megathrusts. Previous measurements in the Himalayas were restricted to horizontal velocities from Global Navigational Satellite Systems, so the locking and the width of transition from apparent locking to interseismic creep were not well constrained. We present new observations of surface deformation from interferometric synthetic aperture radar for approximately 800 km along Himalaya. The interseismic velocity field along arc-perpendicular transects suggests a 5-8 mm/yr uplift in the higher Himalayas. We infer that the megathrust accommodates 20-22 mm/yr convergence over a width of similar to 115 km from the frontal thrust followed by a similar to 40 km transition zone. Sufficient strain has accumulated over the past 5-7 centuries in the central seismic gap that could be released by two Mw 8.8 earthquakes.

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.

Catastrophic landslides are often preceded by slow, progressive, accelerating deformation that differs from the persistent motion of slow‐moving landslides. Here, we investigate the motion of a landslide that damaged 12 homes in Rolling Hills Estates (RHE), Los Angeles, California on 8 July 2023, using satellite‐based synthetic aperture radar interferometry (InSAR) and pixel tracking of satellite‐based optical images. To better understand the precursory motion of the RHE landslide, we compared its behavior with local precipitation and with several slow‐moving landslides nearby. Unlike the slow‐moving landslides, we found that RHE was a first‐time progressive failure that failed after one of the wettest years on record. We then applied a progressive failure model to interpret the failure mechanisms and further predict the failure time from the pre‐failure movement of RHE. Our work highlights the importance of monitoring incipient slow motion of landslides, particularly where no discernible historical displacement has been observed.

National Aeronautics and Space Administration (NASA) Damage Proxy Maps ( DPMs) are products that were developed by NASA's Jet Propulsion Lab (NASA-JPL) to map damage after major natural and anthropogenic disasters. The maps utilize pairs of synthetic aperture radar (SAR) images to detect areas that have experienced significant coherence loss following a disaster event. Areas with significant coherence loss as identified by DPMs are expected to have surface change, which may be related to damage phenomena, such as structural damage. Limited prior research that quantitatively associates a DPM index related to coherence change to structural damage assessed in person is extended here to investigate associations with the probability of exceeding various structural damage states. The damage state considered here is partial or full collapse, using a newly evaluated data set from Amatrice, Italy from the M6.1 Central Italy earthquake (the first major seismic event of the 2016 Central Italy earthquake sequence). Taking a DPM-conditioned probability >= 0.5 as a prediction of partial or full collapse, we find that the DPM has a recall of 80.9%, a precision of 60.8%, and an accuracy of 65.8% in regards to identifying partial or full collapse of structures.

Synthetic aperture radar (SAR) can be combined interferometrically to estimate the coherence of the SAR phase. The coherence of the phase over time depends on the stability of the major objects and surface that are reflecting the SAR signal, at spatial scales near the radar wavelength and larger. When there are major changes in the radar reflection, it causes a loss of interferometric coherence, so we use methods of detecting coherence change as a proxy for damage or other sudden change to buildings, other structures, or the land surface, which we call a damage proxy map (DPM). The most common method for estimating the interferometric coherence is with spatial phase correlation measures over small areas. The simplest damage proxy map method is to calculate the coherence of an interferometric SAR (InSAR) pair before the event and another pair that spans the event and take the difference of the coherence to measure the change likely due to the event. This method can be effective for areas where the InSAR coherence is normally very high at the radar wavelength used. SAR at L-band (24 cm) wavelength has high coherence in a wider variety of environments than the shorter wavelengths. A more advanced, but requiring more computational resources, damage proxy map method involves calculating the coherence of all possible InSAR pairs for an interval of some months or years before the event to develop statistics about the coherence of each ground location as a function of InSAR pair interval and other parameters such as interferometric spatial baseline. Then InSAR pairs that span the event are compared to the pre-event statistics to estimate the likelihood that the co-event coherence has been reduced by damage or other surface changes. We have found that damage proxy maps are useful for detecting likely damage from many different types of events, including earthquakes, hurricanes, tornados, tsunami inundation, large landslides, or other causes.

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production.

We establish a workflow for validating NISAR Solid Earth Science (SES) products based on UAVSAR measurements of secular velocities and coseismic displacements across earthquake faults. UAVSAR is an L-band synthetic aperture radar capable of measuring solid Earth deformations through repeat pass interferometry. High spatial resolution makes UAVSAR especially sensitive to surface deformation at short spatial wavelengths (e.g., near a crustal fault). Furthermore, UAVSAR acquisition schemes can provide a 3D picture of deformation. For NISAR validation, secular (interseismic) deformation will be measured across the creeping section of the San Andreas fault, where fault creep presents a well-defined tectonic signal and a rich UAVSAR data archive exists. The UAVSAR measurements will be quantitatively compared to measurements based on satellite InSAR data. The workflow developed herein is similar to that in the SES algorithm theoretical basis document (ATBD) for validating NISAR products, with changes made to account for the peculiarities of UAVSAR data.

Azimuth or along-track (approximately north-south) motion is critical in constructing three-dimensional ground motion with synthetic aperture radar (SAR) satellites orbiting the Earth in sun-synchronous polar orbit. The main problem of measuring azimuth motion with short-wavelength SAR data is decorrelation. A fleet of newly launched and upcoming long-wavelength L-band SAR satellites bring new opportunities for measuring azimuth motion. However, azimuth motion measured with L-band SAR data often contains large azimuth shifts caused by the Earth's ionosphere. We outline the framework of separating the azimuth motion and ionospheric azimuth shift from an analysis of the ionospheric effects on SAR images and SAR measurement precisions. We demonstrate three methods, among which one is newly proposed, can separate the azimuth motion and ionospheric azimuth shift with higher precisions. We evaluate the performances of the three methods by simulations using parameters of several selected L-band SAR satellites. The results show that, at kilometer resolutions, the azimuth motion measured by multiple-aperture SAR interferometry (MAI) can achieve centimeter precision, while the ionospheric azimuth shifts can be estimated with decimeter precision. Based on these results, a strategy for obtaining corrected azimuth motion is subsequently suggested, which achieves at least a first-order ionospheric correction of the original higher resolution MAI result. The three methods were also compared by real data processing examples. Furthermore, using real and simulated data of selected L-band SAR satellites, we present the first L-band MAI time series analysis result that measures subtle ground motion, as illustrated by the example of the postseismic deformation after the 2016 Kumamoto earthquakes in Japan. The performance is expected to be further improved with future L-band SAR missions that have much higher duty cycles. Some geophysical applications, in particular, those associated with the Earth's tectonic processes, can thus benefit from the azimuth motion measured by L-band SAR data.

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.

We image the rupture process of the 2021 Mw 7.4 Maduo, Tibet earthquake using slowness-enhanced back-projection and joint finite fault inversion, which combines teleseismic broadband body waves, long-period (166-333 s) seismic waves, and 3D ground displacements from radar satellites. The results reveal a left-lateral strike-slip rupture, propagating bilaterally on a 160-km-long north-dipping sub-vertical fault system that bifurcates near its east end. About 80% of the total seismic moment occurs on the asperities shallower than 10 km, with a peak slip of 5.7 m. To simultaneously match the observed long-period seismic waves and static displacements, notable deep slip is required, despite a tradeoff with the rigidity of the shallow crust. This coseismic deep slip within the ductile middle crust could result from strain localization and dynamic weakening. Local crustal structure and synthetic long-period Earth response for Tibet earthquakes thus deserve further investigation. The WNW branch ruptures ~75 km at ~2.7 km/s, while the ESE branch ruptures ~85 km at ~3 km/s, though super-shear rupture propagation possibly occurs during the ESE propagation from 12 s to 20 s. Synthetic back-projection tests confirm overall sub-shear rupture speeds and reveal a previously undocumented limitation caused by the signal interference between two bilateral branches. The stress analysis on the forks of the fault demonstrates that the pre-compression inclination, rupture speed, and branching angle could explain the branching behavior on the eastern fork.

Following the 2019 Ridgecrest (California) earthquake sequence, the Geotechnical Extreme Events Reconnaissance (GEER) association deployed and coordinated a reconnaissance effort that included teams funded by National Aeronautics and Space Administration (NASA), the US Geological Survey (USGS), the California Geological Survey (CGS), and the US Navy to document ground failure that had occurred at China Lake, Searles Lake, and surrounding areas. At Searles Lake, the teams found locations with ejecta and locations without surface manifestation, although the reconnaissance was relatively rapid, and most areas around the lake were not observed. Accordingly, two other data sources have been considered to develop a more complete spatial representation of ground failure: (1) damage proxy maps (DPMs) based on the analysis of multi-epoch synthetic aperture radar (SAR) data; and (2) optical (visible and near-infrared) satellite images. The Searles Lake lakebed lacks vegetation and is relatively level in elevation, making it an ideal location for using the remote sensing data. We begin by training a machine learning algorithm using observations from a small area to detect the presence of ejecta from the optical satellite images. Subsequent testing of the algorithm using observations from a broader area showed reasonably good results, but with larger rates of misidentification than in the training data. The algorithm in combination with the direct observations are used to generate post-event maps of surface manifestation at the same resolution as the satellite images, which are being used to validate the DPMs to facilitate future applications for rapid post-event ground failure detection and loss estimation.

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.

Global success of utilizing X/C/L-band InSAR (Interferometric Synthetic Aperture Radar) to survey ground deformation over non-forested terrain in the past two decades, has raised interest in monitoring forested lands, where relatively short-wavelength X/C/L SAR acquisitions often experience strong decorrelation and downgraded InSAR quality. To address this challenge, we considered the long-wavelength P-band SAR and conducted a large-area experiment over diverse terrains of the U.S. West Coast to comprehensively assess P-band SAR's capability for ground deformation surveying. Our results show that P-band InSAR observations greatly outperformed L-band data for identifying ground deformation within forested regions and for measuring spatially high-gradient displacements, such as for slow-moving landslides. Over the entire study area, P-band InSAR helped to discover >200 new landslides that were missing from existing landslide inventories. It also demonstrated high capability of penetrating through shallow snowpack to collect SAR signals from the ground surface beneath. However, P-band data manifested lower sensitivity to subtle deformation, as expected theoretically, and encountered coherence loss resulting from heavy snowpack. Overall, P-band SAR demonstrated to be a highly effective tool for discovering deformation beneath dense forest canopies and for quantifying spatially high-gradient displacements. These findings provide an experimental basis for planning future satellite and airborne P-band SAR missions to enhance the capability to monitor changes of the Earth's surface.