Multi-temporal InSAR Research Articles

Recent Ground Subsidence in the North China Plain, China, Revealed by Sentinel-1A Datasets

Groundwater resources have been exploited and utilized on a large scale in the North China Plain (NCP) since the 1970s. As a result of extensive groundwater depletion, the NCP has experienced significant land subsidence, which threatens geological stability and infrastructure health and exacerbates the risks of other geohazards. In this study, we employed multi-track Synthetic Aperture Radar (SAR) datasets acquired by the Sentinel-1A (S1A) satellite to detect spatial and temporal distributions of surface deformation in the NCP from 2016 to 2018 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. During the InSAR monitoring period, the temporal pattern of land subsidence was dominated by a decreasing tendency and the spatial pattern of land subsidence in the coastal plain exhibited an expansion trend. Validation results show that the S1A datasets agree well with levelling data, indicating the reliability of the InSAR results. With groundwater level data, we found that the distribution of subsidence in the NCP is spatially consistent with that of deep groundwater depression cones. A comparison with land use data shows that the agricultural usage of groundwater is the dominant mechanism responsible for land subsidence in the whole study area. Through an integrated analysis of land subsidence distribution characteristics, geological data, and previous research results, we found that other triggering factors, such as active faults, precipitation recharge, urbanization, and oil/gas extraction, have also impacted land subsidence in the NCP to different degrees.

Model-Free Characterization of SAR MTI Time Series

Multitemporal interferometric synthetic aperture radar (MTI) displacement time series are usually characterized by the model-dependent temporal phase coherence as a quality measure. Additional tests have to be performed to recognize other “interesting” but nonmodeled trends, and several automated approaches to this task have been proposed to date. We introduce here the fuzzy entropy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{E}$ </tex-math></inline-formula> ), a measure introduced in medical data analysis, as a viable parameter to characterize MTI time series. Being a measure of disorder in a time series, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{E}$ </tex-math></inline-formula> exhibits homogeneously low values for a large class of displacement models, such as seasonal, parabolic, or piecewise linear signals, while increasing for more chaotic trends, dominated by noise. It appears therefore suited as a discriminative parameter to isolate meaningful MTI time series within large data sets, without specifying a predefined model. The calculation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{E}$ </tex-math></inline-formula> has low computational cost and can thus be easily performed as a prescreening filter. In this letter, results over simulated data and some examples on a real data set are shown with interesting performances which hint to possible large-scale implementations.

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

Land subsidence, as one of the engineering geological problems in the world, is generally caused by compression of unconsolidated strata due to natural or anthropogenic activities. We employed interferometric point target analysis (IPTA) as a multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique on ascending and descending Sentinel-1A the terrain observation with progressive scans SAR (TOPSAR) images acquired between January 2015 and December 2018 to analyze the spatio-temporal distribution and cause of subsidence in Abbottabad City of Pakistan. The line of sight (LOS) average deformation velocities along ascending and descending orbits were decomposed into vertical velocity fields and compared with geological data, ground water pumping schemes, and precipitation data. The decomposed and averaged vertical velocity results showed significant subsidence in most of the urban areas in the city. The most severe subsidence was observed close to old Karakorum highway, where the subsidence rate varied up to −6.5 cm/year. The subsidence bowl profiles along W–E and S–N transects showed a relationship with the locations of some water pumping stations. The monitored LOS time series histories along an ascending orbit showed a close correlation with the rainfall during the investigation period. Comparative analysis of this uneven prominent subsidence with geological and precipitation data reflected that the subsidence in the Abbottabad city was mainly related to anthropogenic activities, overexploitation of water, and consolidation of soil layer. The study represents the first ever evidence of land subsidence and its causes in the region that will support the local government as well as decision and policy makers for better planning to overcome problems of overflowing drains, sewage system, littered roads/streets, and sinking land in the city.

The Relationship between Surface Displacement and Groundwater Level Change and Its Hydrogeological Implications in an Alluvial Fan: Case Study of the Choshui River, Taiwan

Balancing the demand of groundwater resources and the mitigation of land subsidence is particularly important, yet challenging, in populated alluvial fan areas. In this study, we combine multiple monitoring data derived from Multi-Temporal InSAR (MTI), GNSS (Global Navigation Satellite System), precise leveling, groundwater level, and compaction monitoring wells, in order to analyze the relationship between surface displacement and groundwater level change within the alluvial fan of the Choshui River in Taiwan. Our combined time-series analyses suggest, in a yearly time scale, that groundwater level increases with the vertical surface displacement when the effect of pore water pressure dominates. Conversely, this relationship is negative when the effect of water-mass loading predominates over pore water pressure. However, the correlation between the vertical surface displacement and the groundwater level change is consistently positive over the time scale of two decades. It is interpreted that the alluvial fan sequence in the subsurface is not fully elastic, and compaction is greater than rebound in this process. These findings were not well reported and discussed by previous studies because of insufficient monitoring data and analyses. Understanding the combined effect of groundwater level change and vertical surface displacement is very helpful for management of land subsidence and usage of groundwater resources. The spatial and temporal integration of multi-sensors can be applied to overcome the limitations associated with the single technique and provides further insights into land surface changes, particularly in highly populated alluvial fan areas.

Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece

The Methana volcano in Greece belongs to the western part of the Hellenic Volcanic Arc, where the African and Eurasian tectonic plates converge at a rate of approximately 3 cm/year. While volcanic hazard in Methana is considered low, the neotectonic basin constituting the Saronic Gulf area is seismically active and there is evidence of local geothermal activity. Monitoring is therefore crucial to characterize any activity at the volcano that could impact the local population. This study aims to detect surface deformation in the whole Methana peninsula based on a long stack of 99 Sentinel-1 C-band Synthetic Aperture Radar (SAR) images in interferometric wide swath mode acquired in March 2015–August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using the Interferometric Point Target Analysis (IPTA) method, involving the extraction of a network of targets including both Persistent Scatterers (PS) and Distributed Scatterers (DS) to augment the monitoring capability across the varied land cover of the peninsula. Satellite geodetic data from 2006–2019 Global Positioning System (GPS) benchmark surveying are used to calibrate and validate the MT-InSAR results. Deformation monitoring records from permanent Global Navigation Satellite System (GNSS) stations, two of which were installed within the peninsula in 2004 (METH) and 2019 (MTNA), are also exploited for interpretation of the regional deformation scenario. Geological, topographic, and 2006–2019 seismological data enable better understanding of the ground deformation observed. Line-of-sight displacement velocities of the over 4700 PS and 6200 DS within the peninsula are from −18.1 to +7.5 mm/year. The MT-InSAR data suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes. In Methana town, ground stability is found on volcanoclasts and limestone for the majority of the urban area footprint while some deformation is observed in the suburban zones. At the Mavri Petra andesitic dome, time series of the exceptionally dense PS/DS network across blocks of agglomerate and cinder reveal seasonal fluctuation (5 mm amplitude) overlapping the long-term stable trend. Given the steepness of the slopes along the eastern flank of the volcano, displacement patterns may indicate mass movements. The GNSS, seismological and MT-InSAR analyses lead to a first account of deformation processes and their temporal evolution over the last years for Methana, thus providing initial information to feed into the volcano baseline hazard assessment and monitoring system.

Application of Time-Series Sentinel-1A for Land Deformation in Central Aceh, Indonesia

Several decorrelation phenomena of Interferometric Synthetic Aperture Radar (InSAR) have led researchers to develop various multitemporal InSAR (MT-InSAR) techniques with the application of time series/stack of images. In this study, we present the land surface movement monitoring using MT-InSAR techniques in the Central of Aceh-Indonesia, and focus on the temporal and spatial pattern of uplift and subsidence by using multi InSAR methods such as Quasi-Persistent Scatterer (Q-PS) and Small Baseline Subset (SBAS). A total of 18 scenes of Sentinel-1A(S-1A) and 14 scenes of ALOS PALSAR-1(PALSAR-1) images were acquired between 2018 and 2019, as well as 2007 and 2010, where then the multitemporal methods and techniques were applied sequentially to a set of those data. The results showed that the either the S-1A and PALSAR-1 velocity subsidence at Nunang and Musara Alun villages were range from 2.4 to 5.7 and 0.6 to 2.3 mm/year, respectively which corresponded to the results obtained by other research publication. The deformation in Central Aceh needs continuous monitoring using InSAR as the place is a landslide prone area because of the nature of the terrain.

Automatic Detection of Volcanic Surface Deformation Using Deep Learning

AbstractInterferometric Synthetic Aperture Radar (InSAR) provides subcentimetric measurements of surface displacements, which are key for characterizing and monitoring magmatic processes in volcanic regions. The abundant measurements of surface displacements in multitemporal InSAR data routinely acquired by SAR satellites can facilitate near real‐time volcano monitoring on a global basis. However, the presence of atmospheric signals in interferograms complicates the interpretation of those InSAR measurements, which can even lead to a misinterpretation of InSAR signals and volcanic unrest. Given the vast quantities of SAR data available, an automatic InSAR data processing and denoising approach is required to separate volcanic signals that are cause of concern from atmospheric signals and noise. In this study, we employ a deep learning strategy that directly removes atmospheric and other noise signals from time‐consecutive unwrapped surface displacements obtained through an InSAR time series approach using an end‐to‐end convolutional neural network (CNN) with an encoder‐decoder architecture, modified U‐net. The CNN is trained with simulated synthetic unwrapped surface displacement maps and is then applied to real InSAR data. Our proposed architecture is capable of detecting dynamic spatio‐temporal patterns of volcanic surface displacements. We find that an ensemble‐average strategy is recommended to stabilize detected results for varying deformation rates and signal‐to‐noise ratios (SNRs). A case study is also presented where this method is applied to InSAR data covering Masaya volcano, Nicaragua and the results are validated using continuous GPS data. The results confirm that our network can indeed efficiently suppress atmospheric and other noise to reveal the noise‐free surface deformation.

Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods

Multi-temporal InSAR (MT-InSAR) methods have been widely used in remote sensing monitoring of ground subsidence, which occurs at many places around the world. Land subsidence, caused by excessive extraction of groundwater, has always been a problem to be solved in Tianjin, China. Although the subsidence in the urban area has been controlled at a low rate, the subsidence issue has not been effectively solved in the suburban area recently, which should be paid much attention. This paper aims to present two multi-temporal differential interferometry techniques, persistent scatterer (PS) and small baseline subset (SBAS), for monitoring the latest surface subsidence in a Tianjin study area on the basis of 20 Sentinel-1A images obtained from March 2017 to March 2019. Our research showed that the average velocity map obtained from the SBAS method closely followed the outcomes of the PS technique from the perspective of identifying similar subsidence patterns. Subsidence rate gradually increased from the urban area of Tianjin to the suburbs and high subsidence zones were mainly distributed at the junction of the Wuqing, Xiqing and Beichen districts. In the past two years, the annual average subsidence rate in the high settlement area mostly exceeded −50 mm/year, which caused serious damage to local infrastructures. Besides, high-resolution remote sensing images combined with field investigations further verified the successful application of MT-InSAR technology in Tianjin’s subsidence monitoring. Effective ground subsidence control measures need to be taken as soon as possible to prevent the situation from getting worse.

INCORPORATING INDEPENDENT COMPONENT ANALYSIS AND MULTI-TEMPORAL SAR TECHNIQUES TO RETRIEVE RAPID POSTSEISMIC DEFORMATION

Abstract. This study investigates the ongoing postseismic deformation induced by two moderate mainshocks of Mw 6.1 and Mw 6.0, 2017 Hojedk earthquake in Southern Iran. Available Sentinel-1 TOPS C-band Synthetic Aperture Radar (SAR) images over about one year after the earthquakes are used to analyze the postseismic activities. An adaptive method incorporating Independent Component Analysis (ICA) and multi-temporal Small BAseline Subset (SBAS) Interferometric SAR (InSAR) techniques is proposed and implemented to recover the rapid deformation. This method is applied to the series of interferograms generated in a fully constructed SBAS network to retrieve the postseismic deformation signal. ICA algorithm uses a linear transformation to decompose the input mixed signal to its source components, which are non-Gaussian and mutually independent. This analysis allows extracting the low rate postseismic deformation signal from a mixture of interferometric phase components. The independent sources recovered from the multi-temporal InSAR dataset are then analyzed using a group clustering test aiming to identify and enhance the undescribed deformation signal. Analysis of the processed interferograms indicates a promising performance of the proposed method in determining tectonic deformation. The proposed method works well, mainly when the tectonic signal is dominated by the undesired signals, including atmosphere or orbital/unwrapping noise that counts as temporally uncorrelated components.In contrast to the standard SBAS time series method, the ICA-based time series analysis estimates the cumulative deformation with no prior assumption about elevation dependence of the interferometric phase or temporal nature of the tectonic signal. Application of the method to 433 Sentinel-1 pairs within the dataset reports two distinct deformation patches corresponding to the postseismic deformation. Besides the performance of the ICA-based analysis, the proposed method automatically detects rapid or low rate tectonic processes in unfavorable conditions.

Review on wetland water level monitoring using interferometric synthetic aperture radar

Water level is an important indicator of wetland hydrological regime. Detection of wetland water levels through interferometric synthetic aperture radar (InSAR) has outstanding advantage, including high spatial resolution, high accuracy, low cost, and high efficiency. We introduced prerequisites for the monitoring of wetland water levels with InSAR, discussed the types of InSAR techniques, the influencing factors for monitoring wetland water levels and their advantages and disadvantages. There are three prerequisites for effectively detecting wetland water levels with InSAR techniques: 1) the presence of emergent aquatic plants; 2) the main backscattering mechanism is double bounce scattering; and 3) the interferometric coherence exceeds a certain threshold. Current water level monitoring techniques have been developed from traditional InSAR techniques to advanced InSAR techniques, such as STBAS, MM, and DSI. These techniques evolve from detecting relative water level changes to estimate absolute water level and water depth time series. The influencing factors of InSAR techniques for monitoring wetland water levels include operating para-meters of the synthetic aperture radar (SAR) and characteristics of the wetlands themselves. Finally, we proposed the key directions for future research in this field: i) investigating the potential use of specific water level monitoring techniques in other regions with different backscattering and interferometric coherence characteristics; ii) developing new algorithms to integrate multi-sensor, multi-track, multi-band, multi-polarization, and multi-temporal InSAR repeat-pass observation; iii) considering alternative sources of SAR data; and (iv) strengthening research on "by-products" of wetland water level monitoring with InSAR, such as wetland hydrological connectivity, flow direction, and flow regime.

Tracking California's sinking coast from space: Implications for relative sea-level rise.

Coastal vertical land motion affects projections of sea-level rise, and subsidence exacerbates flooding hazards. Along the ~1350-km California coastline, records of high-resolution vertical land motion rates are scarce due to sparse instrumentation, and hazards to coastal communities are underestimated. Here, we considered a ~100-km-wide swath of land along California's coast and performed a multitemporal interferometric synthetic aperture radar (InSAR) analysis of large datasets, obtaining estimates of vertical land motion rates for California's entire coast at ~100-m dimensions-a ~1000-fold resolution improvement to the previous record. We estimate between 4.3 million and 8.7 million people in California's coastal communities, including 460,000 to 805,000 in San Francisco, 8000 to 2,300,00 in Los Angeles, and 2,000,000 to 2,300,000 in San Diego, are exposed to subsidence. The unprecedented detail and submillimeter accuracy resolved in our vertical land motion dataset can transform the analysis of natural and anthropogenic changes in relative sea-level and associated hazards.

Two decades of settlement of Hong Kong International Airport measured with multi-temporal InSAR

The Hong Kong International Airport (HKIA) was constructed on a platform mainly reclaimed from the sea. The platform has experienced some significant settlement since its operation in 1998 due mainly to consolidation of the materials used in the land reclamation. Although some studies have been carried out to measure the settlement with techniques such as Interferometric Synthetic Aperture Radar (InSAR), the past studies have each only covered some limited time periods. Therefore, a complete history of settlement since the operation of the Airport has never been available to aid the understanding of the spatiotemporal behavior of the land settlement. We attempt for the first time to make full use of most of the available SAR data from multiple SAR sensors (ERS-2, ENVISAT ASAR, COSMO-SkyMed (CSK), and Sentinel-1A) to generate a settlement time series of the HKIA over 1998–2018 with an improved multi-temporal InSAR technique. In order to fill the time gaps between the different SAR datasets, a settlement model is developed based on the InSAR measurements. The results reveal both the spatial and temporal variations of the land settlement. They also show for the first time that the accumulative settlement reaches up to 40 cm over the past two decades. The settlement is largely associated with the material types used in the landfill works and the underlying alluvial sediments as some earlier research has indicated, and the stages of the reclamation works. The results are validated through cross-validation between the datasets and with leveling and GPS measurements on the Airport platform.

An Improved Multi-Sensor MTI Time-Series Fusion Method to Monitor the Subsidence of Beijing Subway Network during the Past 15 Years

Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature.

RemotIO: A Sentinel-1 Multi-Temporal InSAR Infrastructure Monitoring Service with Automatic Updates and Data Mining Capabilities

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is nowadays a well-developed remote sensing technique for monitoring of Earth’s surface deformation. The availability of regular and open Copernicus Sentinel-1 satellite data with enhanced spatiotemporal coverage has recently stimulated several initiatives for development of new monitoring services which can help to respond to emergencies faster and apply resilience measures more accurately as compared to conventional ground-based techniques. In this paper, the alpha version of the remotIO (Retrieval of Motions and Potential Deformation Threats) system is presented. It is currently able to provide continuous and autonomous updates of MT-InSAR results and post-processing methodology over sites with active deformation hazards to ease the interpretation and facilitate decision-supporting tools for on-time situational awareness. Our post-processing approach implemented in remotIO’s web application has proven to be useful in filtering the resultant deformation maps and in pinpointing problematic zones with potential ground deformation threats also over low-coherent areas.

Wide-area observations of surface deformation in Mexican urban areas and geothermal fields using ENVISAT InSAR

There is an increasingly growing literature on the use of advanced multi-temporal Interferometric Synthetic Aperture Radar (InSAR) to monitor surface deformation with millimeter precision in urban and rural environments, including applications over wide areas spanning hundreds to thousands of square kilometers. In this work, we survey the eastern sector of the Trans-Mexican Volcanic Belt, extending from the state of Hidalgo, through Tlaxcala, Puebla and up to Veracruz, for a total of 19,000 km2. The surveyed area includes both the geothermal exploration site of the Acoculco caldera complex, and Los Humeros geothermal field, one of the largest geothermal exploitation sites of Mexico. Using the multi-temporal Small BAseline Subset (SBAS) InSAR method, we process the whole ENVISAT Advanced SAR (ASAR) IS2 image archive acquired in 2003-2010. Estimated subsidence rates are as high as 3.1 cm/year within the region, and a number of land deformation hotspots are identified within the urban areas of Tulancingo, Tlaxcala and Apizaco, the Tecamachalco valley, as well as within Los Humeros caldera. The surveying of such a scientifically interesting test region provides a proof of concept on how to investigate natural and anthropogenic processes in urban and rural areas using big SAR datasets and InSAR methods.

Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR

Landslides are one of the natural hazards that occur annually in Indonesia. A continuous geodetic observation in the landslide prone area is essential to support the precautionary measures. Because of its hilly topography, torrential rainfall and landslide history, the Ciloto district in Indonesia has been affected by ground deformation for an extended period of time. The purpose of our study is to detect significant movement and quantify the kinematics of its motion using the Interferometric synthetic aperture radar (InSAR) time series analysis and multi-band SAR images. We utilized the small baseline SDFP technique for processing multi-temporal SAR data, comprising ERS1/2 (1998–1999), ALOS PALSAR (2007–2009), and Sentinel-1 (2014–2018). Based on the detected deformation signal in the Ciloto area, the displacement rates are categorized as very slow movements. Two active main landslide zones; the Puncak Pass and the Puncak Highway area, which show the trend of slow movement progressively increasing or descreasing, were detected. The integration of the velocity rate between InSAR results and ground observations (e.g., terrestrial and GPS) was conducted at the Puncak Highway area from the temporal perspective. Using the polynomial model, we estimated that the area had cumulatively displaced up to −42 cm for 25 years and the type of movements varied from single compound to multiple rotational and compound.

IoT technology and big data processing for monitoring and analysing land subsidence in Central Taiwan

Abstract. Over 1992–2018, groundwater overexploitation had caused large-scale land subsidence in the Choshui River Alluvial Fan (CRAF) in Taiwan. The Taiwan High Speed Railway (THSR) passes through an area of severe subsidence in CRAF, and the subsidence poses a serious threat to its operation. How to effectively monitor land subsidence here has become a major issue in Taiwan. In this paper, we introduce a multiple-sensor monitoring system for land subsidence, including 50 continuous operation reference stations (CORS), multi temporal InSAR (MT-InSAR), a 1000 km levelling network, 34 multi-layer compaction monitoring wells and 116 groundwater monitoring wells. This system can monitor the extent of land subsidence and provide data for studying the mechanism of land subsidence. We use the Internet of Things (IoT) technology to control and manage the sensors and develop a bigdata processing procedure to analyse the monitoring data for the system of sensors. The procedure makes the land subsidence monitoring more efficient and intelligent.

An overview of the land subsidence phenomena occurring in Greece, triggered by the overexploitation of the aquifers for irrigation and mining purposes

Abstract. Land subsidence caused by overexploitation of aquifers manifests with an increasing frequency in several regions of Greece. The first signs of land subsidence have been identified since 1965 at the west side of Thessaloniki (broader Kalochori village region), in the form of a progressive marine invasion. Since then several areas have been investigated and proved to be affected mainly by the overexploitation of the aquifers. The Multidisciplinary nature of the subsidence mechanism combining the geological, hydrogeological and morphological setting of the areas with the human activities and the land use data makes their study complicated and require the intervention of multiple scientific specialties. Furthermore, at several sites, beside ground truth data, the land subsidence trends and extends have been also identified via multi-temporal InSAR techniques, such as PSI and SBAS techniques. Among the sites to be presented in the current paper is the wider area of Kalochori village in the eastern part of Thessaloniki plain, the east and west Thessaly plain in central Greece and the region extending to the west–southwest of the Amyntaio opencast coalmine in West Macedonia.

The Multiple Aperture SAR Interferometry (MAI) Technique for the Detection of Large Ground Displacement Dynamics: An Overview

This work presents an overview of the multiple aperture synthetic aperture radar interferometric (MAI) technique, which is primarily used to measure the along-track components of the Earth’s surface deformation, by investigating its capabilities and potential applications. Such a method is widely used to monitor the time evolution of ground surface changes in areas with large deformations (e.g., due to glaciers movements or seismic episodes), permitting one to discriminate the three-dimensional (up–down, east–west, north–south) components of the Earth’s surface displacements. The MAI technique relies on the spectral diversity (SD) method, which consists of splitting the azimuth (range) Synthetic Aperture RADAR (SAR) signal spectrum into separate sub-bands to get an estimate of the surface displacement along the azimuth (sensor line-of-sight (LOS)) direction. Moreover, the SD techniques are also used to correct the atmospheric phase screen (APS) artefacts (e.g., the ionospheric and water vapor phase distortion effects) that corrupt surface displacement time-series obtained by currently available multi-temporal InSAR (MT-InSAR) tools. More recently, the SD methods have also been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. This work is primarily devoted to illustrating the underlying rationale and effectiveness of the MAI and SD techniques as well as their applications. In addition, we present an innovative method to combine complementary information of the ground deformation collected from multi-orbit/multi-track satellite observations. In particular, the presented technique complements the recently developed Minimum Acceleration combination (MinA) method with MAI-driven azimuthal ground deformation measurements to obtain the time-series of the 3-D components of the deformation in areas affected by large deformation episodes. Experimental results encompass several case studies. The validity and relevance of the presented approaches are clearly demonstrated in the context of geospatial analyses.

An Improved Two-Step Multitemporal SAR Interferometry Method for Precursory Slope Deformation Detection Over Nanyu Landslide

A major landslide in Nanyu township, Zhouqu county, Western China, occurred on July 12, 2018, causing widespread damage and blocking the Bailong river. As a widely used surface deformation measurement technique, persistent scatterer interferometry (PSI) method fails in the precursory slope deformation detection over Nanyu landslide for two reasons: the high rate slope deformation of the landslide body before the occurrence of the disaster and the low density of measurement points in the widespread nonurban regions over hillside area. To solve these problems, a two-step multitemporal interferometric synthetic aperture radar (MTInSAR) method is proposed in this letter. In the proposed method, first, a carefully designed small baseline subsets (SBASs) method is applied to extract the linear high rate deformation component, which is then subtracted from the wrapping interferogram to reduce the likelihood of signal aliasing. In the second step, a two-tier network-based MTInSAR method by exploiting both persistent scatterers (PSs) and distributed scatterers (DSs) is employed to retrieve the residual deformation. The proposed method is applied to 47 Sentinel-1 images acquired between December 2016 and July 2018 and successfully detects the precursory deformation over Nanyu landslide for the first time. Experiment results illustrate the advantage of the proposed method for measuring high rate deformation compared with the traditional PSI. By using the proposed algorithm, the deformation rate map shows a clear landslide boundary and precursors of the rapid slope movements with a maximum displacement rate of 483 mm/year along the line of sight of the satellite.