Surface Deformation Monitoring Research Articles

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

InSAR-Based Early Warning Monitoring Framework to Assess Aquifer Deterioration

Aquifer surveillance is key to understanding the dynamics of groundwater reservoirs. Attention should be focused on developing strategies to monitor and mitigate the adverse consequences of overexploitation. In this context, ground surface deformation monitoring allows us to estimate the spatial and temporal distribution of groundwater levels, determine the recharge times of the aquifers, and calibrate the hydrological models. This study proposes a methodology for implementing advanced multitemporal differential interferometry (InSAR) techniques for water withdrawal surveillance and early warning assessment. For this, large open-access images were used, a total of 145 SAR images from the Sentinel 1 C-band satellite provided by the Copernicus mission of the European Space Agency. InSAR processing was carried out with an algorithm based on parallel computing technology implemented in cloud infrastructure, optimizing complex workflows and processing times. The surveillance period records 6-years of satellite observation from September 2016 to December 2021 over the city of Chillan (Chile), an area exposed to urban development and intensive agriculture, where ~80 wells are located. The groundwater flow path spans from the Andes Mountain range to the Pacific Ocean, crossing the Itata river basin in the Chilean central valley. InSAR validation measurements were carried out by comparing the results with the values of continuous GNSS stations available in the area of interest. The performance analysis is based on spatial analysis, time series, meteorological stations data, and static level measurements, as well as hydrogeological structure. The results indicate seasonal variations in winter and summer, which corresponds to the recovery and drawdown periods with velocities > −10 mm/year, and an aquifer deterioration trend of up to 60 mm registered in the satellite SAR observation period. Our results show an efficient tool to monitor aquifer conditions, including irreversible consolidation and storage capacity loss, allowing timely decision making to avoid harmful exploitation.

Remote Sensing Monitoring and Analysis of Jinwuco Lateral Moraine Landslide-Glacial Lake Outburst in Southeast Tibet

On 25 June 2020, a glacial lake outburst flood (GLOF) occurred in Jinwuco, Nidou Zangbo, and southeast Tibet, causing catastrophic damage to multiple infrastructures such as roads, bridges, and farmlands in the surrounding and downstream areas. Due to the lack of long-term monitoring of glacial lake and glacier changes in the region and the surrounding surface, the spatial and temporal evolutionary characteristics and triggering factors of the disaster still need to be determined. Here, we combine multi-temporal optical remote sensing image interpretation, surface deformation monitoring with synthetic aperture radar (SAR)/InSAR, meteorological observation data, and corresponding soil moisture change information to systematically analyze the spatial and temporal evolution characteristics and triggering factors of this GLOF disaster. Optical images taken between 1987 and 2020 indicate that the glacial lake’s initial area of 0.39 km2 quickly grew to 0.56 km2, then plummeted to 0.26 km2 after the catastrophe. Meanwhile, we found obvious signs of slippage beside the lateral moraine at the junction of the glacier’s terminus and the glacial lake. The pixel offset tracking (POT) results based on SAR images acquired before and after the disaster reveal that the western lateral moraine underwent a 40 m line of sight (LOS) deformation. The small baseline subset InSAR (SBAS-InSAR) results from 2017 to 2021 show that the cumulative deformation of the slope around the lateral moraine increased in the rainy season before the disaster, with a maximum cumulative deformation of −52 mm in 120 days and gradually stabilized after the disaster. However, there are three long-term deformation areas on the slope above it, showing an increasing trend after the disaster, with cumulative deformation exceeding −30 mm during the monitoring period. The lateral moraine collapse occurred in a warm climate with continuous and intense precipitation, and the low backscatter intensity prior to the slide suggests that the soil was very moist. Intense rainfall is thought to be the catalyst for lateral moraine collapse, whereas the lateral moraine falling into the glacier lake is the direct cause of the GLOF. This study shows that the joint active–passive remote sensing technique can accurately obtain the spatial and temporal evolution characteristics and triggering factors of GLOF. It is helpful to understand the GLOF event caused by the slide of lateral moraine more comprehensively, which is essential for further work related to glacial lake hazard assessment.

Ground infrastructure monitoring in coastal areas using time-series inSAR technology: the case study of Pudong International Airport, Shanghai

ABSTRACT Shanghai Pudong International Airport (PDIA), with its east side built along the coast with weak geological conditions, is prone to uneven foundation settlement due to the consolidation and compression of soil and erosion of coastal tides, affecting the safe operation of the airport. Therefore, it is crucial to conduct dynamic subsidence monitoring within the airport, especially in the runway area. 29 scenes of ascending track Sentinel-1A radar images from August 2016 to June 2018 are selected to perform surface deformation inversion based on PS-InSAR and improved SBAS-InSAR for PDIA and its around coastal area. Through cross-validation, the reliability of the time-series InSAR technique for dynamic monitoring of surface deformation of coastal zone infrastructures is confirmed. The results show severely uneven settlement. By combining the monitoring results with the local geological and hydrological dataset, the driving factors of differential deformation of the infrastructures are analyzed, including stratigraphic geological conditions, ground loadings, foundation treatment methods, water erosion, and groundwater level changes. Finally, the time-series deformation characteristics and the causes of PDIA's runway are emphasized based on the PS deformation results. This case provides a reference for the safety management of critical infrastructure in coastal areas using advanced InSAR technique.

Effectiveness evaluation of DS-InSAR method fused PS points in surface deformation monitoring: a case study of Hongta District, Yuxi City, China

InSAR technology provides a powerful tool for detecting large-scale surface deformation. In particular, the newly developed DS-InSAR method fused PS points has obvious advantages in monitoring bare land and vegetation covered areas, but at present, there is a lack of effectiveness evaluation of this method. Therefore, this paper takes Hongta District of Yuxi City as an example. The 29 scenes sentinel-1A data from January 2019 to December 2019 were processed and analyzed using the DS-InSAR method fused PS points. The research results show that the position and deformation trend of the inversion results of the three methods are highly consistent, the correlation between DS-InSAR method fused PS points and PS- InSAR and SBAS-InSAR methods is 0.9473 and 0.8583, respectively. But the spatial density of the measurement points (MPs) obtained by the DS-InSAR method of PS points is 11 times and 4 times that of the PS-InSAR method and SBAS-InSAR method respectively. It clearly shows that DS-InSAR method fused PS points has greater advantages than PS-InSAR and SBAS-InSAR methods in deformation mapping displacement, because the MPs density is higher. It is conducive to the detailed analysis of the spatio-temporal characteristics and deformation mechanism of deformation.

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.

Dynamic pressure surface deformation measurement based on a chromatic confocal sensor.

To enable the real-time measurement of pressure deformations in sealed cavities, a high-precision method of detecting the deformation of a material surface is proposed. By combining a chromatic confocal displacement sensor with a pressure sensor, we can acquire dynamic online strain measurements that consider the effects of the deformed material and internal environmental conditions. A 90m m×90m m static mechanical cylindrical cavity is simulated using finite element software. The interior of the cylindrical cavity is continuously pressurized at up to 400kPa with a material deformation of 300µm. We experimentally obtain the spectral peak wavelengths corresponding to the surface deformation experiment, record the spectral data at 20kPa intervals, and use the Voigt fitting algorithm to reduce sensor errors. The results show that the experimental results differ from the simulated results by 1.43µm, with a relative error of 0.083% after sequential pressurization and depressurization using a pressure calibrator, and the uncertainty error of pressure deformation measurement is 1.495µm. Thus, the proposed method is robust against external disturbances and is suitable for micrometer-level surface deformation monitoring, which has numerous applications in the high-precision inspection industry.

Inversion of terrestrial water storage changes from GNSS vertical displacements using a priori constraint: A case study of the Yunnan Province, China

Global Navigation Satellite System (GNSS) is an effective means to monitor surface deformations associated with changes in terrestrial water storage (TWS). In this study, we introduced a priori constraint matrix and estimated the regularization parameter through an iterative least-squares method to infer the TWS changes using GNSS vertical displacements from December 2010 to February 2021 in the Yunnan Province (YNP), China. The GNSS-inferred TWS changes were validated through the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) estimates, the Global Land Data Assimilation System (GLDAS) hydrological model, and the meteorological data (i.e., precipitation and Drought Severity Index (DSI)). The results demonstrate that the spatial resolution of TWS changes derived from 45 GNSS stations can reach 2°×2° in the YNP, and the a priori constraint is better than the traditional Laplacian matrix constraint for solving the discrete ill-conditioned problem of GNSS inversion. The GNSS-inferred TWS changes are consistent with the TWS changes derived from GRACE/GFO and GLDAS estimates in the spatio-temporal domains over the YNP, but the GNSS inversion results show stronger amplitudes. Furthermore, the ground-based GNSS observations are more sensitive to the TWS changes, and the correlation between precipitation and GNSS-inferred TWS changes is improved by about 11 % and 5 % compared to GRACE/GFO and GLDAS estimates in the YNP, respectively. The GNSS-inferred DSI can well reveal the two severe drought events in the YNP during the study period, which is consistent with the DSI derived from GRACE/GFO and GLDAS estimates, as well as the published self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The GNSS observations can compensate the limitations of GRACE/GFO observations (e.g., bridging the data gap between GRACE and GFO missions), and help to better investigate the TWS changes in the YNP, which has significant potentials for regional water resource management and extreme climate changes monitoring.

Identification of Potential Landslide Hazards Using Time-Series InSAR in Xiji County, Ningxia

Potential landslide identification and monitoring are essential to prevent geological disasters. However, in mountainous areas where the surface gradient changes significantly, the leveling effect is not completely removed, affecting the deformation results. In this paper, the SBAS-InSAR and PS-InSAR time-series processing methods were combined to interfere with the SAR image data of the ascending orbit in the southern mountainous area of Ningxia and its surrounding regions. Based on the obtained surface deformation monitoring results and optical images, landslide hazard identification was successfully carried out within the coverage area of 3130 km2 in Xiji County. The results show that the whole study area presented a relatively stable state, most of the deformation rates were concentrated in the range of 0 mm/a to −10 mm/a, and the deformation in the southwest area was larger. A total of 11 large potential landslides (which were already registered potential danger points of geological disasters) were identified in the study area, including three historical collapses. The landslide identification results were highly consistent with the field survey results after verification. The timing analysis of the typical landslide point of the Jiaowan landslide was further carried out, which showed that the Jiaowan landslide produced new deformation during the monitoring time, but it was still in a basically stable state. It can do a good job in disaster prevention and reduction while strengthening monitoring. The results of this study have a guiding effect on landslide prevention and mitigation in the mountainous areas of southern Ningxia.

SAR interferometry for landslide investigation and surface deformation monitoring: a case study of Souk Ahras area, north-east of Algeria

SAR interferometry for landslide investigation and surface deformation monitoring: a case study of Souk Ahras area, north-east of Algeria

МОНІТОРИНГ ВЕРТИКАЛЬНИХ ЗМІЩЕНЬ ЗЕМНОЇ ПОВЕРХНІ ПРАВОБЕРЕЖЖЯ КАНІВСЬКОГО ВОДОСХОВИЩА

Monitoring of vertical displacements of the earth's surface on the right bank of the Kaniv Reservoir in the Vytatchiv-Stajky region was carried out using the differential radar interferometry (DinSAR) technique. According to the results of observations during the spring period from 2015 to 2023, active landslide areas are displayed. The DinSAR method is briefly described, emphasising data filtering steps for landslide activation mapping. A high data correlation was recorded, and the presence of zones of active shifts on the shore of the reservoir was confirmed. The spread and intensity of landslide manifestations are determined by the peculiarities of the geological and geomorphological structure of the territory, its tectonic, neotectonic and seismic regimes, and hydrological and climatic conditions. Limitations of the method arising from the temporal decorrelation of the radar signal in regions with a large amount of vegetation are determined. Due to the significant decorrelation between the obtained results of forested and watered areas, the DinSAR technique, a digital elevation model (DEM), analysis of base surface maps and the results of spectral index calculations were combined to identify regions of active landslides caused by waterlogging of the Dnieper slopes. The study demonstrates the feasibility of using DinSAR to detect and monitor surface deformation.

Monitoring and Assessment of SBAS-InSAR Deformation for Sustainable Development of Closed Mining Areas—a Case of Nanzhuang Mining Area

Closed mining areas are an important potential resource, and rational use of them are critical to implementing sustainable development strategies. To guide the more reasonable development and utilization of the closed mining areas, we proposed a method of surface deformation monitoring and subsidence risk assessment of the closed mining areas with SBAS-InSAR technology. Based on the analysis of deformation, a risk assessment model integrating subsidence hazard and subsidence vulnerability was proposed. Subsidence hazard include accumulate deformation and the subsidence rate of the last year. The tunnels and the land use types are used to assess the subsidence vulnerability. This method can achieve deformation monitoring and risk assessment of the closed mining areas, and the proposed method is validated by taking the Nanzhuang mining areas as an example. The results showed that the main subsidence areas in the closed mining areas are located in the tunnel in the southwest and the gangue pile area in the northeast of the mining areas, while the deformation in the vegetation-rich forested area is low. The area of low, medium and high subsidence risk zones is 65.5%, 27.8% and 6.7% respectively.

Mapping Tunneling-Induced Uneven Ground Subsidence Using Sentinel-1 SAR Interferometry: A Twin-Tunnel Case Study of Downtown Los Angeles, USA

Synthetic Aperture Radar (SAR) interferometry is a formidable technique to monitor surface deformation with a millimeter detection resolution. This study applies the Persistent Scatter-Interferometric Synthetic Aperture Radar (PSInSARTM) technique to measure ground subsidence related to a twin-tunnel excavation in downtown Los Angeles, USA. The PSInSARTM technique is suitable for urban settings because urban areas have strong reflectors. The twin tunnels in downtown Los Angeles were excavated beneath a densely urbanized area with variable overburden depths. In practice, tunneling-induced ground settlement is dominantly vertical. The vertical deformation rate in this study is derived by combining Line of Sight (LOS) deformation velocities obtained from SAR images from both ascending and descending satellite orbits. Local and uneven settlements up to approximately 12 mm/year along the tunnel alignment are observed within the allowable threshold. No severe damages to aboveground structures were reported. Furthermore, ground movements mapped one year before tunnel construction indicate that no concentrated ground settlements pre-existed. A Machine Learning (ML)-based permutation feature importance method is used for a parametric study to identify dominant factors associated with the twin-tunneling induced uneven ground subsidence. Six parameters are selected to conduct the parametric study, including overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, the distance between the two tunnel centerlines, the depth to the tunnel springline, building height, the distance to the tunnel, and groundwater level. Results of the parametric analysis indicate that overburden thickness, i.e., the thickness of artificial fill and alluvium soils above the tunnel springline, is the dominant contributing factor, followed by the distance between tunnel centerlines, depth to the tunnel springline, and building height. Two parameters, the distance to the tunnel, and the groundwater level, play lesser essential roles than others. In addition, the geological profile provides comprehension of unevenly distributed ground settlements, which are geologically sensitive and more concentrated in areas with thick artificial fill and alluvium soils, low tunnel depth, and high groundwater levels.

Urban surface deformation monitoring and prediction by integrating SBAS-InSAR and Elman neural network

The existing prediction methods have complex model application, high requirements for data parameters, and are limited to the prediction of a single observation point. To address this problem, this paper proposes a deep learning-based surface subsidence prediction method. Taking Hefei City of China as the research area, the time-series surface deformation results of this area are obtained by using SBAS-InSAR, and then the SFLA intelligent algorithm and Elman neural network model are combined to predict the surface deformation of key urban areas, and the prediction results are compared and analyzed.The experimental results show that the prediction model proposed in this paper can not only accurately predict a single deformation point, but also predict regional land subsidence, and can be used for auxiliary decision-making of urban spatial planning, early warning of geological hazards and hazard mitigation.

Large-Scale Land Deformation Monitoring over Southern California with Multi-Path SAR Data

Southern California, USA, has been suffering severe surface deformation due to its active crustal movement under the north–south compression of the Pacific Plate and North American Plate. Meanwhile, affected by groundwater extraction and recharge, oil exploitation, surface subsidence, uplift, and seasonal deformation occur commonly in this region. In this paper, multi-path SAR datasets were collected to investigate and monitor surface deformation in Southern California. The unified simultaneous least squares (USLS) approach is applied to remove the deformation discontinuity between adjacent SAR image paths. Multiple deformation patterns of structural faults, groundwater withdrawal, and oil exploitation are observed with the interferometric synthetic aperture radar (InSAR) technique. The InSAR-derived results were validated with GPS monitoring data. The correlations between land deformation and groundwater withdrawal, faults, and precipitation were intensively analyzed, finding out and mastering the magnitude and characteristics of ground deformation in Southern California.

Review on the Research and Applications of TLS in Ground Surface and Constructions Deformation Monitoring.

With the gradual maturity of the terrestrial laser scanners (TLS) technology, it is widely used in the field of deformation monitoring due to its fast, automated, and non-contact data acquisition capabilities. The TLS technology has changed the traditional deformation monitoring mode which relies on single-point monitoring. This paper analyzes the application of TLS in deformation monitoring, especially in the field of ground surface, dam, tunnel, and tall constructions. We divide the methods for obtaining ground surface deformation into two categories: the method based on point cloud distance and the method based on displacement field. The advantages and disadvantages of the four methods (M2M, C2C, C2M, M3C2) based on point cloud distance are analyzed and summarized. The deformation monitoring methods and precisions based on TLS for dams, tunnels, and tall constructions are summarized, as well as the various focuses of different monitoring objects. Additionally, their limitations and development directions in the corresponding fields are analyzed. The error sources of TLS point cloud data and error correction models are discussed. Finally, the limitations and future research directions of TLS in the field of deformation monitoring are presented in detail.

InSAR time-series deformation forecasting surrounding Salt Lake using deep transformer models

The free and open data policy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large areas. InSAR deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau. However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multivariate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. In this paper, an innovative InSAR deformation prediction integrated algorithm based on the transformer models is proposed to predict time series deformation more accurately surrounding Salt Lake. Compared with the other solutions, the unique feature of the proposed method is that: 1) this method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series deformation. 2) The transformer-based model can more accurately simulate seasonal and non-seasonal deformation signals, and is effective for short-term prediction of surface deformation in permafrost areas. The InSAR deformation prediction results demonstrate that the InSAR deformation prediction method achieves better prediction performance in predicting the deformation trends of permafrost with a point scale compared with the prediction results of other models. Based on the predicted deformation and the water extraction results, the expansion trends surrounding Salt Lake are discussed and evaluated. The total area of Salt Lake increased by 57.32 km2 during the period 2015–2019. And Salt Lake maintained slowing expansion trend from 2019 to 2022. The time series deformation forecasting method can be used as a generic framework for modeling nonlinear deformation processes in complex permafrost areas, and it reveals the potential impact of the Salt Lake outburst event on the deformation processes and the degradation of permafrost.

Monitoring of ground surface deformation at Taimyrsky Mine using Sentinel-1 radar imaging for geodynamic risk assessment

The modern stage of mineral mining involves prediction of geotechnical risks. One of the major methods in this regard is the geodynamic zoning. The key target is to assess the cross-effect of mining operations and blocks structure of the Earth’s crust. This article presents the information on ground surface subsidence within the boundary of the mining lease of Taimyrsky Mine as a result of processing of paired images from Sentinel-1A sate llite. The implemented probabilistic kinematic analysis revealed some features of movements in overlying rock mass, in particular, local uplifts of ground surface. The detailed information on vertical deformations was obtained. Such approach enabled detecting the deformation areas subjected to the effect of the global tectonics which is, as a rule, neglected in geotechnical risk assessment while it directly affects mining safety. Despite the apparently consistent patterns resulted from processing of satellite images, the method InSAR is only used as a supplemental technique since the relevant regulatory framework is yet absent. In combination with the conventional surveying for monitoring ground surface deformations, the method InSAR enhances efficiency of monitoring and improves accuracy of geodynamic risk prediction in the course of mineral mining, inclusive of observations over the global tectonic processes. The probabilistic kinematic analysis allows for getting insight into the mechanism of overlying rock mass movement with regard to rock mass jointing and, jointly with InSAR, can be used in the artificial intelligence systems for learning models of prediction of overlying rock movement parameters during mineral mining.

Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python package

Dense, regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks enable the monitoring of plate motion and regional surface deformation. The spatial extent and density of these networks, as well as the length of observation records, have steadily increased in the past three decades. Software to efficiently analyze the ever-increasing amount of available timeseries should be geographically portable and computationally efficient, allow for automation, use spatial correlation (exploiting the fact that nearby stations experience common signals), and have openly accessible source code as well as documentation. We introduce the DISSTANS Python package, which aims to be generic (therefore portable), parallelizable (fast), and able to exploit the spatial structure of the observation records in a user-assisted, semi-automated framework that includes uncertainty propagation. DISSTANS is open-source, includes an application interface documentation as well as usage tutorials, and is easily extendable. We present two case studies that demonstrate our code, one using a synthetic dataset and one using real GNSS network timeseries.

Spatial and temporal characteristics analysis for land subsidence in Shanghai coastal reclamation area using PS-InSAR method

In recent decades, large-scale reclamation projects have been performed in the intertidal flat area of Shanghai, China. Due to the self-weight consolidation of the foundation and dynamic load caused by human activities, the newly reclamation area will sink within a period of time after the land is formed. Therefore, it is necessary to carry out surface deformation monitoring for taking preventive measures in advance. In this research, the PS-InSAR technology, mostly used for urban subsidence monitoring, was applied to obtain the ground deformation information of Shanghai coastal area based on ENVISAT/ASAR (2007.07-2010.02) and Sentinel-1A (2017.07-2020.02) datasets. The results showed that: 1) Compared with ASAR data, the Sentinel-1A data could distinguish more coherent points and get more comprehensive deformation distribution characteristics. 2) Most high-coherent points were detected in artificial objects, especially airport runways, buildings, roads and seawalls. 3) There was obvious uneven land subsidence in the study area during the two monitoring periods, the PS points with high subsidence rates (<-20 mm/a) mainly distributed around Dishui Lake and artificial seawalls. 4) The ground subsidence velocity of the newly formed land gradually slowed down over times, with the average subsidence rate decreased from -10.45 mm/a to -4.94 mm/a. Our study proved that remote sensing monitoring for ground subsidence in reclaimed land could be realized based on PS-InSAR technology, which could provide the spatial distribution characteristics of subsidence in large-scale and long-term series and help the sustainable development of coastal engineering construction.