Interferometric Synthetic Aperture Radar Deformation Research Articles

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios.Graphical

Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022

Abstract A 6.9 magnitude earthquake hit Menyuan County, Haibei Prefecture, Qinghai Province, at 01:45 PM Beijing time on January 8, 2022 (17:45 PM GMT time on January 7, 2022). To explore the magnitude of the earthquake deformation and the affected area, this work combined optical remote sensing interpretation, interferometric synthetic aperture radar (InSAR) coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution Earth observation system of the Qinghai Remote Sensing Center for Natural Resources, high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were acquired immediately after the earthquake. The airborne triangulation encryption method was used to carry out orthographic correction, fusion, and mosaic processing of digital orthophoto map (DOM) and digital surface model (DSM) images, and first-hand optical remote sensing images of the disaster areas were obtained. Based on differential InSAR (D-InSAR), small baseline subset InSAR (SBAS-InSAR) and lifting rail fusion methods, the coseismic deformation field and deformation rate of the lifting rail direction were obtained by using Sentinel-1A data processing before and after the earthquake. Combined with optical interpretation, InSAR deformation, and field investigation, the results show that the deformation trend of the line of sight (LOS) images to the north and south of the ascending and descending orbits show an obvious opposite trend. The surface shape variables are −50 to 45 cm and −65 to 72 cm, respectively, and the deformation rate before the earthquake reached 25 cm/year. The deformation field characteristics show that the earthquake was mainly due to thrust, and the coseismic deformation field fractured along the WNW‒ESE direction with a length of approximately 33 km. The areas affected by 10 mm, 20 cm, and 50 cm deformation magnitudes in the whole earthquake area were 975.14, 321.10, and 38.55 km2, respectively. Within 20 km, there were two main affected townships, namely, Sujitan Township and Huangcheng Mongolian Township. Within 50 km, there were four main affected towns and townships, namely, Sujitan Township, Mongolian Township of the Imperial city, Qingshizui town, and Haomen town.

Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.

Landslide Susceptibility Evaluation of Bayesian Optimized CNN Gengma Seismic Zone Considering InSAR Deformation

Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

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.

Automatic Identification of Thaw Slumps Based on Neural Network Methods and Thaw Slumping Susceptibility

Thaw slumping is a periglacial process that occurs on slopes in cold environments, where the ground becomes unstable and the surface slides downhill due to saturation with water during thawing. In this study, GaoFen-1 remote sensing and fused multi-source feature data were used to automatically map thaw slumping landforms in the Beilu River Basin of the Qinghai–Tibet Plateau. The bi-directional cascade network structure was used to extract edges at different scales, where an individual layer was supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all convolutional neural network outputs. Additionally, we conducted a 5-year multi-scale feature analysis of small baseline subset interferometric synthetic aperture radar deformation, normalized difference vegetation index, and slope, among other features. Our study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfigured neural networks, ResNet101, VGG16, and ResNet152. Through verification using site surveys and multi-data fusion results, we obtained the best ResNet101 model score of intersection over union of 0.85 (overall accuracy of 84.59%).The value of intersection over union of the VGG and ResNet152 are 0.569 and 0.773, respectively. This work provides a new insight for the potential feasibility of applying the designed edge detection method to map diverse thaw slumping landforms in larger areas with high-resolution images.

Multiband 2D InSAR deformation models with error estimates from natural neighbour interpolation: Case study in the Latrobe Valley, Australia

A long term objective for Australia is to develop a continent-wide 2D deformation model based on satellite interferometric synthetic aperture radar (InSAR). Nation-wide InSAR processing has been demonstrated in European countries and Japan. However, there are some key differences to the Australian situation for a 2D model (i.e., vertical and east–west surface movement rates) where SAR data from ascending and descending satellite orbits are needed. The primary difference is that while ESA’s Sentinel-1 ascending and descending satellite observations underpin 2D models in other parts of the world, the Australian continent is only covered by descending Sentinel-1 SAR data in most areas. Combined with Australia’s large size of 7.63 million km2, this means a different approach is needed to realise 2D deformation models. In this article, we propose the novel approach of (1) combining supplementary multiband SAR (ascending X-band and L-band) with the descending C-band Sentinel-1 data, and (2) using natural neighbour (NN) interpolation as a computationally efficient interpolation method for combining the multiband InSAR data on common grids prior to 2D model computation. This is an alternative to the computationally intensive Ordinary Kriging (OK) approach. A disadvantage of NN compared to OK is that it does not compute an error estimate, which we address by deriving an error estimate for the NN interpolation so that errors are provided for each 2D rate to aid with their correct interpretation. To demonstrate our new approach, we compute experimental 2D deformation models that propagate the full error budget from InSAR, a GNSS connection to ITRF2014 and NN interpolation into an error grid in the Latrobe Valley in southeast Australia. The significance of our approach is that it provides a suitable combination of methods that may be used to develop 2D continental deformation models for Australia’s unique circumstances. Our results demonstrate the accuracy of NN and OK interpolation are at the same level (within error), and the error estimates from both interpolation techniques are also comparable: X band (TerraSAR-X) ∼ 1 mm/yr; C band (Sentinel-1) ∼ 2.5 mm/yr, and L band (ALOS PALSAR1) 4 mm/yr. For the first time, we reveal 2D deformation in the Latrobe Valley region showing horizontal east–west deformation in the area surrounding an above-ground mine and reaching 20 mm/yr, with vertical subsidence rates reaching −40 mm/yr.

Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.

Isolating the Poroelastic Response of the Groundwater System in InSAR Data From the Central Valley of California

AbstractWorking with 5 years (2015–2019) of high quality interferometric synthetic aperture radar (InSAR) data covering the Central Valley of California, we developed a new approach to isolate the poroelastic component in InSAR data by estimating and removing the hydrologic loading component. K‐means clustering was used to identify the InSAR deformation time series dominated by the loading response and those dominated by the poroelastic response. The former time series include seasonal oscillations of uplift and subsidence related to mass changes in snow and ice in the adjacent mountain range, while the latter include interannual deformation and seasonal oscillations related to changing head in the valley. The loading component accounts for, on average, 56 ± 12% of the deformation in the Central Valley. Without correcting for the loading response, the deformation due to the poroelastic response (and therefore any derived estimate of change in head) is underestimated by 50%–60% in areas of the valley.

Tectonic Implication of the 2022 MS 6.9 Earthquake in Menyuan, Qinghai, China: Analysis of Precise Earthquake Locations and InSAR.

Precise earthquake locations and InSAR (Interferometric Synthetic Aperture Radar) deformation observation are the major methods to understand the earthquake occurrence and disaster-causing process. This paper proposes a processing framework for analyzing strong earthquake mechanisms from one-dimensional velocity inversion to precise earthquake locations combined with InSAR deformation observation, and discusses earthquake-generating fault and dynamic mechanisms of tectonic deformation. We analyzed the Menyuan Ms 6.9 earthquake in 2022 and discuss the historical seismic activities and corresponding stress adjustment processes in the research region. To analyze and study the seismogenic structure and mechanism of the earthquake, we investigated the spatial and temporal distribution characteristics of the Menyuan earthquake sequence and analyzed the InSAR coseismic deformation field. We obtained the precise locations of the main shock and aftershocks and the coseismic InSAR deformation field of the main shock. It was confirmed that the Ms 6.9 earthquake was a shallow sinistral strike-slip earthquake, which led to the sequential activation of the Tuolaishan and Lenglongling faults. The main seismogenic fault of the mainshock was the northwestern end of the Lenglongling fault, and the earthquake rupture was segmented. It can be inferred that the earthquake was a stress-adjusted event triggered in the Qilian-Haiyuan tectonic belt caused by the northeasterly push of the Qinghai-Tibet Plateau. The risk of moderate to high earthquakes in the region remains high in the future, requiring enhanced seismic observations.

Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region

Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability.

MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland

Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain’s responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.

Double rupture event in the Tianshan Mountains: A case study of the 2021 Mw 5.3 Baicheng earthquake, NW China

Abstract On March 23, 2021 (21:14 universal time coordinated), an Mw 5.3 earthquake occurred in Baicheng County in Xinjiang, northwestern China, according to the United States Geological Survey. The earthquake produced a 4-km-long surface rupture at the epicenter, which is generally rare for earthquakes of magnitude 5.3. Thus, investigating the Baicheng earthquake is crucial for understanding the seismogenic structure of the region. We obtained the interferometric synthetic aperture radar deformation field and inverted the slip distribution of the Baicheng earthquake using Sentinel-1A satellite data and surface rupture data. The results indicate that the surface deformation area was elliptical, with long and short axes of approximately 20 and 10 km, respectively. The seismogenic structure is a left-lateral strike-slip fault with a small dip-slip component and strike and dip angles of 248° and 70°, respectively. Two other slip centers were also observed at 2 and 8 km beneath the surface in the dip direction. The maximum slip at 2 km was 0.45 m. Shear deformation between the Tarim Basin and Southern Tianshan Mountains was responsible for the strike-slip features of the Baicheng earthquake.

Earthquake-induced risk assessment of cultural heritage based on InSAR and seismic intensity: A case study of Zhalang temple affected by the 2021 Mw 7.4 Maduo (China) earthquake

Earthquakes have been acknowledged as threats to heritages for a long time in the past decades. Many scientific publications and international guidelines have addressed this issue. However, the related reports are still lacking particularly for the 2021 Mw 7.4 Maduo earthquake and its surrounding heritage sites, even though this event was the largest earthquake in China after 2008 with 13 National Key Protected Cultural Relic Units (NKPCRUs) of China located in the surrounding areas within 200 km from the epicenter. On May 21, 2021, an Mw 7.4 earthquake struck Maduo County in Qinghai Province, China. The earthquake was large in magnitude and caused ∼150 km of surface rupture. The integrity and authenticity of cultural heritages near the epicenter are under severe threat, making it critically important to quantify the potential damages and vulnerability of these sites affected by the Mw 7.4 earthquake. Nevertheless, many geological and geophysical studies related to this earthquake have been conducted, but none of them consider the seismic damage to the heritage sites. To fulfill this gap, the Zhalang temple (also known as “Golok Peaceful Liberation Memorial”) which was a Tibetan Buddhist monastery located ∼140 km away from the Mw 7.4 epicenter of the 2021 Maduo earthquake was taken as an example to investigate whether and how the strong earthquake could affect a cultural heritage in such a distance. Collaborative monitoring based on InSAR and seismic intensity was conducted to measure the earthquake-induced deformation and characterize the risks and damages to cultural heritages by earthquakes. First, fast responses within 30 min based on the seismic method were performed. The PGV-vS30 intensity, that was the peak ground velocities (PGV) further site-corrected by the site conditions of the time-averaged shear-wave velocity to 30 m depth (vS30) seismic intensity map was obtained, which provided a general and fast guidance to preliminary determinations of the severe disaster area and the scope of the disaster area. Initial decisions could be made about whether or where a certain location should be taken into further on-site inspection. Then, the Interferometric Synthetic Aperture Radar (InSAR) processing of Sentinel-1 images was performed to define two-dimensional (2D) East-West (E-W) and vertical co-seismic deformation maps near the Zhalang temple. Finally, the measurements obtained by seismic and InSAR methods were validated by unmanned aerial vehicle (UAV) imagery and field surveying. The results showed that: the maximum E-W striking deformation was ∼2.54 cm toward the east and the vertical subsidence was ∼0.01 cm centered on the Zhalang temple, suggesting that the E-W striking deformation significantly affected the surrounding region. Moreover, the spatial heterogeneity indicator of the InSAR deformation maps revealed the locations of underground faults with surface expressions. Further field survey detected surface fractures at Zhalang temple due to secondary effects of the Mw 7.4 earthquake. The genetic process of faulting and fracturing is crucial in terms of hazard assessment and mitigation strategies. In countries with strong environmental and seismic risks, the presented work contributes to the disaster risk reduction (DRR) of heritages in natural-hazard-prone areas and also takes on a social value because it provided supplements for the protection of a heritage that represents the very identity nationwide.

Source Model and Characteristics of the 27 July 2022 MW 7.0 Northwestern Luzon Earthquake, Philippines

The geometry and kinematics of the causative fault of the 27 July 2022 moment magnitude (Mw) 7.0 earthquake, which is one of the strongest to hit northern and central Luzon in the past 30 years, were estimated through inverse modeling of line-of-sight interferometric synthetic aperture radar deformation. We modeled rupture along multiple candidate faults based on fit with the pattern of line-of-sight deformation, consistency with focal mechanisms, and compatibility with the known kinematics of the mapped active faults in the region. Our preferred fault model, located west of and parallel to the Abra River Fault (ARF), exhibits localized reverse-slip (average 67° rake) at 15-35 km down-dip. Peak slip occurs at 13-16 km depth, with 95 cm of pure reverse-slip. The existence of a reverse-slip dominated ARF-parallel fault rupture is consistent with a complex shear partitioning model, wherein the NW-SE oblique plate convergence is accommodated not only by the sinistral strike-slip Philippine Fault Zone and the major subduction zones, but also by minor faults in intervening crustal blocks.

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

World-wide InSAR sensitivity index for landslide deformation tracking

• Sensitivity index: a priori assessment of InSAR geometry at any landslide. • 91% of the world’s slopes allow for displacement monitoring by InSAR. • Detection potential: assess the ability to extract InSAR deformation time series. • A Google Earth Engine app for world-wide semi-automated assessment of InSAR potential. Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture Radar (InSAR) has shown to be a valuable resource of deformation information, but it requires expert knowledge and considerable computational efforts. Moreover, the successful application of InSAR for landslides requires a favorable acquisition geometry relative to the landslide deformation pattern. Consequently, there is a need for a widely applicable tool to assess the potential of InSAR at a particular location a priori. Here we present a novel, generic approach to assess the potential of InSAR-based deformation tracking, providing a standardised and automated method applicable on any slope. We define the detection potential as the sensitivity of InSAR to detect downslope displacement combined with the presence of coherently scattering surfaces. We show that deformation can be detected on at least 91% of the global landslide-prone slopes, and provide an open source Google Earth Engine tool for the quick assessment of the availability of potential coherent scatterers. This tool enables any person interested in applying InSAR to routinely assess the potential for monitoring landslide deformation in their region of interest.

A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning

Land subsidence (LS), which mainly results from poor watershed management, is a complex and nonlinear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road or fault, rainfall, geology and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANN- and GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR deformation map, 39%, 20%, 25%, 13% and 3% of the investigated areas are subject to a yearly LS of −1 to −2.5, −2.5 to −5, −5 to −7.5, −7.5 to −10 and −10 to −20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km2) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future.