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SAR Interferometry (InSAR) has been proven to be effective for measuring landslides deformation. However, the InSAR application of landslide mapping is limited by the blind observation areas caused by SAR geometric distortion or vegetation decorrelation. We proposed an approach of landslide detection and mapping with a priori assessment of InSAR applicability, which can not only guide the selection of SAR data, but also prejudge the observable areas. Meanwhile, it can assist to analyze the reliability of landslide detection and help to reveal the reason of detection failure of occurred landslides. We employed this approach to evaluate the InSAR applicability over the whole Guizhou Province at first. Then, we took Shuicheng County as an example to conduct time-series InSAR landslide detection combined with a priori applicability. Six large active landslides were identified from the results of three SAR data stacks (ALOS-2 PALSAR-2 and ascending/descending Sentinel-1 from April 2017 to July 2019). The reliabilities of landslide detection results were analyzed and the possible reasons for the detection failure of Jicangzhen landslide were revealed. We inferred that the Jichangzhen landslide suddenly happened induced by short-term heavy rainfalls and presented no precursors, which cannot be captured by SAR satellites with intermittent observations.

As the spaceborne mission dedicated to bistatic SAR interferometry (InSAR), the TerraSAR-X/TanDEM-X satellite constellation can acquire highly coherent data pairs with flexible baseline configurations and minimal atmospheric disturbances, which are well suited for high-accuracy DEM generation. However, hindered by the intrinsic contradiction between sensitivity of height measurement and reliability of phase unwrapping in terms of normal baseline length, as well as geometric distortions of layover and shadow, it is still difficult to generate high-quality DEM in mountainous areas using single-baseline InSAR data. Aiming at these two problems, we developed a cascaded multi-baseline InSAR processing framework for DEM generation from multiple bistatic InSAR observations with variable normal baselines and different imaging geometries. By starting from phase unwrapping and phase-to-height conversion for the interferogram with shortest normal baseline, this approach adopted a strategy of DEM updating assisted by a reference elevation dataset. The updated DEM is then used as the reference for processing interferograms with longer baselines successively. In this iterative way, the quality of the resultant InSAR DEM can be improved gradually. In addition, fusion between multiple geocoded InSAR DEMs derived from different orbits is carried out to further refine the results by filling data voids and reducing random height noise. To demonstrate the effectiveness of the proposed method, we apply it to four pairs of stripmap-mode TerraSAR-X/TanDEM-X bistatic data covering the Mount Song area in central China. The vertical accuracies of the generated DEMs are evaluated using an aerial photogrammetric DEM, ICESat/GLAS measurements, and the standard TanDEM-X global DEM product. The experimental results show that the absolute height error of the generated multi-baseline InSAR DEM at 5 m posting is less than 10 m in typical mountainous areas and can meet the American HRTI-3 standard, which means the generated DEM has similar height accuracy and higher spatial resolution in comparison with the 12 m TanDEM-X global DEM products.

In densely vegetated mountainous areas, the height measurement accuracy of SAR interferometry is contaminated by phase unwrapping errors and volume scattering effects of the vegetation. Aiming at this limitation of InSAR, we propose the high-resolution radargrammetry-assisted SAR interferometry method to achieve high-precision topographic mapping. Specifically speaking, we propose to apply our methods in the TerraSAR-X images with a repeat-pass InSAR pair and a stereo radargrammetric pair sharing the same master image which can avoid radarcoding the reference DEM and minimize the acquisition time discrepancy between the reference DEM and InSAR data. Compared with medium-resolution reference DEM, the high-resolution radargrammetric DEM can simulate the reference topographic phase more accurately which reduces the InSAR phase unwrapping errors. In addition to the removal of the reference topographic phase, we automatically select reliable points from radargrammetric DEM for calibrating the absolute InSAR phase offset. Finally, we fuse the radargrammetric DEM and InSAR DEM together with weights determined from theoretical height error to fill in data voids in InSAR DEM. The experimental results show that the DEM generated by radargrammetry-assisted SAR interferometry has better height accuracy than DEM generated by either single method.

Dynamic loads of vehicles have a considerable impact on the safety of elevated highways, but few studies have addressed the influence of dynamic loads on the safety of such linear infrastru...

Landslides and debris flows in the Loess Plateau pose great threats to human lives and man-made infrastructure, such as buildings and expressways. Thus, the detection and monitoring of the stability of slopes are crucial in geohazard prevention and management. In this study, the time series synthetic aperture radar interferometry (InSAR) analysis method that combines persistent scatters (PSs) and distributed scatters (DSs) is employed to detect and map active slopes along the upstream Yellow River from the Longyang Gorge dam to the Lijia Gorge dam using one ALOS PALSAR data stack from 2006 to 2011 and two Sentinel-1 data stacks from 2015 to 2017. More than 100 active slopes in a total coverage of 222.5 km2 were identified. Through a time series displacement analysis of active slopes, we found that changes in the water content of loess slopes induced by rainfall or reservoir impoundment might be a major factor that can activate unstable slopes or accelerate the movement of active slopes.

SAR Interferometry (InSAR) is a powerful technique for mapping land surface deformation, but single one-dimensional (1D) InSAR line-of-sight (LOS) measurement limits its application in three-dimens...

SAR Interferometry (InSAR) has been proven to be effective for measuring landslides deformation. However, the accuracy of InSAR application of landslide mapping and monitoring is limited by the complex atmospheric distortion in alpine valley areas. The sparse external atmospheric data cannot accurately reflect the complex heterogeneous atmosphere in alpine valley areas. The conventional atmospheric delay corrections based on InSAR phase are weakened by the presence of confounding signals (e.g., tropospheric delays, deformation signals, and topographic errors) and spatial heterogeneity of the troposphere.In this study, we propose the multi-temporal moving-window linear model to estimate the stratified tropospheric delay. The linear relationship between the multi-temporal interferometric phases and the local terrain is estimated using moving windows and then we retrieve the vertical stratified atmospheric phase over the whole scene. Taking into account the deformation information and phase unwrapping errors, the model is solved by an iterative robust estimation algorithm weighted by both deformation rates and residual unwrapping phases. We first compared our model with four other InSAR atmospheric delay correction methods: traditional empirical linear model, REA5 numerical atmospheric model, GACOS, and temporal/spatial filtering method. The results demonstrated that our proposed model has the best performance on atmospheric delay correction over the reservoir of the Lianghekou hydropower station using Sentinel-1 datasets. Meanwhile, our model was less affected by randomly turbulent phase and phase unwrapping errors, which significantly improves the accuracy of landslide deformation detection and monitoring.Then, we integrated the multi-temporal moving window atmospheric delay correction model into the STAMPS-SBAS program. The high-precision wide-area time series deformation over the reservoir area can be obtained through iterating phase unwrapping and atmospheric delay correction. In particular, the phase unwrapping errors were gradually corrected during the iteration process. The improvement of the proposed model on the landslide investigations was validated through using UAV images and field surveys. Some landslides that have not been identified in the traditional time-series InSAR results can be identified after atmospheric delay correction by the proposed model.

Small-to-moderate earthquakes (e.g. ≤Mw5.5) occur much more frequently than large ones (e.g. >Mw6.0), yet are difficult to study with InSAR due to their weak surface deformation that are severely contaminated by atmospheric delays. Here we propose a stacking method using time-series SAR images that can effectively suppress atmospheric phase screens and extract weak coseismic deformation in centimeter to sub-centimeter level. Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm. We also propose a strategy to downsample interferograms with weak deformation signal based on quadtree mesh obtained from preliminary slip model. With the downsampled datasets, we invert for the centroid locations, fault geometries and slips of these events. Our results demonstrate the potential of using time-series InSAR images to enrich earthquake catalog with geodetic observations for further study of earthquake cycle and active tectonics. Nanyang Technological University Published version This work was supported by the National Key Research and Development Program of China (Grant No. 2019YFC1509201) and the National Science Foundation of China (NSFC Grant Nos. U1939202 and 41974017). Shengji Wei is partially supported by Earth Observatory of Singapore grant 04MNS001909A620.

This paper reports on the activities carried out in the context of “Dragon project 32278: Three- and Four-Dimensional Topographic Measurement and Validation”. The research work was split into three subprojects and encompassed several activities to deliver accurate characterization of targets on land surfaces and deepen the current knowledge on the exploitation of Synthetic Aperture Radar (SAR) data. The goal of Subproject 1 was to validate topographic mapping accuracy of various ESA, TPM, and Chinese satellite system on test sites in the EU and China; define and improve validation methodologies for topographic mapping; and develop and setup test sites for the validation of different surface motion estimation techniques. Subproject 2 focused on the specific case of spatially and temporally decorrelating targets by using multi-baseline interferometric (InSAR) and tomographic (TomoSAR) SAR processing. Research on InSAR led to the development of robust retrieval techniques to estimate target displacement over time. Research on TomoSAR was focused on testing or defining new processing methods for high-resolution 3D imaging of the interior of forests and glaciers and the characterization of their temporal behavior. Subproject 3 was focused on near-real-time motion estimation, considering efficient algorithms for the digestion of new acquisitions and for changes in problem parameterization., European Space Agency

On June 17, 2019, an Ms 6.0 earthquake occurred in Changning, Sichuan, China (the Changning event), which is the largest earthquake within 50 km of the area since records began. It has attracted great attentions as this region is one of the largest shale gas production areas in China. The cause, the fault structure, and the earthquake effects remain the center of debates.Using Interferometric Synthetic Aperture Radar (InSAR) data, we measure the coseismic deformation and build the fault models of the Changning event and two earlier Ms>5.0 earthquakes (P1:2018/12/16 Ms5.7 and P2:2019/1/3 Ms5.3) using Sentinel-1 and ALOS2 satellite data. From the coseismic interference of ALOS2, the deformation caused by P1, P2, and the Changning event as well as some of their aftershocks can be clearly identified. The deformation caused by the Changning event affects an area of about 150 km2 and the surface deformation is mainly uplift with a maximum of 17.2 cm (towards the satellite). We obtain two fault models for the Changning event. The model inclining southwest has a smaller fitting error than the model inclining northeast and is more consistent with the distribution characteristics of aftershocks and local underground structure. The final model shows that the Changning event was caused by a fault with left-lateral strike and thrust slip. The strike is 124.9° with a dip angle of 49.8°. The inversed seismic moment is 4.79×1017 Nm, corresponding to Mw 5.75.On the basis of the fault models, we analyze the cause of the Changning earthquake from the following three aspects: (1) Stress change. The cumulative stress change of P1 and P2 on the Changning event fault is less than 0.1 MPa, which is too small to trigger an Ms 6.0 earthquake. Therefore, there is no direct triggering relationship between the Changing event and event P1 or P2. (2) Aftershock distribution. The aftershocks of the Changning event are negatively correlated with time. The Time-Number curve of the aftershocks well obeys the Omori-type aftershocks law. It is inconsistent with the characteristics of a triggered or induced earthquake which has more pre-earthquakes and rapidly decreasing aftershocks. (3) Tectonic backgrounds. The movement of the Changning earthquake fault is accord with the local tectonic motion. Moreover, the causative fault we inferred coincides with a fault located in the basement, which was found by the seismic reflection profile analysis. The fault in the basement is likely to be related to the Changning earthquake.Therefore, there is no direct evidence showing that the Changning earthquake was induced by shale gas production or other human activity. We consider that the event is a naturally tectonic earthquake.

A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.

Speckle inherent in synthetic aperture radar (SAR) images usually complicates visual interpretation and brings difficulty to information extraction for applications. Current speckle filters are mainly developed for single SAR image or an image pair (InSAR or PolInSAR). Although some multichannel filters are proposed, they only exploit pixel intensity to identify statistically homogeneous pixels (SHPs). In this paper, we present a new unified approach to filter multitemporal SAR images by adaptively estimating complex covariance matrix-based multitemporal filtering, named CCM-MTF. The key idea is to employ generalized likelihood ratio (GLR) test on the Wishart distributed initial CCM to evaluate the similarity between two pixels. A special design is given to the initial CCM estimation, in which temporal samples are used instead of spatially neighboring samples. Then, a threshold determined by the asymptotic distribution of the logarithm of GLR test statistics at a fixed significance level is used to select spatial SHPs for the reference pixel. Subsequently, the filtering is implemented by estimation of the final CCM from original SAR scattering vector over SHP pixels, and all filtered target information channels including intensity, interferometric phase, and coherence can be explicitly derived from the final CCM. The effectiveness of the proposed CCM-MTF method is validated by experiments on both simulated and real multitemporal SAR images. Both qualitative and quantitative comparisons between CCM-MTF and four state-of-the-art SAR filters are carried out to demonstrate its advantages in terms of speckle suppression as well as detail preservation for all the three information channels.

Detection of potential slope instabilities across wide areas using SAR interferometry (InSAR) techniques can aid prevention and mitigation of landslide disasters. Nevertheless, the low spatial density of coherent radar targets detected by conventional time series InSAR methods in mountainous areas with complex topography and vegetation cover introduces considerable uncertainties into the deformation measurements, and thus results in a high probability of omissions for landslide detection. In this study, a new time series InSAR method named Coherent Scatterers InSAR (CSI) is proposed to solve this problem through the joint exploitation of Persistent Scatterers (PS) and Distributed Scatterers (DS) to increase the quantity of Measurement Points (MPs) in rural environments. We applied this approach to detect potentially unstable slopes at a catchment scale, delineate sliding boundaries, and measure the deformation of major landslides. The archived ALOS PALSAR and ENVISAT ASAR data stacks covering the Danba County in the upper reach of the Dadu River Basin in southwest China, were processed. The PALSAR-measured deformation rate map revealed 17 suspected landslides and their sliding boundaries. By contrast, only six out of these 17 landslides were detected from the ASAR results, as the other 11 landslides were undetected due to the much lower density of coherent points. These detection results were indirectly verified by two different approaches. Two differential interferograms for recently acquired L-band ALOS-2 PALSAR-2 data pairs were visually inspected to check whether these landslides are still in active deformation. The reliability of the InSAR results was further evaluated by comparisons against field survey and in-situ GPS measurements. The spatial and temporal patterns of surface displacements at several slow-moving landslides were analyzed in detail, and general guidelines on the application of the CSI method for landslide investigations were discussed and summarized.

A major limitation for wide application of Synthetic Aperture Radar (SAR) remote sensing in mapping landslide surface displacements is the intrinsic gap between the ultimate objective of measuring three-dimensional displacements and the limited capability of detecting only one or two-dimensional displacements by repeat-pass SAR observations of identical imaging geometries. Although multi-orbit SAR observations of dissimilar viewing geometries can be jointly analyzed to inverse the three-dimensional displacements, the reliability of inversion results might be highly questionable in case of continuous motion because of the usually asynchronous acquisitions of multi-orbit SAR datasets. Aiming at this problem, we proposed an approach of retrieving time series three-dimensional displacements from multi-angular SAR datasets for step-like landslides in the Three Gorges area in this article. Firstly, time series displacements of a common ground target in the azimuth and line-of-sight (LOS) direction can be estimated using traditional methods of SAR interferometry (InSAR) and SAR pixel offset tracking (POT), respectively. Then, a spline fitting and interpolation procedure was employed to parameterize the displacement history in the sliding/dormant periods of step-like landslides and estimate displacements from multi-angular observations for identical date series. Finally, three-dimensional displacements can be inverted from these synchronized multi-angular measured displacements in traditional ways. As a case study, the proposed method was applied to retrieve the three-dimensional displacements history of the Shuping landslide in the Three Gorges area, China. Comparisons between SAR-measured displacements and measurements of global positioning system (GPS) showed good agreement. Furthermore, temporal correlation analyses suggest that reservoir water level fluctuation and rainfall are the two most important impact factors for the Shuping landslide stability.

InSAR technology provides a powerful tool to detect potentially unstable slopes across wide areas and to monitor surface displacements of a single landslide. However, conventional time series InSAR methods such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) can rarely identify sufficient measurement points (MPs) in mountainous areas due to decorrelations caused by steep terrain and vegetation coverage. In this study, we developed a new InSAR approach, coherent scatterer InSAR (CSI), to map landslide surface displacements in the radar line-of-sight (LOS) direction by combining persistent scatterers (PS) and distributed scatterers (DS). The key ideas of CSI include the employment of the generalized likelihood ratio (GLR) test for the identification of statistically homogeneous pixels (SHPs) and the use of the phase linking algorithm to estimate optimal phase for each DS pixel. The joint exploitation of PS and DS targets dramatically increases the spatial density of MPs, which makes the phase unwrapping more reliable. To demonstrate the effectiveness of the CSI approach, we applied it to retrieve the historical displacements of the Jiaju landslide in Danba County of southwest China using 19 L-band ALOS PALSAR images (2006–2011) and nine C-band ENVISAT ASAR images (2007–2008). Multiple comparisons clearly illustrated the big advantages of CSI over PSI and SBAS in mapping landslide displacements with more details owing to much higher (> 10 times) MP density. Furthermore, the superiority of L-band SAR data over C-band for landslide investigation in rural environments was confirmed. Quantitative validation of the CSI results for PALSAR data against in-situ GPS measurements suggested an accuracy of about 10.5 millimeters per year (mm/year) in terms of root mean square error (RMSE). Afterwards, the spatial-temporal characteristics of the Jiaju landslide surface displacements were summarized, with a new upper boundary for the active northern part delineated. Particularly, the northern part of the landslide moved faster than the southern part, exhibiting a maximum LOS displacement rate of around 120 mm/year. Subsequently, the fluvial erosion by the Dajinchuan River was identified as the predominant impact factor for the instability of the Jiaju landslide. Finally, the major problems and challenges for the application of CSI method were discussed, and the conclusions were given.

As the longest water transfer route in the world, the South-North Water Diversion Project (SNWDP) plays a key role in mitigating the serious water shortage problem in the arid north China, and thus the routine stability monitoring of this mega infrastructure becomes a critical issue to ensure continuous fresh water supply. Compared with traditional geodetic tools such as leveling and GNSS that can only measure deformation at discrete points along the canal, modern satellite Synthetic Aperture Radar Interferometry (InSAR) that can capture subtle ground deformation over wide areas is much more suitable to accomplish this challenging task. In this study, we carried out multi-scale InSAR analyses with multi-track Sentinel-1 data stacks to monitor deformation along the Middle Route canal of SNWDP. A coarse-resolution InSAR analysis was first carried out to map regional land subsidence in north China, and the result suggested that the Middle Route canal bypassed the huge subsidence funnel in the southeast part of Hebei province and exhibited overall stability. Afterwards, the results of relatively high-resolution InSAR analyses performed within the 5 km buffering zones along the entire canal revealed that a few sections were affected by significant embankment deformation or land subsidence. In particular, the high-filled canal sections are more prone to deform, such as the cases in Ye County and Jiaozuo City in Henan province. The validation of InSAR deformation measurements with synchronous leveling in Jiaozuo City indicated that a measurement accuracy of 6 mm was achieved. Another big problem identified is the severe uneven land subsidence along the Tianjin Branch Route, which may pose great threats upon the long-term safety of local water transfer operation, and must be handled properly to avoid further degradation.

The Xinmo landslide occurred in the early morning of 24 June 2017 at about 5:38 am local time. This catastrophic event caused enormous casualties and huge economic losses in Xinmo Village, Mao County, Sichuan Province, China. In this study, Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2), and C-band Sentinel-1 over the disaster area were collected and analyzed to characterize this landslide. The high-resolution TerraSAR-X intensity images were used to evaluate the landslide disaster and delineate the sliding area. Afterwards, two ALOS-2 PALSAR-2 image pairs and a stack of 45 Sentinel-1 images were processed to detect precursory movements of the landslide surface, using the conventional differential InSAR (DInSAR) method and advanced time series InSAR analysis. The unstable source area near the ridge was identified from the displacement rate map derived from Sentinel-1 datasets. The maximum displacement rate detected at the source area was −35mm/year along the radar line of sight (LOS) direction. The time series of LOS displacements over 2 years presents an easily discerned seasonal evolution pattern. In particular, a sudden acceleration of the displacement, dozens of days before the collapse was clearly captured by the Sentinel-1 observations, which might suggest that early warning of landslide disasters is possible given the availability of operational SAR data acquired in frequent repeat-pass mode, such as the Sentinel-1 twin-satellite constellation.

Acting as economic lifelines, bridges are essential infrastructure elements in China. The continuous monitoring of their stability therefore is of utmost significance to prevent the public death and injures resulting from bridge collapses. Typically, the deformation of bridges is detected by traditional on-structure surveying methods which are limited by the sparse measurements and large consumption of time and manpower. Thanks to the availability of numerous archived high-resolution spaceborne synthetic aperture radar (SAR) images coupled with the technology development of multi-temporal interferometry SAR, currently, millimetre level of subtle deformation monitoring and health diagnosis on bridges through the estimation of subsidence velocities using time-series SAR images has become a precise and cost-effective solution, assisting the conventional measures for bridges monitoring. Due to the fine level of detail information in X-band SAR images, the identification of even smaller bridges is poss...

With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.

Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage. In this paper, we first employ the Small Baseline Subset method to survey potential landslides in Guide County, Qinghai Province, which is identified as a loess landslide prone area for geological and climate conditions. Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks. Then, qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation. Finally, preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement. The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing, precursory information extracting, and early warning.

The dominant error source of Synthetic Aperture Radar Interferometry (InSAR) is atmospheric phase screen (APS), resulting in phase delay of the radar signal propagating through the atmosphere. The APS in the atmosphere can be decomposed into stratified and turbulent components. In this paper, we introduced a method to compensate for stratified component in a radar interferogram using ERA-Interim reanalysis products obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). Our comparative results with radiosonde data demonstrated that atmospheric condition from ERA-Interim could produce reasonable patterns of vertical profiles of atmospheric states. The stratified atmosphere shows seasonal changes which are correlated with time. It cannot be properly estimated by temporal high-pass filtering which assumes that atmospheric effects are random in time in conventional persistent scatterer InSAR (PSI). Thus, the estimated deformation velocity fields are biased. Therefore, we propose the atmosphere-corrected PSI method that the stratified delay are corrected on each interferogram by using ERA-Interim. The atmospheric residuals after correction of stratified delay were interpreted as random variations in space and time which are mitigated by using spatial–temporal filtering. We applied the proposed method to ENVISAT ASAR images covering Taiyuan basin, China, to study the ground deformation associated with groundwater withdrawal. Experimental results show that the proposed method significantly mitigate the topography-correlated APS and the estimated ground displacements agree more closely with GPS measurements than the conventional PSI.

Because of its centimetre-level precision, SAR interferometry (InSAR) has been widely recognized as an effective tool for large-area deformation mapping. However, applications of InSAR technology are limited by temporal–geometric decorrelation and atmospheric disturbance. Large-field displacement could be underestimated as a result of these limitations. A point-like target offset tracking (PTOT) method that solely uses SAR pixel amplitude information through highly accurate image matching can measure 2D displacements in the azimuth and range directions. This method is minimally affected by decorrelation and weather, and it is more suitable for mapping large displacements. In this study, landslide-prone areas were identified using high-resolution TerraSAR-X InSAR data pairs with short normal baselines and temporal baselines covering Zigui County in the Three Gorges area, and the PTOT method was applied to study potential landslides using image pairs with long temporal baselines. An estimate of the cumulative deformation was given. Time series displacements measured at corner reflectors and point-like targets were analysed. The feasibilities of landslide detection using DInSAR and PTOT methods with high-resolution SAR images were then evaluated and summarized.

Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the “salt-and-pepper” noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study.

The primary bottlenecks that hinder the widespread application of Differential Synthetic Aperture Radar Interferometry (DInSAR) technique for bridge monitoring are the difficulties in reverting three-dimensional (3D) deformation of these complex structures and achieving detailed structural risk assessment for end users. To address these challenges, we developed an improved time series InSAR analysis approach that can retrieve 3D deformation of bridges and enable a detailed structural risk assessment. The proposed strategy effectively integrates the observations from multiple SAR sensors to reveal the 3D deformation. Moreover, the selected point-like targets (PTs) are classified and linked to specific bridge features based on their temporal deformation models to make a detailed structural risk assessment. Our results demonstrate the effectiveness of integrating observations from multiple SAR sensors in studying bridge 3D deformation and investigating the structural risk.

Three dimensional displacements are more convenient for people understanding ground processes. However, only azimuth or line of sight (LOS) measurements can be directly extracted from SAR images which restrict further applications, especially for people who are not expertise in radar remote sensing. Aiming at this problem, we proposed an approach of retrieving time series three-dimensional displacements from multi-angular SAR datasets. Firstly, time series displacements in the azimuth and LOS direction can be estimated using traditional methods of SAR interferometry (InSAR) and SAR pixel offset tracking. Then, a fitting and interpolation procedure was applied to parameterize the displacement history and interpolate the displacements from different datasets with identical dates. Thus, three dimensional displacements can be obtained by making use of the different observation geometry of different SAR datasets. Our method was applied to retrieve three dimensional displacements history of the Shuping landslide, Three Gorges area, China.

A high spatial and temporal resolution of the precipitable water vapour (PWV) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally differenced maps of the spatial distribution of PWV by analysing the tropospheric delay "noise" in interferometric synthetic aperture radar (InSAR). Time series maps of differential PWV were obtained by processing a set of ENVISAT ASAR (Advanced Synthetic Aperture Radar) images covering the area of southern California, USA from 6 October 2007 to 29 November 2008. To get a more accurate PWV, the component of hydrostatic delay was calculated and subtracted by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapour. The InSAR-derived differential PWV maps were calibrated by means of the GPS PWV measurements over the study area. We validated our results against the measurements of PWV derived from the Medium Resolution Imaging Spectrometer (MERIS) which was located together with the ASAR sensor on board the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantage of the InSAR technique is that it provides water vapour distribution with a spatial resolution as fine as 20 m and an accuracy of ∼ 2 mm. Such high-spatial-resolution maps of PWV could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts. With the launch of Sentinel-1A and Sentinel-1B satellites, every few days (6 days) new SAR images can be acquired with a wide swath up to 250 km, enabling a unique operational service for InSAR-based water vapour maps with unprecedented spatial and temporal resolution.

Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution pattern of Linggang New City (LNC) in Shanghai, China, using space-borne synthetic aperture radar interferometry (InSAR) methods. Three data stacks including 11 X-band TerraSAR-X, 20 L-band ALOS PALSAR, and 35 C-band ENVISAT ASAR images were used to retrieve time series deformation from 2007 to 2010 in the LNC. An InSAR analysis from the three data stacks displays strong agreement in mean deformation rates, with coefficients of determination of about 0.9 and standard deviations for inter-stack differences of less than 4 mm/y. Meanwhile, validations with leveling data indicate that all the three data stacks achieved millimeter-level accuracies. The spatial distribution and temporal evolution of deformation in the LNC as indicated by these InSAR analysis results relates to historical reclamation activities, geological features, and soil mechanisms. This research shows that ground deformation in the LNC after reclamation projects experienced three distinct phases: primary consolidation, a slight rebound, and plateau periods.

In radar remote sensing education and training, only few people have a good knowledge of radar remote sensing and geology, which would be necessary fully analyse the surface motions estimated by persistent scatterer interferometry. Using scientific visualization, data can be presented in an intuitive way for surface motion analysis of non-radar experts. In this paper, we introduce a web-based interactive visualization of PS-InSAR point clouds for education and training.

Excessive groundwater exploitation is common through the Taiyuan basin, China, and is well known to result in ground subsidence. However, most ground subsidence studies in this region focus on a single place (Taiyuan city), and thus fail to demonstrate the regional extent of the deformation phenomena in the whole basin. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) time series analysis to investigate land subsidence across the entire Taiyuan basin region. Our data set includes a total of 75 ENVISAT ASAR images from two different frames acquired from August 2003 to September 2010 and 33 TerraSAR-X scenes spanning between March 2009 and March 2010. ERA-Interim reanalysis was used to correct the stratified delay to reduce the bias expected from the systematic components of tropospheric delay. The residual delay after correction of stratified delay can be considered as a stochastic component and be mitigated through spatial-temporal filtering. A comparison with MERIS (Medium-Resolution Imaging Spectrometer) measurements indicates that our atmospheric corrections improved agreement over the conventional spatial-temporal filtering by about 20%. The displacement results from our atmosphere-corrected time series InSAR were further validated with continuous GPS data. We found eight subsiding centers in the basin and a surface uplift to the north of Taiyuan city. The causes of ground deformation are analyzed and discussed in relation to gravity data, pre-existing faults, and types of land use.

Landslides constitute a major threat to people’s lives and property in mountainous regions such, as in the Three Gorges area in China. Synthetic Aperture Radar Interferometry (InSAR) with its wide coverage and unprecedented displacement measuring capabilities has been widely used in landslide monitoring. However, it is difficult to apply traditional InSAR techniques to investigate landslides having large deformation gradients or moving primarily in north-south direction. In this study, we propose a time series split-bandwidth interferometry (SBI) procedure to measure two dimensional (azimuth and range) displacements of the Shuping landslide in the Three Gorges area with 36 TerraSAR-X high resolution spotlight (HS) images acquired from February 2009 to April 2010. Since the phase based SBI procedure is sensitive to noise, we focused on extracting displacements of corner reflectors (CRs) installed on or surrounding the Shuping landslide. Our results agreed well with measurements obtained by the point-like targets offset tracking (PTOT) technique and in-situ GPS stations. Centimeter level accuracy could be achieved with SBI on CRs which shows great potential in futures studies on fast moving geohazards.

Thermal dilation is a vital component of deformation along the extensive railway network infrastructure. To monitor subtle deformation, the synthetic aperture radar interferometry (InSAR) technique has been adopted as a space-borne geodetic tool. However, InSAR applications in railway stability surveillance have been largely limited by the sparseness of detectable point-like targets (PTs). Moreover, only one-dimensional linear displacements in radar line-of-sight direction can be measured by a single data stack. To address these issues, we developed an improved persistent scatterers InSAR approach that can retrieve thermal dilation effects with an increased number of PTs along the railways. This proposed strategy effectively combines SAR amplitude, interferometric phase, and the spatial information of railway structures to maximize the number of PTs. A least square fitting of the residual phase obtained by iterative spatial-temporal filtering with respect to temperature difference is used to estimate the thermal dilation of metal and concrete-asphalt materials. To validate the effectiveness of this approach, case studies using ENVISAT ASAR (ASAR) and TerraSAR-X (TSX) datasets were carried out on the railways of Beijing–Tianjin, Beijing–Shanghai, and Shanghai–Hangzhou. Subsidence velocity, gradient, and thermal dilation were used to identify hazardous grades along each railway. Furthermore, linear deformation rates in two dimensions, i.e., vertical and west-east directions, along Shanghai–Hangzhou Railway were inverted from ascending ASAR and descending TSX observations to reveal track conditions at a high level of detail.

Danba County, located at the northwestern Sichuan, is one of the areas prone to severe landslides in China. The landslides in this area present great threaten upon the local public safety and the famous heritage architectures. Therefore, monitoring landslide is of great importance for the sustainable developments in Danba. In this paper, InSAR techniques is employed to investigate typical landslide activities, based on the multi-frequency SAR images acquired from C-band Envisat, X-band TerraSAR-X, and L-band ALOS-1/2 satellites. Firstly, differential InSAR (D-InSAR) is used to recognize known landslides and find potential unstable slopes in a region scale. Then, for a specific landslide, advanced multi-temporal InSAR method is exploited to characterize its surface deformation by obtaining time-series displacement on coherent targets. Furthermore, the triggering factors are discussed based on the deformation results and on-site surveys.

C- and X-bands Synthetic Aperture Radar (SAR) images acquired from February 2009 to September 2010 were processed with Persistent Scatterer Interferometry (PS-InSAR) algorithm to investigate spatial and temporal variations in deformation over Taiyuan City, China. The spatial pattern of subsidence and the magnitude of subsidence rate are similar in the velocity field maps achieved by the algorithm from these two data sets. It shows that there are four primary subsidence centers in Taiyuan City: Xiayuan, Wujiabao, Xiaodian, Sunjiazhai, which are near the groundwater extraction wells. The maximum subsidence rate is up to 70 mm/year at Sunjiazhai. The locus of maximum subsidence has shifted from its historical location in the north to the south. In view of the severe shortage of water resources and presented features of subsidence over Taiyuan City, we inferred that excessive pumping of groundwater was the dominant reason of land subsidence.

SAR interferometry (InSAR) is an effective tool for wide-area earth surface deformation detection and mapping. In this letter, Persistent Scatterers InSAR method was employed to derive the deformation histories of Huangtupo and Zhaoshuling landslides located in Badong County of the Three Gorges area. One Environmental Satellite C-band Advanced Synthetic Aperture Radar (ASAR) data stack acquired from descending orbit and two Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (PALSAR) data stacks collected from two adjacent ascending orbits were used. Different deformation trends were observed from the descending and ascending data stacks. Disagreements were found on Huangtupo and Zhaoshuling landslides between measurements of ASAR data and PALSAR data, which might be primarily attributed to different viewing geometries. Meanwhile, due to smaller decorrelation effects for longer wavelength, L-band PALSAR data can be used to detect more point-like targets than C-band ASAR d...

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.

Monitoring deformation of linear infrastructures such as expressway and railway caused by natural processes or anthropogenic activities is a vital task to ensure the safety of human lives and properties. Interferometric Synthetic Aperture Radar (InSAR) has been widely recognized as an effective technology to carry out large-area surface deformation mapping. However, its application in linear infrastructure deformation monitoring has not been intensively studied till now. In this article, a modified Small BAseline Subset (SBAS) method is proposed to retrieve the deformation patterns of the expressway. In our method, only the point-like targets identified on the expressway were kept in our analysis, and two complementary subsets of interferograms were formed to better separate the signals of height error and deformation from inteferometric phase observations. We successfully applied this method with multitemporal high-resolution TerraSAR-X images to retrieve the spatial-temporal pattern of surface deformati...

Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points.

The Heifangtai terrace located in Yongjing County of Gansu Province in northwest China is characterized by a large number of landslides with frequent failure events related to agricultural irrigation activities. Thus, it is a task of great importance to identify and monitor the active loess slopes and sort out the impact factors. In this study, we employed the time series InSAR method to map the active slopes in the Heifangtai terrace with two TerraSAR-X data stacks covering the year of 2016 and two Sentinel-1 data stacks from 2016 to 2018. The detected active spots are mainly distributed along the edges of the terrace. Time series displacements derived from TerraSAR-X and Sentinel-1 observations were validated by comparisons against GPS and crack gauge measurements. Good agreement between InSAR and GPS was achieved at slow moving locations, while unwrapping errors induced by sudden accelerations make InSAR underestimate the pre-failure displacements with respect to GPS measurements. The trends of InSAR-measured displacement and crack gauge measurements also match well with correlation higher than 0.75. InSAR-measured displacements at the Jiaojia landslide group were highly correlated with the bury of groundwater record, which suggested that the rising of groundwater level may seriously decrease the stability of loess slopes.

The application of SAR interferometry (InSAR) in topographic mapping is usually limited by geometric/temporal decorrelations and atmospheric effect, particularly in repeat-pass mode. In this paper, to improve the accuracy of topographic mapping with high-resolution InSAR, a new approach to estimate and remove atmospheric effect has been developed. Under the assumptions that there was no ground deformation within a short temporal period and insignificant ionosphere interference on high-frequency radar signals, e.g. X-bands, the approach was focused on the removal of two types of atmospheric effects, namely tropospheric stratification and turbulence. Using an available digital elevation model (DEM) of moderate spatial resolution, e.g. Shuttle Radar Topography Mission (SRTM) DEM, a differential interferogram was firstly produced from the high-resolution InSAR data pair. A linear regression model between phase signal and auxiliary elevation was established to estimate the stratified atmospheric effect from the differential interferogram. Afterwards, a combination of a low-pass and an adaptive filter was employed to separate the turbulent atmospheric effect. After the removal of both types of atmospheric effects in the high-resolution interferogram, the interferometric phase information incorporating local topographic details was obtained and further processed to produce a high-resolution DEM. The feasibility and effectiveness of this approach was validated by an experiment with a tandem-mode X-band COSMO-SkyMed InSAR data pair covering a mountainous area in Northwestern China. By using a standard Chinese national DEM of scale 1:50,000 as the reference, we evaluated the vertical accuracy of InSAR DEM with and without atmospheric effects correction, which shows that after atmospheric signal correction the root-mean-squared error (RMSE) has decreased from 13.6 m to 5.7 m. Overall, from this study a significant improvement to derive topographic maps with high accuracy has been achieved by using the proposed approach.

Synthetic Aperture Radar (SAR) offers many ways to reconstruct digital surface models (DSMs). The two most commonly used methods are SAR interferometry (InSAR) and stereo radargrammetry. Stereo radargrammetry is a very stable and reliable process and is far less affected by temporal decorrelation compared with InSAR. It is therefore often used for DSM generation in heavily vegetated areas. However, stereo radargrammetry often produces rather noisy DSMs, sometimes containing large outliers. In this manuscript, we present a new approach for stereo radargrammetric processing, where the homologous points between the images are found by geocoding large amount of points. This offers a very flexible approach, allowing the simultaneous processing of multiple images and of cross-heading image pairs. Our approach relies on a good initial geocoding accuracy of the data and on very fast processing using a massively parallel implementation. The approach is demonstrated using TerraSAR-X images from Mount Song, China, and from Trento, Italy.

Multiple InSAR techniques are increasingly being developed for earth observation. However, among them, persistent scatterers-based InSAR (PSI) techniques fail to obtain enough measurement points (MPs) in rural mountainous area due to the lack of persistent scatterers (PSs), such as ma-made structures, rocks, and outcrops, etc. In this paper, Distributed Scatterers-based InSAR (DS-InSAR) algorithm, exploiting both persistent scatterers and distributed scatterers (DSs) widely spreading in rural areas, is proposed to make up the limitation of persistent scatterers-based technique when monitoring mountainous landslides. There are two key steps to preprocess DSs in DS-InSAR algorithm: selecting DS candidates and estimating optimal interferometric phases. The selected DSs and PSs are combined for further processing using traditional PSI procedure. A qualitative and quantitative simulation analysis was operated to validate the DS-InSAR algorithm. Then, both PSI and DS-InSAR were implemented to monitor Xishan landslide in western mountainous region of Sichuan province, based on high-resolution TerraSAR-X images. The obtained results demonstrate that DS-InSAR could detect much more MPs and provide more reliable deformation information.

High-speed railways play important roles in connecting populated cities. Strict attention to ground subsidence is applied to ensure its transportation safety. Shanghai-Hangzhou high-speed railway is an important part of China's “Four Vertical and Four Horizontal” Passenger Line Network. In this study, TerraSAR-X and ASAR images were employed to recover the surface evolution along the high-speed railway using advanced InSAR technique. A spatial-temporal criterion is updated for PS identification. Nonlinear deformation is modeled and removed by a linear least squares fit. Measurements from TSX and ASAR during a closer period as well as the transverse subsidence profiles showing a fairly consistent agreement. According to the longitudinal subsidence profiles, although the subsidence gradient now is within the guidelines of high-speed railway, the increasing cumulative subsidence can still be a potential threat for the line.

In this paper, a new approach developed to retrieve temporally-differenced maps of the spatial distribution of water vapor from interferometric synthetic aperture radar (InSAR) data with a horizontal resolution as fine as 20 m is described. To get a more accurate map of wet delay, we used the ERA-Interim to predict the dry delay component in the total atmospheric delay from InSAR. In addition, the ERA-Interim was used to compute the conversion factors required to convert the wet delay to precipitable water vapor (PWV). The InSAR-derived differential PWV maps were calibrated by means of the ground-based GPS PWV measurements. The approach was applied to map the temporal change of PWV over the Southern California, USA. We validated our results against measurements of the PWV acquired from a Medium-Resolution Imaging Spectrometer (MERIS) onboard the ENVISAT satellite. The results show strong spatial correlation with values of uncertainty of less than 2 mm.

In this paper, we propose a modified unsupervised classification method for the analysis of polarimetric and interferometric synthetic aperture radar (PolInSAR) images using the intensity, polarimetric and interferometric contributions to the Shannon entropy characterization. In order to improve the classification accuracy where the polarimetric information is similar, the method gives intensity, polarimetric and interferometric information equal weighting to more effectively use the full range of information contained in PolInSAR data. In addition, this method uses an iterative clustering scheme which combines the expectation maximization (EM) and fast primal-dual (FastPD) optimization techniques to improve the classification quality. The first step of the method is to extract the Shannon entropy characterization from the PolInSAR data. Then, the image is initially classified respectively by the spans of the intensity, polarimetric and interferometric contributions to Shannon entropy. Finally, classification results of different contributions are merged and reduced to a specified number of clusters. An iterative clustering scheme is applied to further improve the classification results. The effectiveness of this method is demonstrated with DLR (German Aerospace Center) E-SAR PolInSAR data and CETC (China Electronics Technology Group Corporation) 38 Institute PolInSAR data.

In this paper, we carried out a combination of permanent scatterer and quasi permanent scatterer time-series InSAR image analyses to extract geometric information over the area of the Three Gorges Dam. For the first time, we measured and analyzed the deformation of the Three Gorges Dam and its surrounding area using 40 SAR images acquired from 2003 to 2008. Our results indicate that the temporal deformation of the left part of the dam has ceased and that the deformation of the dam was influenced by the changing level of the Yangtze River. Seasonal deformation due to varying temperature is also observed. The obtained results agree well with the published results of the Three Gorges Dam deformation obtained by employing conventional survey methods. We also found that there is an area of abnormal subsidence near Zigui County. This paper demonstrates the potential of time-series InSAR image analysis in the monitoring of dam stability and measurement of subsidence.

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2.

The phase coherence in synthetic aperture radar interferometry is often used in classification algorithms to detect possible temporal changes of the imaged terrain. However, in mountain areas, the interferometric coherence is also sensitive to the slight variations of the acquisition geometry. In this letter, we propose a very simple but effective method to separate the temporal decorrelation from the geometrical one. Assuming the imaged terrain can be modeled as a distributed target, the geometrical coherence can be estimated by exploiting a topographic model and the sensor acquisition parameters. The discrepancy between the geometrical coherence and the observed one can then be ascribed to temporal changes. Moreover, in presence of pointlike targets, the hypothesis of distributed terrain is no longer valid, and higher values of the observed coherence with respect to the synthetic geometrical one can be used to detect such targets. The proposed approach allows then in mountain areas the following conditions: (1) a simple and very fast rough estimation of the temporal coherence, and (2) the identification of pointlike targets using just two images. The method has been applied and tested in the Badong (China) site using European Remote Sensing satellite tandem data.

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up-to-date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub-pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS-2 SAR data. Validated by reference data derived from high-resolution colour-infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud-prone and rainy areas.

In this study, we explored the potential to improve urban impervious surface modeling and mapping with the synergistic use of optical and Interferometric Synthetic Aperture Radar (InSAR) imagery. W...

In this paper, an unsupervised change-detection approach was proposed to detect new urban areas from multi-temporal SAR images. The novelty of the proposed approach is the joint use of coherence and intensity characteristics of SAR imagery. The approach involves two main steps: (a) the extraction of difference feature containing information on changed areas, and (b) the unsupervised two-dimensional (2D) thresholding. First, two difference features based on the concepts of long-term coherence and backscattering temporal variability are extracted from a series of multitemporal SAR images. Then, the resulting features that represent the INSAR signal temporal variability of changed areas are merged, and a 2D thresholding technique based on the maximum 2D Renyi’s entropy criterion is developed to obtain the change-detection results. The effectiveness of the proposed approach is confirmed with experimental results obtained from a set of six ERS-1/2 SLC SAR images acquired in Shanghai, China.