Interferometric Synthetic Aperture Radar Analysis Research Articles

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Landslides pose a growing concern worldwide, emphasizing the need for accurate prediction and assessment to mitigate their impact. Recent advancements in remote sensing technology offer unprecedented datasets at various scales, yet practical applications demand further case studies to fully integrate these technologies into landslide analysis. This study presents a case study approach to fully leverage variety of multi-source remote sensing technologies for analyzing the characteristics of a landslide. The selected case is a landslide with a long runout debris flow that occurred in Gokseong County, South Korea, on August 7, 2020. The chosen multi-source technologies encompass digital photogrammetry using RGB and multi-spectral imageries, 3D point clouds acquired by light detection and ranging (LiDAR) mounted on an unmanned aerial vehicle (UAV), and satellite interferometric synthetic aperture radar (InSAR). The satellite InSAR analysis identifies the initial displacement, triggered by rainfall and later transforming into a debris flow. The utilization of digital photogrammetry, employing UAV-RGB and multi-spectral image data, precisely delineates the extent affected by the landslide. The landslide encompassed a runout distance of 678 m, featuring an initiation zone characterized by an average slope of 35°. Notably, the eroded and deposited areas measured 2.55 × 104 m2 and 1.72 × 104 m2, respectively. The acquired UAV-LiDAR data further reveal the eroded and deposited landslide volumes approximately measuring 5.60 × 104 m3 and 1.58 × 104 m3, respectively. This study contributes a valuable dataset on a rainfall-induced landslide with a long runout debris flow, underscoring the effectiveness of multi-source remote sensing technology in monitoring and comprehending complex landslide events.

Land subsidence monitoring using InSAR technique in the southwestern region of Bangladesh

Deltas like the Ganges-Brahmaputra Delta (GBD) experience significant land subsidence due to overlaying load, consolidation, sediment compaction, regional tectonics, and human activity. The southwestern region of Bangladesh, marked by its low-lying floodplain with compressible sediments, experiences significant land subsidence. However, accurate assessments for these areas are very scarce, often focusing on the point-based measurements. This study employed Interferometric Synthetic Aperture Radar analysis, spanning from 2014 to 2022, to effectively measure land subsidence, and visualize it spatially. In the study area, subsidence rates were found to be ranging from 3 to 20 mm/year, with most areas experiencing 3-10 mm/year. While some areas show higher rates of 10-13 mm/year, very few exceeded 14 mm/year. The average subsidence rate was 5.98 mm/year, demonstrating significant spatial variability, with higher rates in Gopalganj, Patuakhali, Bhola, Barishal, and other areas. The findings were validated based on existing Global navigation satellite system (GNSS) data points, affirming their reliability. Despite minor discrepancies with other measurements at specific points, the overall results align well with existing data. This comprehensive analysis provides a detailed assessment of land subsidence in the southwestern region of Bangladesh, highlighting significant spatial variability and offering valuable insights for future coastal development and risk mitigation strategies.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

Preliminary Investigation of Sudden Ground Subsidence and Building Tilt in Balitai Town, Tianjin City, on 31 May 2023

A short-term rapid subsidence event occurred in the Bi Guiyuan community in Balitai Town, Tianjin City, leading to the tilting of high-rise buildings and the emergency evacuation of over 3000 residents. In response to this incident, InSAR (Interferometric Synthetic Aperture Radar) technology was swiftly employed to monitor the subsidence in the area before and after the event. Our observations indicate that the region had maintained stability for 8 months prior to the incident. However, over the course of the 15-day event, the ground experienced more than 10mm of subsidence. By integrating the findings from an InSAR analysis with geological studies, we speculate that the rapid subsidence in the region is related to the extraction of geothermal resources. It is suspected that during drilling operations, the wellbore mistakenly penetrated a massive underground karst cavity. Consequently, this resulted in a sudden rapid leakage of drilling fluid, creating a pressure differential that caused the overlying soil layers to collapse and rapidly sink into the cavity. As a result, short-term rapid subsidence on the ground surface and tilting of high-rise buildings occurred.

Subsurface magma movement inferred from extensometer and tiltmeter records during the early stage of the 2018 Shinmoe-dake eruptions, Japan

We infer the temporal changes in the pressure sources that induced crustal deformation during the 2018 Shinmoe-dake eruption using strain and tilt observations and discern that the deep magmatic activity associated with the early stage of this eruption began approximately 19 h earlier than the previously defined onset of magmatic activity. Distinct tilt changes were observed from around 09:00 on 6 March to 12:00 on 8 March 2018 (JST), coincident with observed lava outflow into the crater and lava dome formation. Existing studies have attributed this tilt change to the onset of the deflation of a spherical pressure source located at ~ 7 km bsl (below sea level) to the northwest of Shinmoe-dake. Here we examine strain and tilt data that were acquired in the Kirishima volcanic group, and we find that the distinct changes in the measured strain at Isa-Yoshimatsu Observatory began at around 14:00 on 5 March. This change can be explained by the deflation of a spherical pressure source, thereby suggesting that the onset of magma ascent was earlier than previously thought. The time variation in the spherical pressure source is estimated using the time-dependent inversion of the Ensemble Kalman Filter; the deflation source ascended from ~ 11 to 7 km bsl during Phase 1 (14:00 on 5 March to 06:00 on 6 March) and descended from 7 to 8 km bsl during Phase 2 (06:00 on 6 March to 12:00 on 8 March). Interferometric synthetic aperture radar analysis suggests that a dike intrusion had occurred just below Shinmoe-dake crater until 5 March, and this inflatable crustal deformation is attributed to the emplacement of residual volcanic fluids from the 2011 eruption. It is also known that the surface eruptive activity increased during Phase 1, including an increase in ash venting from the night of 5 March. These strain and tilt observations, therefore, suggest that magma ascended from ~ 11 km bsl to the magma reservoir at 7 km bsl during Phase 1, followed by a deflation of the magma reservoir during Phase 2 due to the large magma supply to the surface.Graphical

Investigating Deformation Mechanism of Earth-Rock Dams with InSaR and Numerical Simulation: Application to Liuduzhai Reservoir Dam, China

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Revealing the Land Subsidence Deceleration in Beijing (China) by Gaofen-3 Time Series Interferometry

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images.

Changes in widespread aquifer properties caused by a magnitude 6-class earthquake evaluated using InSAR analyses

Correlations between surface displacements and groundwater level changes have been widely used to understand aquifer properties and their site characteristics; however, the underlying mechanisms of various correlation types and the influence of earthquakes have not been fully investigated. In this study, we examine correlations between surface displacements from interferometric synthetic aperture radar analyses and groundwater level monitoring data in Osaka and Kyoto, Japan, over 4 years, a period including the 18 June 2018 Mw 5.6 northern Osaka earthquake. Both positive and negative correlations between the seasonal groundwater level changes and the seasonal surface displacements are identified. Based on the observations of the effects of the earthquake, a new conceptual aquifer dynamical model driving the relationship between the surface displacements and the groundwater level changes is proposed. We further reveal that sites with negative correlations increased after the earthquake, suggesting that the earthquake increased the groundwater recharge rate as a result of increases in aquifer transportation properties such as permeability and porosity.

Observation of spatial and temporal patterns of seasonal ground deformation in central Yakutia using time series InSAR data in the freezing season

The seasonal freeze/thaw variations of the active layer cause seasonal ground deformation, such as thaw subsidence in summer and frost heave in winter due to the cyclic phase changes of soil water. The Synthetic Aperture Radar (SAR) remote sensing, especially interferometric SAR (InSAR) techniques, has been proven to be a very useful tool for observing surface displacements of permafrost environments. Most previous studies on seasonal InSAR displacement have been concentrated in several study sites in the Arctic tundra regions. The objective of this study is to extend the InSAR analysis of seasonal ground deformations to boreal forest regions, where monitoring ground deformation is particularly important in terms of better understanding active layer dynamics under various ecological and geological conditions. In this study, we presented a strategy to analyze the time series SAR data in the freezing season to estimate the differential phase for seasonal ground deformation while overcoming the problem of coherence losses due to the rapid growth of vegetation during the short growing season. We applied the SBAS-based InSAR processing method to Sentinel-1 SAR in the central Yakutian lowlands, where boreal forests and thermokarst landforms are widely distributed, and retrieved frost-related displacement signals successfully. In addition to the magnitude of ground deformation in the freezing season, we further examined the temporal deformation pattern related to the progression of the freezing front in the soil during active freezing. Based on the Stefan solution, we proposed a piecewise linear model for the time series deformation. The proposed model divides the time series displacement according to the progression of freezing into three linear segments, and the slope parameter of each linear model can be interpreted as indicating the hydrological characteristics of the upper, middle, and lower parts of the soil. The results for the Yakutian lowlands illustrated the potential of SAR remote sensing to observe and quantify spatial details of the thermal and hydrological properties inside the active layer soil even in highly vegetated subarctic boreal forest areas.

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic aperture radar (InSAR) is often adopted for measuring the ground deformation rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation rate in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance to road) and the permafrost stability is mapped with the random forest method based on the high-quality data extracted from the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis.

EZ-InSAR: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user’s workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user’s area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all.

Refined mapping and kinematic trend assessment of potential landslides associated with large-scale land creation projects with multitemporal InSAR

Since 2012, a mega-project, “Mountain Excavation and City Construction” (MECC), has been underway on the Chinese Loess Plateau, aiming to create extra flat ground for urban growth. However, this large-scale land creation has led to dramatic changes in the local geological environment, forming a large number of engineered loess slopes. This study aims to map and assess the boundaries and stability of these potentially unstable engineered slopes (potential landslides). Based on spatio-temporal deformation patterns of landslides prior to failure, a novel interferometric synthetic aperture radar (InSAR)-based procedure for potential landslide mapping and stability evaluation was constructed in this study. The proposed procedure was employed to map and evaluate the potential landslides in the Yan’an New District (YND), the largest land-creation area in the Loess Plateau. First, the spatial locations and boundaries of the potential landslides were determined by combining optical image interpretation with differential deformation maps derived from Sentinel-1 small baseline subset InSAR (SBAS-InSAR) analysis. Then, the kinematic evolution trend of the mapped potential landslide was evaluated by quantitative analysis of the time-series displacements using an exponential model. Finally, the potential influencing factors and failure modes of the potential landslide were examined and discussed. The results indicate that the InSAR-derived differential deformation maps reflect the external boundary of the potential landslides well, and a total of 18 potential landslides are identified and mapped for the first time in the large-scale land-creation area of the YND. Over 82% of the coherent targets (CTs) within these potential landslides showed primary or steady creep during the InSAR observation period; however, tertiary creep was also observed on several slopes. Thus, it is crucial to utilize multitemporal InSAR and/or ground-based equipment for the continuous monitoring of engineered slopes in large-scale land-creation areas. The high correlation between spatiotemporal deformation patterns and environmental factors indicates that the activity of potential landslides is mainly controlled by the thickness of filling loess, while rainfall accelerates the displacement of slopes. These findings can contribute to the development of landslide disaster prevention and mitigation measures in the YND.

Interferometric Synthetic Aperture Radar versus Inclinometer Ground Surface Deformations at Active Landslide

Rockfalls and landslides commonly occur in the Hawaiian islands. When these events occur along a thoroughfare, they can result in the loss of lives, cause significant damage to infrastructure, and force full or partial road closures, resulting in traffic congestion and commuting delays, sometimes for months. The Hawaii Department of Transportation (HDOT) is interested in historic slope movements along its roads and highways, as this history can guide in prioritizing spending on slope remediation. This has prompted HDOT to be interested in the use of interferometric synthetic aperture radar (InSAR) imagery for detecting historical slope movements and identifying areas that might pose concern for landslide or rockfall activity. To provide a proof of concept for the implementation of this technology in Hawaii, an InSAR case study of a landslide on the island of O’ahu was conducted to compare the results with available inclinometer data. Two-dimensional decomposition was implemented for opposing orbits of descending and ascending Sentinel-1 datasets. Overall, the results show that when both ascending and descending datasets are used to derive line-of-sight displacements that are resolved in a direction along the direction of movement (typically perpendicular to the slope contours), InSAR analysis can effectively capture inclinometer trends in areas experiencing relatively no or little displacement over time (<30 mm/year) but the accuracy diminishes in fast-moving slides (≥270 mm/year).

An identification method of potential landslide zones using InSAR data and landslide susceptibility

Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |DU| or |DE|>10 mm/year (DU and DE are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|DU| or |DE|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates DU and DE were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

Comparison of tropospheric delay correction methods for InSAR analysis using a mesoscale meteorological model: a case study from Japan

A major source of error in interferometric synthetic aperture radar (InSAR), used for mapping ground deformation, is the delay caused by changes in the propagation velocity of radar microwaves in the troposphere. Correcting this tropospheric delay noise using numerical weather models is common because of their global availability. Various correction methods and tools exist; selecting the most appropriate one by considering weather models, delay models, and delay calculation algorithms is essential for specific applications. We compared the performance of two tropospheric delay correction methods applied to Advanced Land Observing Satellite-2 (ALOS-2) data acquired over Japan, where the atmospheric field is complex with significant seasonal variation. We tested: (1) a method of delay integration along the slant radar line-of-sight (LOS) path using the mesoscale model (MSM) provided by the Japan Meteorological Agency and (2) the Generic Atmospheric Correction Online Service (GACOS) for InSAR, which estimates delay using the high-resolution forecast (HRES)-European Centre for Medium-Range Weather Forecasts (ECMWF) products along with an iterative decomposition approach. The results showed that the tropospheric delay correction using the slant-delay integration approach with MSM, which has a finer temporal and spatial resolution, performed slightly better than GACOS. We further found that the differences in the refractivity models would have limited significance, suggesting that the difference in performance mainly originates from differences in the numerical weather models being used. This study highlights the importance of using the best-available numerical weather model data for tropospheric delay calculations.Graphical

Joint Use of Optical and Radar Remote Sensing Data for Characterizing the 2020 Aniangzhai Landslide Post-Failure Displacement

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, and until June 2021, emergency engineering work was carried out to prevent the further failure of the reactivated landslide. This study investigates the potential of joint use of time series Interferometric Synthetic Aperture Radar (InSAR) and optical pixel offset tracking (POT) to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were deeply explored. Surface deformation along the line-of-sight (LoS) direction was retrieved by the time series InSAR processing with the two Sentinel-1 datasets, revealing a maximum deformation rate up to 190 mm/year. The large horizontal displacements were also detected from the POT processing using 11 optical images acquired by the PlanetScope satellite (3 m spatial resolution), showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series analysis from the InSAR and optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide. A slight acceleration was detected from both InSAR and optical POT time series analysis between May 2021 and June 2021, which could be caused by the increased rainfall in May 2021.

Increased Water Content in the Active Layer Revealed by Regional‐Scale InSAR and Independent Component Analysis on the Central Qinghai‐Tibet Plateau

AbstractIsolating seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) time‐series is critical to quantitative understanding the freeze‐thaw processes in permafrost regions. Physics‐ or statistics‐based approaches have been developed to extract seasonal deformation, yet both constraining their evolution in time domain, and thus impeded the quantification of their amplitude variability especially over large scales. By applying Independent Component Analysis (ICA) on Sentinel‐1 InSAR measurements during 2015–2019 on the central Qinghai‐Tibet Plateau, we reveal that the averaged seasonal deformation is increasing with a linear trend of around 0.17 cm/year. The growing seasonal amplitude is attributed to an 8 cm increase of the Equivalent Water Thickness in the active layer. The results demonstrate the capability of ICA‐based decomposition on isolating freeze‐thaw‐related deformation from other components. The large‐scale spatial distribution of varied seasonal deformation can provide new insight into quantifying the water mass balance in vast permafrost regions.

Coseismic DInSAR Analysis of the 2020 Petrinja Earthquake Sequence

Interferometric SAR analysis provides an excellent opportunity to perform large-scale and rapid coseismic deformation mapping. Between December 28-30, 2020, three earthquakes with magnitudes greater than 4.3 occurred during the 2020 Petrinja Earthquake Sequence near Petrinja in Croatia, resulting in significant coseismic deformation. Considering both the available ascending and descending Sentinel-1A/B images preceding and following the Petrinja Earthquake Sequence, 2.5D differential interferometric analysis was performed to determine the resulting deformation field, which have significant importance in civil engineering related countermeasures and hazard assessment. With the applied methodology, the derived horizontal and vertical deformation fields can be characterized by a maximum of ±0.43 m local East-West, a maximum of 0.15 m local subsidence and a maximum of 0.19 m local vertical uplift near Petrinja.

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.