Wide-Area InSAR Survey of Surface Deformation in Urban Areas and Geothermal Fields in the Eastern Trans-Mexican Volcanic Belt, Mexico

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece

The Methana volcano in Greece belongs to the western part of the Hellenic Volcanic Arc, where the African and Eurasian tectonic plates converge at a rate of approximately 3 cm/year. While volcanic hazard in Methana is considered low, the neotectonic basin constituting the Saronic Gulf area is seismically active and there is evidence of local geothermal activity. Monitoring is therefore crucial to characterize any activity at the volcano that could impact the local population. This study aims to detect surface deformation in the whole Methana peninsula based on a long stack of 99 Sentinel-1 C-band Synthetic Aperture Radar (SAR) images in interferometric wide swath mode acquired in March 2015–August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using the Interferometric Point Target Analysis (IPTA) method, involving the extraction of a network of targets including both Persistent Scatterers (PS) and Distributed Scatterers (DS) to augment the monitoring capability across the varied land cover of the peninsula. Satellite geodetic data from 2006–2019 Global Positioning System (GPS) benchmark surveying are used to calibrate and validate the MT-InSAR results. Deformation monitoring records from permanent Global Navigation Satellite System (GNSS) stations, two of which were installed within the peninsula in 2004 (METH) and 2019 (MTNA), are also exploited for interpretation of the regional deformation scenario. Geological, topographic, and 2006–2019 seismological data enable better understanding of the ground deformation observed. Line-of-sight displacement velocities of the over 4700 PS and 6200 DS within the peninsula are from −18.1 to +7.5 mm/year. The MT-InSAR data suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes. In Methana town, ground stability is found on volcanoclasts and limestone for the majority of the urban area footprint while some deformation is observed in the suburban zones. At the Mavri Petra andesitic dome, time series of the exceptionally dense PS/DS network across blocks of agglomerate and cinder reveal seasonal fluctuation (5 mm amplitude) overlapping the long-term stable trend. Given the steepness of the slopes along the eastern flank of the volcano, displacement patterns may indicate mass movements. The GNSS, seismological and MT-InSAR analyses lead to a first account of deformation processes and their temporal evolution over the last years for Methana, thus providing initial information to feed into the volcano baseline hazard assessment and monitoring system.

State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

Land subsidence is probably one of the most evident environmental effects of groundwater pumping. Globally, freshwater demand is the leading cause of this phenomenon. Land subsidence induced by aquifer system drainage can reach total values of up to 14.5 m. The spatial extension of this phenomenon is usually extensive and is often difficult to define clearly. Aquifer compaction contributes to many socio-economic effects and high infrastructure-related damage costs. Currently, many methods are used to analyze aquifer compaction. These include the fundamental relationship between groundwater head and groundwater flow direction, water pressure and aquifer matrix compressibility. Such solutions enable satisfactory modelling results. However, further research is needed to allow more efficient modelling of aquifer compaction. Recently, satellite radar interferometry (InSAR) has contributed to significant progress in monitoring and determining the spatio-temporal land subsidence distributions worldwide. Therefore, implementation of this approach can pave the way to the development of more efficient aquifer compaction models. This paper presents (1) a comprehensive review of models used to predict land surface displacements caused by aquifer drainage, as well as (2) recent advances, and (3) a summary of InSAR implementation in recent years to support the aquifer compaction modelling process.