Series Interferometric Synthetic Aperture Radar Research Articles

Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR

Abstract: Long-term series monitoring of ground surface settlement by remote sensing has become an effective method. Based on TS-InSAR (time series Interferometric Synthetic Aperture Radar) interferometry, this paper proposes a new model based on the Grey Wolf Optimizer (GWO) and Long Short-Term Memory (LSTM) to monitor and predict the ground surface settlement in Kunming City. The results show that the MAPE (mean absolute percentage error), RMSE (root mean square error), and MAE (mean absolute error) of GWO-LSTM are significantly reduced. R2 (goodness of fit) in the six sub-study areas of Kunming City is improved in comparison to the LSTM model. The problem of manual parameter selection in the LSTM model is solved by the GWO algorithm to select parameters automatically. This approach not only significantly reduces the model’s training time but also identifies the most suitable network parameters. This can bring the best performance. Based on TS-InSAR data, the prediction of urban ground surface settlement by the GWO-LSTM model has good accuracy and robustness, which offers a scientific foundation for monitoring and issuing early warnings about urban land disasters.

Surface Subsidence Characteristics and Causes Analysis in Ningbo Plain by Sentinel-1A TS-InSAR

In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of Sentinel-1A SAR images between January 2018 and June 2023. The time series interferometric synthetic aperture radar (TS-InSAR) technique was used to acquire surface subsidence information in the area. The causes of subsidence were analyzed. The results show that: (1) the annual deformation rate of the Ningbo Plain ranges from −44 mm/yr to 12 mm/yr between 2018 and 2023. A total of 15 major subsidence zones were identified by using both the subsidence rate map and optical imagery. The most severe subsidence occurred in the northern industrial park of Cixi City, with a maximum subsidence rate of −37 mm/yr. The study reveals that the subsidence issue in the main urban area has been significantly improved compared to the 2017 subsidence data from the Ningbo Bureau of Natural Resources and Planning. However, three new subsidence areas have emerged in the main urban area, located, respectively, in Gaoqiao Town, Lishe Town, and Qiuyi Village, with maximum rates of −29 mm/year, −24 mm/year, and −23 mm/year, respectively. (2) The causes of subsidence were analyzed using various data, including land use data, geological data, groundwater-monitoring data, and transportation network data. It is found that a strong link exists between changes in groundwater levels, compressible layer thickness, and surface subsidence. The groundwater levels changes and the soft soil layer thickness are the main natural factors causing subsidence in the Ningbo Plain. Additionally, the interaction between static loads from large-scale industrial production and urban construction, along with the dynamic loads from transportation networks, contribute significantly to surface subsidence in the Ningbo Plain. The results from this study enhance the understanding of the driving factors of subsidence in the Ningbo Plain, which can provide necessary guidance for the economic development and decision-making in the region, helping to manage and potentially mitigate future subsidence issues.

Utilizing Gaofen-3 InSAR time series to reveal land subsidence in Beijing (China)

ABSTRACT Various applications have been achieved by Gaofen-3 images, while the time series interferometric synthetic aperture radar (TS-InSAR) analysis was rare due to the issues with baseline instability. In this study, we revealed that the primary artefacts degrading the quality of Gaofen-3 interferograms are the long spatial baselines and inaccuracy of orbit information induced by the lack of consideration of satellite orbital tube manoeuvres. To address these challenges, we have proposed an enhanced interferometry processing method aimed at isolating the main artefacts from wrapped interferograms and enhancing the interferometry capabilities of Gaofen-3 images. It mainly includes tips on estimating precise baselines based on orbital information and fringe frequency in the wrapped interferogram and refining the residual phase further by a quadratic polynomial regression model. Subsequently, the TS-InSAR analysis is successfully implemented to acquire high-accuracy land subsidence in Beijing during 2020–2021 based on these refined interferograms. This case study demonstrates the achievement of the land subsidence analysis through TS-InSAR with Gaofen-3 images, highlighting that our proposed method could enhance the quality of Gaofen-3 interferograms.

TIME SERIES INSAR ANALYSIS FOR SLOPE STABILITY MONITORING USING SENTINEL-1 IN OPEN PIT MINING

Abstract. Surface mining is an important economic activity in many regions where mineral resources are located close to the surface. The process of ore extraction is characterised by high pit walls with steep slopes. The mining process introduces significant stress and strain changes that could lead to movement on the slope face. Slope failure on the pit walls can occur without warning and can lead to loss of life and disruptions to the mining process. The monitoring of slopes remains the most reliable way to detect movement on unstable slopes. Therefore, slope monitoring programmes have been established to monitor and measure slope movement to help mine management with the stability status of open pit slopes. Conventional monitoring techniques include visual inspections and in situ instrumentation that are point-based, time-consuming, and costly. Open-source remote sensing techniques such as Synthetic Aperture Radar interferometry have been used to monitor slope movements on open-pit mine walls. Time series interferometric Synthetic Aperture Radar (InSAR) can be used to overcome limitations of in-situ monitoring for monitoring slope movements in open-pit mines.

Land subsidence simulation based on Extremely Randomized Trees combined with Monte Carlo algorithm

Land subsidence is a threat to the living safety of urban residents. Land subsidence simulation helps make prevention and conservation efforts more effective. This work introduces an innovative model for land subsidence simulation which combines Extremely Randomized Trees (ET) with the Monte Carlo algorithm to conduct quantitative analysis on the importance of different factors in land subsidence. Long-time series land surface deformation results in the eastern part of Beijing Plain were obtained by time series Interferometric Synthetic Aperture Radar (InSAR) monitoring technology based on 91 scenes of RADARSAT-2 data collected from Sept. 2009 to Jan. 2020. In addition, multi-source data, such as layered compressible layer and layered groundwater level were introduced to analysis the evolutionary mechanism of land subsidence. The results showed that the average annual rates of land subsidence at the following three typical subsidence bowls in the Beijing Plain, Chaoyang-Jinzhan, Dong Balizhuang-Da Jiaoting and Tongzhou-Song Gezhuang were −66.4 mm/a, −64.2 mm/a and −53.2 mm/a from 2010 to 2019, indicating a rather alarming subsidence issue. The most important feature affecting land subsidence is the groundwater level which contributed 67.6% to 81.8% to land surface deformation. The Monte Carlo simulation results indicated that land subsidence occurs at Dong Balizhuang-Da Jiaoting Subsidence Bowl is estimated to be the worst, with a 95% confidence interval differing by 95.7 mm/a between the upper and lower limits. Such work contributes to better understand the complex relationship between the land subsidence and influencing factors. And the simulation results provide different scientific means to facilitate efficient management and control of three land subsidence bowls.

Surface Subsidence of Nanchang, China 2015–2021 Retrieved via Multi-Temporal InSAR Based on Long- and Short-Time Baseline Net

Urban land subsidence threatens the safety of urban buildings and people’s lives. The time series interferometric synthetic aperture radar (InSAR) technology can provide us with large-area, high-resolution, and high-precision ground deformation monitoring. In this study, the time series InSAR technology and the strategy with long- and short-time baseline networking are used to obtain the surface deformation along the line of sight of Nanchang City based on the six-year (from December 2015 to December 2021) Sentinel-1 data. Longer datasets and better baseline strategies allow us to obtain more stable deformation results of Nanchang City than other researchers. The results of surface deformation show that the overall surface of Nanchang City is stable, but there are several obvious subsidence funnels. We carried out a field survey on four areas with significant surface subsidence. We considered that these subsidence areas may be related to soil compaction, building construction, and groundwater extraction. Based on the surface deformation results around the subway line, we analyzed the impact of subway construction on the surface along the line and identified the sections that need to be focused on by the managers to prevent the deformation area from affecting the surrounding buildings and subway line operation safety.

Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

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

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

Creep identification by the baseline optimized TS-InSAR technique considering the monthly variation in coherence

Time series interferometric synthetic aperture radar (TS-InSAR) technique can be applied to identify the creep body. However, it has strict requirements regarding the quality of the interferograms and the interferometric baseline networks. In regions with intermittent coherence, the use of conventional methods to select interferometric pairs can easily lead to interferometric baseline disconnections, redundancy, or missing key pairs. This results in reduced accuracy and may even misidentify creep characteristics. To solve this problem, the segmented average coherence threshold method (SACTM) is proposed. SACTM is an interferometric baseline optimization method that considers monthly coherence variability. In this paper, the SACTM baseline optimization TS-InSAR technique was used to identify creep bodies in Wudongde Reservoir area located along the Jinsha River in China. Compared with the conventional methods, the SACTM method yields a more robust interferometric baseline network and a smaller standard deviation for deformation rate, which demonstrates the superiority of this method.

Temporal Subset SBAS InSAR Approach for Tropical Peatland Surface Deformation Monitoring Using Sentinel-1 Data

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change.

DS-InSAR Based Long-Term Deformation Pattern Analysis in the Mining Region With an Improved Phase Optimization Algorithm

The environment management and land utilization of the abandoned mining region is critically dependent on precise monitoring and comprehensive understanding of mining subsidence. In order to overcome the shortcomings of the traditional distributed target phase optimization method in the space continuity constraints of adjacent pixels, an improved phase optimization algorithm was proposed, which combines region growing and time series interferometric synthetic aperture radar with distributed scatterers (DS-InSAR). By using 17 L-band and 51 C-band SAR images, the characteristics of temporal and spatial distribution in Peibei mining area of Xuzhou, China, were obtained during the period from 2007 to 2011 and the period from 2017 to 2020. With the long-term monitoring, the evolution of deformation in the mining area was carried out. The modified phase optimization technique has proven its ability in the density of measurement points and the influence of noise in space, which is promising for the detection of large gradient deformation and the accurate analysis of surface deformation in mining areas. The study has been concentrated toward detecting continuous subsidence in the mining region. Coal mines in operation are usually accompanied by unstable ground, and the uplift or second subsidence has sometime occurred in the closed mine region. Conclusively, the presented methodology is practically feasible for long-term deformation pattern analysis in coal-exhausted mining areas.

Ascending and Descending Time Series Interferometric Synthetic Aperture Radar Monitoring and Analysis of Landslide Deformation

Ascending and Descending Time Series Interferometric Synthetic Aperture Radar Monitoring and Analysis of Landslide Deformation

Prediction of InSAR deformation time-series using a long short-term memory neural network

ABSTRACT The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.

Improving Boundary Constraint of Probability Integral Method in SBAS-InSAR for Deformation Monitoring in Mining Areas

Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. In addition, the probability integral method (PIM) is a surface movement model that is widely used in the field of mining subsidence. In recent years, the integration of TS-InSAR and the PIM has been extensively studied. In this paper, we propose a new method to estimate mining subsidence with the PIM based on TS-InSAR results. This study focuses on the improvement of a boundary constraint and dynamic parameter estimation in the PIM through the inversion of the line-of-sight (LOS) time series deformation derived by TS-InSAR. In addition, 45 Sentinel-1A images from 17 June 2015 to 27 December 2017 of a coal mine in Jiaozuo are utilized to acquire the surface displacement. We apply a time series deformation analysis using small baseline subsets (SBAS) and place the results into an improved PIM to estimate the mining parameters. The simulated mining subsidence is highly consistent with the leveling data, exhibiting an RMSE of 0.0025 m. Compared with the conventional method, the proposed method is more accurate in discovering displacement in mining areas. In the final section of this paper, some sources of error that affect the experiment are discussed.

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Precise Topographic Model Assisted Slope Displacement Retrieval from Small Baseline Subsets Results: Case Study over a High and Steep Mining Slope.

Due to the intrinsic side-looking geometry of synthetic aperture radar (SAR), time series interferometric SAR is only able to monitor displacements in line-of-sight (LOS) direction, which limits the accuracy of displacement measurement in landslide monitoring. This is because the LOS displacement is only a three dimensional projection of real displacement of a certain ground object. Targeting at this problem, a precise digital elevation model (DEM) assisted slope displacement retrieval method is proposed and applied to a case study over the high and steep slope of the Dagushan open pit mine. In the case study, the precise DEM generated by laser scanning is first used to minimize topographic residuals in small baseline subsets analysis. Then, the LOS displacements are converted to slope direction with assistance of the precise DEM. By comparing with ground measurements, relative root mean square errors (RMSE) of the estimated slope displacements reach approximately 12–13% for the ascending orbit, and 5.4–9.2% for the descending orbit in our study area. In order to validate the experimental results, comparison with microseism monitoring results is also conducted. Moreover, both results have found that the largest slope displacements occur on the slope part, with elevations varying from −138 m to −210 m, which corresponds to the landslide area. Moreover, there is a certain correlation with precipitation, as revealed by the displacement time series. The outcome of this article shows that rock mass structure, lithology, and precipitation are main factors affecting the stability of high and steep mining slopes.

Analysis of Land Subsidence Distribution and Influencing Factors in Yan'an New District Based on Time Series InSAR

Objectives The excavation and city in Yan'an New District is the largest geotechnical project in the collapsible loess gully region. Large-scale land creation, engineering construction and complex engineering geological conditions has caused a large number of uneven land subsidence, which is posing an increasing threat to the stability of urban infrastructure and public safety. Methods The ascending Sentinel-1A data stacks obtained during December 2017 to December 2018 were analyzed using the time series interferometric synthetic aperture radar (InSAR) technique, and the distribution characteristics of the post-construction land subsidence of the mountain excavation and city construction in Yan'an New District were obtained. The reliability of the detection results of InSAR technology was demonstrated by field investigation. Finally, based on the deformation monitoring results, the main influencing factors of land subsidence distribution were analyzed. Results (1) The results show that significant uneven land subsidence formed after the mountain excavation and city construction in Yan'an New District is mainly distributed in the filling area, and the maximum deformation rate reaches up to 120.1 mm/a. Field investigation validates the reliability and accuracy of the deformation results. (2)The remodeling of original loess and the change of its physical properties during the filling process is the main intrinsic factors of land subsidence in the filling area. Large-scale land creation determines the distribution of land subsidence, and the thickness of the filling are the main controlling factor of the distribution and size of land subsidence. In addition, human activities and changes in the geo-environment will also accelerate the development of land subsidence. Conclusions In order to achieve the early detection of potential geo-hazards and active risk prevention in the site selection and feasibility study stage of mega engineering projects in the Loess Plateau, the time-series InSAR technology can provide an effective technical that allows for the early detection of potential geo-hazards such as land subsidence and landslides, and provide a scientific basis for further monitoring and warning, planning and construction, geologic disaster prevention.

Evaluating Potential Ground Subsidence Geo-Hazard of Xiamen Xiang’an New Airport on Reclaimed Land by SAR Interferometry

The land reclaimed from the seaside may have a long-term subsidence trend, which poses a potential geohazard in the future land use. Xiamen Xiang’an New Airport (XXNA) is built on reclaimed land since 2016. Based on the spaceborne Sentinel-1 data between January 2018 to April 2019 and the time series interferometric synthetic aperture radar (InSAR) technique, this paper analyzed the reclaimed land subsidence evolution at XXNA in this period. InSAR measurements show that XXNA is suffering from severe subsidence, mainly in three regions because of the earth and sand compacting. By analyzing the spatial subsidence characterizations of the main subsiding areas combined with historical land reclamation and future land use planning, we find the potential threat of subsidence to future land use. Correlation between subsidence and the period of reclamation was found, indicating that the consolidation and compression in dredger fill is the main cause of subsidence. By combining subsidence monitoring results with different land use types and adopting the Expectation (Ex) and Entropy (En) methods, we analyzed the key area with potential subsidence geo-hazard. This work shows that with SAR interferometry, it is possible to find the large area ground subsidence in the airport reclaimed area. The areas with potential subsidence geo-hazards are consistent with the deep reclaimed earth, which means high subsidence risk in the future.

Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region

Time Series Interferometric Synthetic Aperture Radar (TSInSAR) methods have been widely and successfully applied for spatiotemporal ground deformation monitoring. The main groups of methodological approaches are often referred to as Persistent Scatterer (PS), Small Baseline (SB), and hybrid approaches that incorporate PS and SB concepts. While TSInSAR techniques have long been able to provide accurate deformation rates for various applications, their corresponding performance in complex environments such as mining areas has to be investigated. This study focuses on comparing the performance of three open source TSInSAR toolboxes (Stamps, Giant, Mintpy) over an extended region that includes an active opencast coal mine. We present the deformation results of each TSInSAR method on a Sentinel-1 dataset of 125 acquisitions spanning around 2.5 years over the Ptolemaida-Florina coal mine site that is characterized by several environmental and surface deformation conditions. First, a cross-comparison analysis is presented over different land cover classes. The study shows that all TSInSAR methods are capable for generating similar ground deformation results when the area has stable ground scattering conditions and the dataset sufficient temporal sampling. The most controversial results between TSInSAR approaches were found in land cover classes that include medium to high vegetation. An external comparative analysis between the different results from TSInSAR methods and leveling measurements is also performed. Stamps approach presented the best agreement with the in-situ deformation rates. The Giant approach yielded the best cumulative deformation results due to our a priori knowledge of temporal behavior of deformation in the vicinity of the leveling locations. Finally, we discuss the main pros and cons of each TSInSAR approach and we highlight the importance of comparison analysis that can provide insights and can lead to better interpretation of the results.

Multi-Scale and Multi-Dimensional Time Series InSAR Characterizing of Surface Deformation over Shandong Peninsula, China

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.