User-Friendly InSAR Data Products: Fast and Simple Timeseries Processing

Interferometric synthetic aperture radar (InSAR) analysis is a radar technique for generating large-area maps of ground deformation using differences in the phase of microwaves returning to a satellite. In recent years, high-resolution SAR sensors have been developed that enable small-scale slope deformation to be detected, such as the partial block movement of a landslide. The L-band SAR (PALSAR-2) is mounted on Advanced Land Observing Satellite-2 (ALOS-2), which was launched on 24 Mar. 2014. Its main improvements compared with ALOS are enhanced resolution of as high as 3 m with a high-frequency recurrence period (14 days). Owing to its high resolution and the use of the L-band, PALSAR-2 can obtain reflective data passing through a tree canopy surface, unlike the other synthetic aperture radars. Therefore, the coherence of InSAR in mountainous forest areas is less likely to decrease, making it advantageous for the extraction of slope movement. In this study, to verify the accuracy of InSAR analysis using PALSAR-2 data, we compared the results of InSAR analysis and the measurement of the displacement in a landslide by global navigation satellite system (GNSS) observation. It was found that the average difference between the displacements obtained by InSAR analysis and the field measurements by GNSS was only 15.1 mm in the slant range direction, indicating the high accuracy of InSAR analysis. Many of the areas detected by InSAR analysis corresponded to the locations of surface changes due to landslide activity. Additionally, in the areas detected by InSAR analysis using multiple datasets, the ground changes due to landslide movement were confirmed by site investigation.

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

There is limited research focusing on Interferometric Synthetic Aperture Radar (InSAR) applications in the Great Lakes coastal wetlands with large water level fluctuations. In this study, we investigated the potential of using C-band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes. InSAR analysis was conducted using Radarsat-2 and Sentinel-1 data collected at Long Point, Ontario, Canada over the period of 2016–2018. Observations indicated that both backscattering coefficients and coherence from tall plants (e.g. cattail/Phragmites), short plants (e.g. grass), and water varied with different sensor modes (incidence angles and polarizations) in response to changes in phenology, disturbance, and water level. InSAR phase changes were closely related to fluctuations in water level and flow direction. We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error (RMSE). It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site, type of wetland vegetation, incidence angle and polarization. Although results from some sensor modes provided good correlation at a few locations, the low fringe rate and RMSE between 9 and 28 cm indicated that InSAR observations of water level changes were generally underestimated.

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

Owing to the remarkable improvements that have been made in radar sensors, it becomes possible to obtain the information for a single structure from high-resolution SAR images. In our previous research, a method for detecting the heights of low-rise buildings automatically was proposed using 2D GIS data and a single high-resolution TerraSAR-X intensity image. However, this method is difficult to apply for high-rise buildings due to surface/material conditions of their exterior walls. In this study, a new method was developed for estimating the heights of high-rise buildings based on the results from an Interferometric SAR (InSAR) analysis. The characteristics of phases in the InSAR result were investigated and used for extracting the potential layover areas of target buildings. Then, the heights were estimated according to their layover lengths. The developed method was tested on two TerraSAR-X images for San Francisco, USA, in the HighSpot mode. Comparing the result with Lidar data, the detected heights were found to be reasonable.

Over the last decade, Fengcheng oil field rich in heavy oil geological reserves has had a broad prospect of exploration. The reservoirs are mainly composed of high porosity, high permeability sandstone and conglomerate in study area. The reservoir characteristics led to poor fluid and gas injection causing ground surface deformation with the injection of gas and fluid and threatened the safety of nearby construction facilities. Interferometric Synthetic Aperture Radar (InSAR) technology has been used to identify ground surface deformation with high precision, which is widely used in various fields. This paper adopted Small Baseline Subset InSAR (SBAS-InSAR) method to monitor oil field for the period of February 2007 to September 2010. In this study, the Fengcheng oil field in Xinjiang served as an example; we measured subsurface fluid and gas injection-induced ground surface deformation by SBAS-InSAR technique and investigated the causes of observed deformation. The results showed significant uplift over Fengcheng oil field and testified the feasibility of SBAS-InSAR method. The Phased Arrary L-band Synthetic Aperture Radar (PALSAR) datasets outcome revealed ground surface maximum cumulative uplift for 4 years was 40 cm in the line of sight (LOS) direction, and annual average deformation rates were ranging from − 80 to 120 mm/year. Further, the time series analysis based on monitoring results found that the causes of ground surface uplift were related to the increase in pore pressure under the reservoir, and the surface deformation area corresponded to the production well position of study area. Therefore, InSAR technique is an essential tool for the ground surface deformation monitoring due to fluid and gas injection in heavy oil field, ensuring the safety of personnel and property.

Rainfall induced landslides have been the No.1 geohazards in Hong Kong (HK). In coastal subtropical monsoon regions, air temperature and humidity vary substantially and frequently. Tropospheric delays (TDs) limit the accurate detection of slow slope motion (which is the common case in HK) using interferometric synthetic aperture radar (InSAR).  In this presentation, we introduce a new TD correction method for InSAR line-of-sight (LOS) measurements at individual slope scale. The TD signal was estimated from LOS time series through a blind source separation (i.e., independent component analysis). The stratified TD was isolated according to a spatially elevation-linked and temporally periodic independent-component (IC), which was determined via a correlation test and power spectrum analysis. Therefore, the TD correction was not relying on any external weather products/meteorological data and had unprecedented spatiotemporal details equivalent to the SAR images.A case study in Tai O, HK was conducted to verify the proposed method using 63 descending CosmoSkyMed (CSK) and 143 ascending Sentinel-1 (SNT-1) images. We used meteorological data of air temperature, humidity and weather products of ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-5 and GACOS (Generic Atmospheric Correction Online Service) to validate the estimated TD. We estimated up to 3-4 cm spatiotemporally relative TD in the LOS directions of CSK and SNT-1, corresponding to a slope elevation change of ~ 400 m. The estimated TD was largely affected by specific air conditions (e.g., temperature and humidity) on the SAR image-acquisition days. In addition, we found the relative TD exhibited a slower increment rate than that of the slope elevation, suggesting the TD was not linearly related to the elevation. Ground deformation measurements from prisms and records of rainfall and tide were used to validate and interpret the InSAR deformation time series. It is interesting to find different hydrological forcings regulate the seasonal deformations in the slope (~ 10mm) and the reclamation (~ 15mm) in Tai O. Downslope movement (due to increase in pore-water pressure) occurred when rainfall accumulated from a dry season to a wet season, whereas upslope rebound (due to soil shrinkage) occurred when rainfall decreased from a wet season to a dry season. However, the periodic deformation of the reclamation substrate seemed to be more related to the sea level, instead of rainfall. The TD correction has reduced the root-mean-square error (RMSE) by 42.3%, such that InSAR LOS time series with millimeter-level accuracy (potentially 1-3 mm) were obtained in the Tai O case.

Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Mapping land subsidence and aquifer system properties of the Willcox Basin, Arizona, from InSAR observations and independent component analysis

Smart monitoring of critical civil engineering infrastructures has become a priority nowadays as ageing of construction materials may have dramatic consequences on the community. The issue is exacerbated as it applies to many structures at the network level rather than to single structures or limited areas. To this effect, catalogues for assessment of decay conditions and identification of maintenance actions are crucial pieces of information for infrastructure management purposes. Within this context, innovative non-destructive methods, such as space-borne techniques, have been increasingly used for monitoring purposes in the past two decades. Among these, the Interferometric Synthetic Aperture Radar (InSAR) imagery technique is gaining momentum nowadays. This method is used to monitor ground and infrastructure displacements at the large scale with a millimeter resolution. It can compare radar satellite images over time, and it is capable to measure variations accurately using interferometry. An advantage of using InSAR techniques is that these are not affected by cloudiness as well as lighting conditions, since data can be also collected at night time. On the contrary, InSAR is computational-demanding as it requires to filter out unnecessary information from the entire captured area to obtain data on the target domain. Most common applications include landslide assessment and monitoring of surface deformations following major seismic events. This paper reports a methodology for the assessment of surface deformations of viaducts by reducing drastically computational time. To this purpose, a multi-stage automatic bridge monitoring protocol is developed at the network level by integration of information into Geographic Information System (GIS) catalogues and use of the InSAR imagery technique. The first stage locates the viaducts in the area of interest by querying open data and inputting results into a GIS catalogue. On a second stage, an InSAR analysis of the identified bridges is performed. This approach allows an estimate of surface displacements as well as an identification of bridge areas affected by millimeter-scale settlements. Fundamental theoretical and working principles of the two methodologies are first introduced in the paper. Advantages against drawbacks of each technique are then discussed. The last Section reports a case study and a discussion of the main results including conclusions and future prospects.

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

Over the past three decades, synthetic aperture radar (SAR) and SAR interferometry (InSAR) have revolutionized Earth observation, allowing for geophysical monitoring of Earth's surface deformation with centimeter‐to‐millimeter precision. InSAR time series are successive maps of ground motion used for natural hazards assessment with the monitoring of processes slowly deforming the Earth's surface, such as landslides, subsidence, or slow tectonic motion. In particular, the detection of transient displacements of millimeter amplitude is of utmost importance to better capture the dynamics of active tectonic faults, for a better understanding of the underlying physics governing the earthquake cycle. However, despite tremendous advances over the last two decades, noise affecting the data, primarily due to atmospheric delay, still challenges the detection of small deformation signals in InSAR time series. Consequently, an unknown amount of slow tectonic deformation signals remains concealed within the noise, raising the need for novel methodologies for signal extraction. We introduce InSARDenoiser, a spatiotemporal attentive convolutional U‐Net, designed to extract small‐scale deformation in noisy InSAR time series. The combination of a deep spatial U‐Net with a spatiotemporal Transformer tracks transient deformation in space and follows its temporal signature through time. We develop a mathematical formulation to generate realistic multi‐scale atmospheric noise and build a synthetic data set. When applied to a real InSAR time series along the North Anatolian Fault, the method effectively extracts millimeter‐scale slow deformation. The extracted deformation aligns with independent creepmeter measurements, thereby validating our method and opening the possibility of its application to diverse tectonic settings globally.

• Analyzed GNSS calibration effectiveness on different InSAR algorithms. • Compared calibrated InSAR displacements with submillimeter precision NDTs. • Used polynomial regressions to model and correct InSAR displacement errors. • Validated improvement locally and globally using GNSS and levelling data. • Found linear adjustment to be the best justified option for calibration in most cases. This article presents an analysis of the effectiveness of performing a calibration through Global Navigation Satellite System (GNSS) data on different approaches of the Interferometric Synthetic Aperture Radar (InSAR). InSAR has become a key technique for monitoring ground surface, but its results often exhibit systematic biases and deviations from ground truth measurements. This study addresses the need for more reliable displacement data by evaluating a GNSS-based calibration approach across three InSAR processing approaches: i) Persistent Scattering Interferometry (PSI) method using the open-source software StaMPS, ii) PSI method using the commercial SARPROZ software and iii) the Quasi-PS method implemented with SARPROZ. Sentinel-1 A/B data from both ascending and descending orbits, covering the period from October 2017 to January 2019, were used. Displacement errors from the same points were modeled using polynomial regressions and calibrated using reference data from GNSS and levelling, both with submillimeter precision. The effectiveness of the calibration was assessed at two levels: locally (via a corner reflector) and globally (via a GNSS baseline several kilometers long). Results show that 25 out of 27 displacement time series required correction in the mean value, with StaMPS showing the greatest need for calibration. The calibration improved the original results at all scales and acted mainly on the mean difference, with linear calibration being the most robust and consistent option. These findings highlight the importance of incorporating ground truth data to enhance InSAR reliability and support its application in geodetic and structural monitoring.

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes.