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Persistent Scatterer Interferometry (PSI) is a time series remote sensing technique to estimate displacements of geo-objects from the interferometric phases of selected Persistent Scatterers (PS). The relative position of a scatterer within a resolution cell causes an additional phase contribution in the observed phase, which needs to be accounted for in PSI processing. Here we analyze the influence of this sub-pixel position correction on point localization and displacement quality. Apart from a theoretical evaluation, we perform experiments with TerraSAR-X, Radarsat-2, and Sentinel-1, demonstrating various levels of improvement. We show that the influence of the sub-pixel correction is significant for the geolocation of the scatterer (meter-level improvement), modest for the elevation estimation (centimeter-level improvement), and limited for the displacement estimation (submillimeter-level). For displacement velocities, we find variations of a few tenths of a millimeter per year. The effect of sub-pixel correction is most dominant for large orbital baselines and short time series.

Multi-temporal interferometric Persistent Scatterers Interferometry (PSI) techniques derive from the elaboration of satellite Synthetic Aperture Radar (SAR) images and represent a useful tool to detect ground millimetric movements over wide areas; thanks to non-invasiveness and high accuracy. However, PSI data are relative measurements estimated along the sensor Line Of Sight and referred to a chosen stable motionless reference point, so they lack absolute reference both in time and space. In this work, we propose a methodological procedure that exploits Global Navigation Satellite System (GNSS) data acquired from permanent stations to calibrate and fix relative InSAR results into conventional geodetic reference systems. Mean yearly velocities of PSI radar targets are corrected with GNSS values throughout operative procedures used in geodesy for crustal and local deformation data. The methodology is tested in Ravenna and Ferrara cities on the north-western Adriatic coast within the eastern alluvial plain of Po river (Italy), extensively affected by subsidence with strong spatial and temporal variations. The results reveal high rates of long-term subsidence of the study area and the effectiveness of the presented methodology for producing unique ground deformation maps over wide areas. .

In the Beijing Plain, land subsidence is one of the most prominent geological problems, which is affected by multiple factors. Groundwater exploitation, thickness of the Quaternary deposit and urban development and construction are important factors affecting the formation and development of land subsidence. Here we choose groundwater level change, thickness of the Quaternary deposit and index-based built-up index (IBI) as influencing factors, and we use the influence factors to predict the subsidence amount in the Beijing Plain. The Sentinel-1 radar images and the persistent scatters interferometry (PSI) were adopted to obtain the information of land subsidence. By using Google Earth Engine platform and Landsat8 optical images, IBI was extracted. Groundwater level change and thickness of the Quaternary deposit were obtained from hydrogeological data. Machine learning algorithms Linear Regression and Principal Component Analysis (PCA) were used to investigate the relationship between land subsidence and influencing factors. Based on the results obtained by Linear Regression and PCA, a suitable machine learning algorithm was selected to predict the subsidence amount in the Beijing Plain in 2018 through influencing factors. In this study, we found that the maximum subsidence rate in the Beijing Plain had reached 115.96 mm/y from 2016 to 2018. The land subsidence was serious in eastern Chaoyang and northwestern Tongzhou. In addition, the area where thickness of the Quaternary deposit reached 150–200 m was prone to more serious land subsidence in the Beijing Plain. In groundwater exploitation, the second confined aquifer had the greatest impact on land subsidence. Through Linear Regression and PCA, we found that the relationship between land subsidence and influencing factors was nonlinear. XGBoost was feasible to predict subsidence amount. The prediction accuracy of XGBoost on the subsidence amount reached 0.9431, and the mean square error was controlled at 15.97. By using XGBoost to predict the subsidence amount, our research provides a new idea for land subsidence prediction.

Since the 1970s, land subsidence has been rapidly developing on the Beijing Plain, and the systematic study of the evolutionary mechanism of this subsidence is of great significance in the sustainable development of the regional economy. On the basis of Interferometric Synthetic Aperture Radar (InSAR) results, this study employed the Mann–Kendall method for the first time to determine the mutation information of land subsidence on the Beijing Plain from 2004 to 2015. By combining the hydrogeological conditions, “southern water” project, and other data, we attempted to analyse the reasons for land subsidence mutations. First, on the basis of ENVISAT ASAR and RADARSAT-2 data, the land subsidence of the Beijing Plain was determined while using small baseline interferometry (SBAS-InSAR) and Persistent Scatterers Interferometry (PSI). Second, on the basis of the Geographic Information System (GIS) platform, vector data of displacement under different scales were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit and the displacement rate from 2004 to 2015 was obtained. Finally, a trend analysis of land subsidence was carried out on the basis of the Mann–Kendall mutation test. The results showed that single-year mutations were mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan. Among these mutations, the greatest numbers occurred in 2015 and 2005, being 1344 and 915, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water sources, and the edge of the groundwater funnel have undergone several mutations. Combining hydrogeological data of the study area and the impact of the south-to-north water transfer project, we analysed the causes of these mutations. The experimental results can quantitatively verify the mutation information of land subsidence in conjunction with time series to further elucidate the spatial-temporal variation characteristics of land subsidence in the study area.

Synthetic Aperture Radar Interferometry (InSAR) techniques have been long exploited for detecting and mapping slow-moving ground surface displacements due to their millimetre accuracy, non-invasiveness and wide area coverage. A review on different applications of Persistent Scatterers InSAR approaches, proposed and applied over Tuscany region (Italy) across time, is here presented. The study area is characterized by both subsidence of alluvial plains and landslides on hilly and mountainous reliefs. Tuscany has a leading role in Italy in the field of interferometric applications: the first InSAR analysis, which date back to 2003, have been performed at local basin scale, by exploiting various PSI-based approaches for risk mapping. First InSAR applications at regional scale date back to 2009, relying on historical SAR archives of ERS and ENVISAT satellites for updating subsidence and landslide inventory maps at a certain temporal date. Nowadays, the availability of Sentinel-1 SAR data with a regular and systematic 6-days acquisitions plan, allows a near-real time monitoring of deformative scenario at regional scale rather than a solely mapping of geo-hydrological phenomena. Last innovative InSAR applications over Tuscany region scan the territory, exploiting the regular repeat pass of Sentinel-1, and promptly highlight the sites affected by the highest ground movements with high temporal frequency. Such approach permits to pass from a static ‘picture’ of the regional slope instability up to a weekly-updated ‘movie’ with improved detail, useful for civil protection practices. These last ongoing works significantly enhance the value of multi-temporal InSAR approaches for investigating and managing geo-hazards over the Region.

International audience; Recent development by European Space Agency (ESA) involved the extension of the interferometric capabilities of the SentiNel Application Platform (SNAP) to enable SAR interferometric time series analysis through the Stanford Method of Persistent Scatterer (StaMPS) software package. In the current work, we demonstrate the enabled Persistent Scatterers Interferometry (PSI) processing of Copernicus Sentinel-1 mission data through SNAP-StaMPS integrated processing. A detailed overview of the processing steps involved as well as considerations and assumptions to exploit the SNAP-StaMPS synergy are presented. We intend to support the Earth Observation community by providing guidelines for Sentinel-1 TOPS data PSI processing using open source software packages. We demonstrate the SNAP functionality by processing two stacks of Sentinel-1 products over Mexico City (Mexico) and Rome (Italy).

The evaluation of landslide specific risk, defined as the expected degree of loss due to landslides, requires the parameterization and the combination of a number of socio-economic and geological factors, which often needs the interaction of different skills and expertise (geologists, engineers, planners, administrators, etc.). The specific risk sub-components, i.e., hazard and vulnerability of elements at risk, can be determined with different levels of detail depending on the available auxiliary data and knowledge of the territory. These risk factors are subject to short-term variations and nowadays turn out to be easily mappable and evaluable through remotely sensed data and GIS (Geographic Information System) tools. In this work, we propose a qualitative approach at municipal scale for producing a “specific risk” map, supported by recent satellite PSI (Persistent Scatterer Interferometry) data derived from SENTINEL-1 C-band images in the spanning time 2014–2017, implemented in a GIS environment. In particular, PSI measurements are useful for the updating of a landslide inventory map of the area of interest and are exploited for the zonation map of the intensity of ground movements, needed for evaluating the vulnerability over the study area. Our procedure is presented throughout the application to the Volterra basin and the output map could be useful to support the local authorities with updated basic information required for environmental knowledge and planning at municipal level. Moreover, the proposed procedure is easily managed and repeatable in other case studies, as well as exploiting different SAR sensors in L- or X-band.

International audience; Developing and improving methods to monitor both natural and non-natural environments such as forest and urban in space and time is a timely challenge. To overcome this challenge, we created a software platform - TomoSAR. The kernel of this platform supports the entire processing from SAR, Interferometry, Polarimetry, to Tomography (so called TomoSAR). The objective of this paper is to introduce this platform about its capability in Persistent Scatterers Interferometry (PSI) technique to estimate subsidence using TOPS Sentinel-1 data.

International audience; This paper presents new observation of the interseismic deformation along the Longitudinal Valley (Eastern Taiwan) that represents a major tectonic boundary of the Taiwan collision zone. We investigate the southern part of the Valley from Rueisuei to Taitung (latitude 23.5°N–22.7°N), which is the part of the Valley where interseismic surface creep has already been observed at some points of the Longitudinal Valley Fault (LVF). A Persistent Scatterer SAR interferometry approach (StaMPS) is applied using ten L-band SAR images from ALOS satellite acquired over the period 2007–2010. Interferograms from L-Band data show a dramatic improvement of coherence in comparison to previous studies using C-Band ERS data. The density of measurement resulting from StaMPS processing is the highest achieved so far in the area (about 40–55 points per km2 for a total of 77,000 points) allowing a continuous view of the deformation along the Valley and also giving information on its borders (Central Range and Coastal Range). The most striking feature of the resulting mean velocity map is a clear velocity discontinuity localized in a narrow band (0.1–1 km) along the LVF and responsible for up to 3 cm/yr velocity offset along the radar line of sight, which is attributed to shallow interseismic creep. InSAR results are in good agreement with continuous GPS measurements over the same period (0.3 cm/yr rms). The density of measurement allows us to improve fault trace map along the creeping section of the LVF (with accuracy of about 100 m) and to find new field evidences of the fault activity. In some places, our trace differs significantly (hundreds of meters) from previous published traces. The creep rate shows significant variations along the fault. At the southern end of the valley the deformation is distributed on several structures, including the Luyeh Strand, and drops significantly south of the Peinanshan. However there are discrepancies with previous studies made from ERS data over the period 1993–1999 that remain to be investigated. The mean velocity for each point of measure and the improved faults' trace are provided as Supplementary data.

This article presents the main results of the Persistent Scatterer Interferometry Codes Cross Comparison and Certification for long term differential interferometry (PSIC4) project. The project was based on the validation of the PSI (Persistent Scatterer Interferometry) data with respect to levelling data on a subsiding mining area near Gardanne, in the South of France. Eight PSI participant teams processed the SAR data without any a priori information, as a blind test. Intercomparison of the different teams' results was then carried out in order to assess any similarities and discrepancies. The subsidence velocity intercomparison results obtained from the PSI data showed a standard deviation between 0.6 and 1.9 mm/year between the teams. The velocity validation against rates measured on the ground showed a standard deviation between 5 and 7 mm/year. A comparison of the PSI time series and levelling time series shows that if the displacement is larger than about 2 cm in between two consecutive SAR-images, PS-InSAR starts to seriously deviate from the levelling time series. Non-linear deformation rates up to several cm/year appear to be the main reason for these reduced performances, as no prior information was used to adjust the processing parameters. Under such testing conditions and without good ground-truth information, the phase-unwrapping errors for this type of work are a major issue. This point illustrates the importance of having ground truth information and a strong interaction with the end-user of the data, in order to properly understand the type and speed of the deformation that is to be measured, and thus determine the applicability of the technique. © 2009 Elsevier B.V. All rights reserved.

Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.