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Countries with low to lower-middle income have limited resources to deploy and maintain ground monitoring networks. In this context, satellite-based techniques such as Radar interferometry (InSAR) is a great solution for detecting volcanic ground deformation at regional-scale. With the launch in 2014 of Sentinel-1 mission, regional monitoring of volcanic unrest becomes easier as SAR data are freely available with a revisit time of 6-12 days. Here, we develop a tuned processing workflow to produce Sentinel-1 InSAR time series and to automatically detect volcanic unrest over 80 volcanic systems located along the East African Rift System (EARS). First, we show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. For a 5-year times series (between Jan. 2015 and Dec. 2019), we show that statistically uncertainties in InSAR velocities are around 0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. For the automatic detection, we found that volcanic unrest can be detected with high confidence in the case the cumulative displacements exceed three times the temporal noise (threshold of 3σ). Based on this criterion, our survey reveals ground unrest at 16 volcanic centres among the 38 volcanic centres showing historical evidence of eruptive or unrest activity. A large variety of processes causing deformation occurs in the EARS: (1) subsidence due to contraction of magma bodies at Alu-Dalafilla, Dallol, Paka and Silali; (2) subsidence due to lava flows compaction at Kone and Nabro; (3) subsidence due to fluid migration at Olkaria and Aluto or fault-fluids interactions at Haludebi and Gada Ale; (4) rapid inflation due to magma intrusions at Erta Ale and Fentale; (5) short-lived inflation of shallow reservoirs at Nabro and Suswa; (6) long-lived inflation of large magmatic systems at Corbetti, Tullu Moje and Dabbahu. Except Olkaria and Kone, all these volcanoes were identified as deforming by previous satellites missions (between late 90’s and early 2000), which is an indication of the persistence of activity over long-time scales (>10 years). Finally, we fit the time series using simple functional forms and classify seven of the volcano time series as linear, six as sigmoidal and three as hybrid, enabling us to discriminate between steady deformation and short-term pulses of deformation. We found that the characteristics of the unrest signals are independent of the expected processes, which means that additional information (structural geology, seismicity, eruptive history and source modelling) will be necessary to characterize the processes causing the unrest. Our final objective will be to improve the transfer of this information to local scientists in Africa, which can be achieved by integrating our tools to an existing monitoring system and by developing web-platform where the InSAR products can be freely available.

The Alpine-Himalayan Belt (AHB) includes 75 percent of all earthquakes that have killed more than 10,000 people in the past century. Geodetic measurements of crustal deformation provide important information for studying earthquake hazards, indicating how the strain is accumulating and illuminating the mechanics of large-scale continental deformation. The COMET-LiCSAR InSAR processor was designed to automatically produce InSAR products on a global scale [1]. Processed data are made freely available to the community (https://comet.nerc.ac.uk/comet-lics-portal/). With the recent expansion of the system, we aim at generating high-resolution velocity field for the entire AHB. The area is covered by 644 ascending and descending frames. We have processed 130,000 Sentinel-1 epochs in this region and generated more than half a million interferograms. The average length of the connected small baseline network is 6 years. In some sub-regions such as the Anatolia, Caucuses, Iran, Tibet and Tianshan, more than 80 percent of all Sentinel-1 acquisitions are processed. In this study, we first used the LiCBSAS approach [2] to invert for the LOS displacement time-series and velocities. Next, following the VELMAP approach [3], we used the LOS velocities and the GNSS data to solve for the velocities in nodes of a spherical triangle mesh as well as the InSAR reference frame adjustment parameters. This results in the InSAR LOS velocities in a Eurasian reference frame. We finally decomposed these referenced LOS velocities into the east-west and vertical velocities. While the vertical velocities are mainly dominated by the anthropogenic displacements such as water pumping, or any other environmental parameters such as permafrost, the east-west velocity field exhibits the features of the long-wavelength deformation along the major faults in central, east and west of the AHB. References: [1] Lacecky et al., 2020 https://doi.org/10.3390/rs12152430 [2] Morishita et al., 2020 https://doi.org/10.3390/rs12030424 [3] Wang and Wright, 2012 https://doi.org/10.1029/2012GL051222

InSAR information to record dynamic changes on the surface using remote sensor has been obtained for the Metropolitan Area of San Salvador and compared with surface dynamic situation and fixed GPS surface movement information. The city is located in an active tectonic depression between the tectonic remains of ancient volcanoes and active volcanic centers. This area has been historically affected by earthquakes, volcanic activity, erosion, mass movements, and flooding. The morphology of the area has been changed by human activities, most of the time without any respect to natural processes thus increasing the problems. There are sectors that show subsidence close to Ilopango Caldera that could be connected to diastrophic block tectonic movements connected to post-seismic episodes after 2012 earthquake, and aquifer level changes due effects of climate change and overexploitation of aquifers. These movements may increase the huge problems of erosion and mass wasting through las Canas basin. In addition, after 2017 seismic swarm a horizontal movement was recorded with the satellite radar interferometry (InSAR) in two areas, which reflects the possible movement of faults that crosses the area of study. This type of information provides important data for the decision-making of land, protection and mitigation zones, monitoring of governmental institutions, and for regional planning.

At the northwestern tip of the India-Asia collision zone, the north-advancing Pamir orocline overrides the Tajik-Tarim Basin along a low-angle décollement with N–S shortening rates of 10-15 mm/yr. The Pamir’s advance is buttressed in the North by the Tian Shan. Westward collapse of Pamir crust into the Tajik basin leads to overall E–W shortening in the ~N-trending Tajik fold-thrust-belt. Crustal seismicity highlights fault systems at the northern rim of the Pamir and, since the 2017 Mw7.2 Sarez earthquake, the Sarez-Karakul fault system that separates the western from the eastern Pamir as a surface expression of the northwestern tip of the underthrusting Indian cratonic mantle lithosphere. Towards southwest, the Pamir connects to the rarely sampled Hindu Kush with sparse crustal but abundant intermediate-depth seismicity; the latter is an effect on an ongoing slab break-off.We recorded displacements along the most active structures creating the recent regional deformation field by multiple satellite-geodetic observations. Up to 4.5-yr-long radar-interferometric time-series (InSAR) provide E–W and vertical surface deformation fields in unprecedented spatio-temporal resolution of 400 m and 12-24 days. The relative InSAR rate maps were tied to and complemented with accurate rates derived from Global Navigation Satellite System (GNSS) data. We collected these data in continuous and survey mode along—sometimes km-spaced—profiles across the most active faults in the region.We confirm the high interseismic strain localization along the Pamir’s northern thrust front and an increased dextral component towards the northwestern edge of the thrust belt of >8 mm/yr, accommodating the westward collapse of the orocline. The sinistral Sarez earthquake at 120-170 km distance from the front activated the basal décollement, as suggested by mm-to-cm-scale, sharp surface offsets along the whole frontal segment. Relocking occurred gradually in the following four years. Similar co-seismic offsets were observed along the sinistral, NE-trending Darvaz fault, separating the western Pamir from the Tajik basin. The Tajik fold-thrust-belt exhibits ~10 mm/yr of internal E–W shortening, in agreement with fossil shortening rates of 12-8 mm/yr since ~12 Ma. The majority of the deformation is accommodated by the Babadag backthrust (~6 mm/yr)—a major thrust located far west in the belt, and the sinistral Ilyak fault (~6 mm/yr) that bounds the belt to the North. The belt also hosts spectacular horizontal spreading rates of ≤350 mm/yr at the Hoja Mumin salt fountain. Along the most prominent fault of the Hindu Kush, the Panjsheer fault, a fault-perpendicular GNSS profile observed sinistral slip of >1-4 mm/yr. The fault is probably only locked in the upper ~km as suggested by a sharp, InSAR line-of-sight rate increase of ~6 mm/yr across the fault. This could explain the absence of shallow seismicity in the region.

Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms.

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article., This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science—LQ1602” and from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center—LM2015070”.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large- scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products., This work was partially undertaken on ARC4, part of the High Performance Computing facilities at the University of Leeds, UK.

Our automatized interferometric monitoring system, IT4S1, contains a database of Sentinel-1 satellite image bursts that have been preprocessed to the state of a consistent well-coregistered dataset. The coregistration solution introduces a new type of data, an SLC-C (corrected single look complex data). These are SLC images ready for interferometric analyses with maximally reduced phase signature of topography and flat-Earth phase. The further processing time for multitemporal interferometry techniques is significantly reduced for achieving velocity maps based on e.g. Persistent Scatterers or Small Baseline techniques. In this paper, authors focus on quality of estimated topographic phase, finding out that computation of perpendicular baseline between satellite positions based on precise orbit ephemerides can differ and thus the approach may suffer from a residual topographic signature or a flat-Earth phase. The paper presents conclusions taken by an investigation of one such pre-prepared dataset over a mountainous area in southern Spain. Though some orbital inparallelity occurs during Sentinel-1 acquisitions (causing inaccuracies of perpendicular baselines within first meters), the flat-Earth is removed properly using this approach. The paper further demonstrates results using SLC-C approach using available and custom processing techniques and notifies about the possibility that in terms of overall accuracy and precision of multitemporal interferometry this approach contains inaccuracies that can be either estimated as part of error phase screens or even neglected.

After two decades of SAR satellite operation, their data, processed by interferometric techniques (InSAR), allows for a sensitive detection of terrain movements. Various projects have risen towards the detection of landslide activity and thus possibility of early warning system based on SAR data. The ability of InSAR techniques to detect a slope stability is limited and depends on slope orientation, movement rate, vegetation cover and temporal and spatial image resolution. This paper presents common issues found in past projects of the team of authors observing landslides using available satellite SAR data processed by various InSAR techniques. It is demonstrated that in some cases the basic differential InSAR combining only two images provides more valuable information compared to advance multi-temporal InSAR techniques. The paper aims at providing guidelines for maximal successful detection of landslide activity and to introduce pros and cons of the InSAR method, which often discussed in geology and geophysics circles. We prove the potential of techniques using satellite-based InSAR to identify creeping movement of structures built at moving slopes and the limited possibility to detect movements at moderately vegetated slopes by using especially the L-band or a combination of winter-season SAR and other images within very short temporal difference. It is possible to overcome misinterpretation of results by proper understanding of InSAR source phase component at slopes, including phase distortions due to SAR geometry, vegetation movement (vegetated mass movement) and atmospheric pressure changes correlated with height differences.

The topic of this work covers current implementation of satellite radar (SAR) interferometry (InSAR) techniques for routine identification of dynamic land processes such as downhill creep and landslide activity, subsidence or displacements of various objects of infrastructure. With the emerge of European Copernicus programme, the need of effectivity in satellite Big Data processing increased. There are two Sentinel-1 satellites observing the Earth with 6 days revisit time, sending daily 100 TB of data to be archived. In case of the relatively small area of Czechia, the amount of data to be archived in a Czech national mirror is around 24 GB per day. Czech CESNET e-infrastructure has accepted the role of assessing Copernicus Ground Segment programme. A database mirroring Sentinel data over Czechia is established, however still in its early stage. A potential service based on an interferometric processing of Sentinel-1 data from this database has been prepared in Czech national supercomputing center IT4Innovations. As a basis of the system, several open-source projects were deployed, including MySQL-based burst metadatabase (TU Leeds), ISCE TOPS Processor (NASA/JPL), Doris coregistration algorithm (TU Delft) and StaMPS Small Baselines processor (Stanford University). Though more functionality can be rapidly developed, incorporating some of the own post-processing algorithms, even current early version of the system can yield interesting results by a fully automatic processing chain.

Currently, ongoing global climate change brings, among other things, an increasing frequency of extreme weather events such as heavy rains, which can cause flash floods. Responsible authorities have tried to develop systems for early warning of such events. Such systems already exist in the US and in some European countries. They often rely on the prediction of extreme rainfall, possibly with the use of weather radar data, as well as rainfall-runoff models. The weakness in these systems, which limits their global usage, is based on the precise use of rainfall-runoff models and the attempt to quantify the impacts of extreme rainfall in the affected area. Therefore, we have developed a methodology based on the simplified data inputs (data from weather radar) that release a warning for potentially vulnerable areas in the longest time possible before extreme rainfall effects are due to occur. Our ambition is not to quantify these effects. Due to the short time interval between downpours and flash floods caused by them, we do not consider this information to be significant. We decided to test our methodology inter alia on a case of regional flooding, which was the result of regional precipitations combined with extreme local rains. The results presented in this paper show that, even in this situation, the proposed methodology allows us to provide an early warning for the population to take refuge in a safe site.