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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

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.