Sentinel-1 InSAR Research Articles

Optimising sub-metre resolution 3D geomorphic change detection over large areas using multitemporal airborne laser scanning with Sentinel-1 InSAR and Sentinel-2 optical observations

Airborne laser scanning (ALS) is widely used in studies of Earth surface change and has potential to inform targeted landscape remediation over large areas. Leveraging this capability requires geomorphic change detection methods that exploit the full 3D information contained in ALS point clouds but remains challenging over large areas (i.e., > 10 km2). We developed a methodology for geomorphic change detection over large areas using multitemporal ALS in a multiscale model-to-model cloud comparison (M3C2) framework adapted for volumetric estimation. Time series Sentinel-2 optical observations were used to isolate persistently-bare areas as candidate sites to co-register the ALS point clouds. Geomorphic stability of those sites was determined from coherence change detection using time series Sentinel-1 InSAR, thereby ensuring only geomorphically-stable sites were used for co-registration. Results showed the Sentinel-based co-registration produced a closer vertical alignment (0.00 ± 0.09 m) between ALS point clouds over stable parts of the landscape, while co-registration using an iterative closest-point algorithm contained bias (0.07 ± 0.10 m). The methodology was used to estimate annual sediment yield for a semi-arid catchment in northeastern Australia and results were compared with long-term field-based stream sediment monitoring. The ALS-based geomorphic change detection estimated 2.58 ± 0.54 t·ha−1·a−1 sediment yield and stream sediment monitoring estimated 1.40 t·ha−1·a−1. These similar estimates indicate multitemporal ALS can produce realistic whole-of-catchment sediment yield estimates in ungauged catchments (i.e., with no stream sediment monitoring) and improves the spatial detail of those estimates. Accurately detecting geomorphic change from multitemporal ALS also required a strategy to manage vegetation-related error due to misclassification of ALS point clouds. Combined identification of fine-scale erosion processes and reliable estimation of catchment-scale erosion rates indicates the proposed methodology provides a valuable tool for planning landscape remediation over large areas.

Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR

Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman’s rank correlation coefficient (ρ) between this measure and the DCs from the Copernicus Emergency Management Service (DCCEMS), from MODIS + CHIRP (DC1km) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a ρ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. DC1km was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than DCCEMS and +300% than the temperature data. If a ρ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the DC1km values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in DC1km significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled “Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition” which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method.

Precursory crater contraction associated with the 2017 eruption of Shinmoe-dake volcano (Japan) detected by PALSAR-2 and Sentinel-1 InSAR

The time series of PALSAR-2 and Sentinel-1 images reveal inflation at the volcanic flank and contraction at the crater for approximately 5 months before the 2017 eruption of Shinmoe-dake volcano, Japan. While the observation of inflation at the volcano’s flank is ubiquitous, few studies have reported crater contraction preceding an eruption. The flank inflation stopped after the 2017 eruption, while the contraction at the crater continued until the 2018 eruption. We found that a pipe-shaped deformation source above sea level best fits the observation preceding the 2017 eruption. Suppose the flux of ejected materials constrains the conduit radius during the previous 2011 eruption. In that case, the amount of deformation of the pipe-shaped deformation source, whether open or closed at its top, is too large to be realistic. Although constraining the conduit radius from the eruption flux overestimates the pressure change of the pipe-shaped deformation source, water-saturated fractures along the volcanic conduit could extend the effective conduit radius of the pressure source. We propose one potential scenario for the mechanism of the crater contraction preceding volcanic eruptions based on the combination of compaction due to cooling by ambient groundwater and material withdrawal within the conduit. The groundwater inflows from the ambient aquifer through cracks in the porous conduit wall, which are generated by conduit expansion during the magma ascent. Decoupling from the conduit wall due to a decrease in volume of the material promotes material instability and crater contraction. The interaction between the groundwater and the magma triggers the 2017 eruption of Shinmoe-dake volcano, as previous studies have reported.Graphical

Spatiotemporal Pattern Detection of Ground Deformations Induced by Extreme Rainfall using InSAR EGMS: The case of Cortina d’Ampezzo after Vaia Storm

Abstract. During October 2018 Vaia Storm occurred in the north of Italy, resulting in a huge amount of rainfall that was recorded. The focus here is given to Cortina d’Ampezzo area in the Veneto Region, due to the high importance of the area from touristic and sport perspectives and the presence of landslides. This study is devoted to investigating the impact of the severe sudden rainfall caused by this extreme atmospheric disturbance on the trends of ground deformations over this area, adopting a risk assessment perspective. The ground deformation Time Series (TS) have been derived from the European Ground Motion Service (EGMS) Ortho products derived from Sentinel-1 InSAR data. Then, according to the daily precipitation records and considering the time of this event as a turning temporal point, the TSs of all data points have been divided into pre-event and post-event phases. After obtaining the displacement rates in each phase, the quantified magnitude of changes in the pre-post rates has been calculated through a proposed simple formulation. The spatial distribution of the deformation parameters (i.e., pre-event displacement velocity and change in the pre-post displacement velocities) is found to be a practical tool for gaining comprehensive knowledge regarding the ground deformation and the effect of the extreme weather conditions on the displacement trends over the area. Furthermore, the positioning of the occurrence of deformation parameters has been compared to the landslide inventory of the area. The results showed that the extreme rainfall caused severe acceleration of displacements, more significant in horizontal East-West direction rather than vertical Up-Down direction. Some slight deceleration has been also detected. No initiation (or reactivation) of the stable zones was witnessed, and the portions with high magnitude of pre-event velocities are the ones whose displacement rates have been remarkably modified due to the severe event.

Statistical and Independent Component Analysis of Sentinel-1 InSAR Time Series to Assess Land Subsidence Trends

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection.

Ground subsidence in major Philippine metropolitan cities from 2014 to 2020

Land subsidence is recognized as one of the hazards that threaten Metro Manila, Philippines, and other major urban areas worldwide. It has become a significant global issue caused by excessive groundwater extraction, rapid urbanization, and natural sediment compaction, exacerbated by climate change through rising sea levels. This paper presents vertical ground motion rates in Philippine metropolitan cities using Sentinel-1 InSAR time series analysis from 2014 to 2020 through UK COMET’s LiCSAR products and the LiCSBAS package of Morishita et al. (2020). The results revealed a maximum subsidence rate of 109 mm/yr in Bulacan Province in Greater Manila. For the first time, this paper also presents ground motion estimates and the following maximum subsidence rates for other Philippine metropolitan cities: 11 mm/yr in Metro Cebu, 38 mm/yr in Metro Davao, 9 mm/yr in Metro Iloilo, and 29 mm/yr in Legazpi City. Areas with remarkably high subsidence rates are observed as circular to elliptical deformation features in vertical motion maps. Sinking areas mostly coincide with industrial and commercial complexes evident as a contiguous distribution of large and expansive man-made structures with distinct radar reflections. Monitoring this hazard is crucial as it increases the risk of floods, building and infrastructure damage, and economic loss, and exposes residents along the coast to worsening tidal incursions and storm surges due to climate change. InSAR is proposed here as a targeted, long-term deformation monitoring tool, especially related to groundwater usage.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

Interseismic strain accumulation across the Tuolaishan–Lenglongling segment of the Qilian–Haiyuan fault zone prior to the 2022 Mw 6.7 Menyuan earthquake from Sentinel-1 InSAR time series

Interseismic strain accumulation across the Tuolaishan–Lenglongling segment of the Qilian–Haiyuan fault zone prior to the 2022 Mw 6.7 Menyuan earthquake from Sentinel-1 InSAR time series

Life Cycle Mining Deformation Monitoring and Analysis Using Sentinel-1 and Radarsat-2 InSAR Time Series

The life cycle of mining results in various patterns of surface deformation as it progresses through development, production, and reclamation. Therefore, the spatial–temporal patterns of ground deformation provide a crucial indicator to understand the mining activities, related geohazards, and environmental restoration. This study investigates the decadal deformation (2012–2022) of three coal mines during different stages of mines’ life cycles in Henan, China, using radar interferometry with Radarsat-2 and Sentinel-1 data. The results reveal multiple deformation patterns across different areas: the Changcun mine area changed from ground subsidence to uplift following the termination of exploitation in 2016; the Xiadian mine area has been continuously developing over the past decade, resulting in a cumulative subsidence of 55.6 mm; and the Liyuan mine area exhibits surface rebound at a rate of 7.9 mm/year since its closure in 2007. We also probe the mining geometry of the production process by using a rectangular model. This study highlights the significance of long-term InSAR observations and deformation modeling in elucidating the mining operation dynamics of small mining zones in their production, transition, and post-closure periods, thereby facilitating the management of small-scale mining.

Current active fault distribution and slip rate along the middle section of the Jiali-Chayu fault from Sentinel-1 InSAR observations (2017–2022)

The Jiali-Chayu fault, situated on the eastern side of the eastern Himalayan syntaxis, is the southeastern margin of the large strike-slip fault zone of the Jiali Fault. The study of the distribution and activity within this fault zone is imperative for a comprehensive understanding of the tectonic movement patterns in the southeastern Tibetan Plateau. Previous studies have established that the kinematic characteristic of the Jiali-Chayu fault diverges significantly from that of other segments within the Jiali fault. Nonetheless, the current tectonic characteristics, including the slip sense, slip rate, and geometric deformation of this fault, are still not well resolved, leading to divergent interpretations regarding its contemporary activity intensity. This paper introduced an optimized time-series InSAR method with phase compensation designed for regions characterized by low coherence and exhibiting slow deformation. Using Sentinel-1 SAR data from both ascending and descending orbits spanning the period between 2017 and 2022, we successfully derived deformation rates for the middle part of the Jiali-Chayu fault at a spatial resolution of 150 m. The slip and dip rates of active faults are determined by considering the fault movement rates from two different observation angles, in conjunction with strike angle and the assumed dip angle of the fault. The results show that the deformation rates of the three branches are very different, with F2-1 and F2-2 exhibiting notable activity, while other areas exhibit relatively weaker activity. The strike-slip rates for F2-1 and F2-2 faults range between 3.6 and 5.3 mm/a and 3.05 to 5.13 mm/a, respectively, while their respective dip-slip rates fall within the range of 1.1–2.7 mm/a and 2.99–5.02 mm/a. In accordance with the fault slip directions, we classify the F2-1 fault as a sinistral (left-lateral) strike-slip fault and the F2-2 fault as a dextral (right-lateral) strike-slip fault. This study addresses a gap in remote sensing methods for detecting active fault activity in this region, providing a systematic foundation for identifying weak activity characteristics within the fault zone.Graphical

Rapid subsidence in the Kathmandu Valley recorded using Sentinel-1 InSAR

ABSTRACT Urban subsidence poses significant challenges for rapidly developing cities. Published InSAR data reveal Kathmandu as a prime example, demonstrating an alarming rate of subsidence during rapid urban expansion. To monitor the spatiotemporal evolution of recent subsidence, we use Sentinel-1 Synthetic Aperture Radar (SAR) data and the LiCSBAS open-source processing package. Vertical surface motion maps from 2015 to present reveal many localized zones with high subsidence rates (>100 mm year−1), while the mountains that surround the valley have experienced slight surface uplift of ~5 mm year−1. The highest subsidence rate is ~200 mm year−1 and occurs in the centre of the Kathmandu metropolitan area. The distribution of subsidence in the valley matches with areas of the Pliocene to recent sediment up to 500 m thick. The deep aquifer compaction is likely to be the main driver of subsidence in the Kathmandu Valley. Time-series data show a dominant linear subsidence signal with weak sinusoidal signal peaks associated with groundwater recharge of shallow aquifers during the monsoon season. Subsidence rates decrease in proximity to the main river channels, likely driven by the seasonal recharge into the distal floodplain. The distribution of subsidence in the Kathmandu Valley has significant implications for future flood risk and infrastructure in the city.

2.5D Time Series of Postseismic Deformation Following the 2018 Mw 7.5 Palu Earthquake Using Sentinel-1 InSAR

The observed Displacement of Line of Sight (LOS) InSAR can only measure the displacement towards and away from the satellite sensor, making it difficult to provide an intuitive depiction of the deformation source. In the previous studies, research on the characteristics of postseismic deformation mechanisms was conduct by InSAR but only in the vertical component. Therefore, our study attempts to complement this deformation picture by considering both the horizontal and vertical components. It is hoped that this will provide a more comprehensive understanding of the postseismic deformation mechanism of the 2018 Palu earthquake. In this study, we attempted to estimate the 2.5-D surface deformation resulting from the 2018 Palu Earthquake using Sentinel-1 data. The data was then modeled using exponential and logarithmic functions to understand the characteristics of the postseismic deformation mechanism. The 2.5-D surface deformation exhibited variations in horizontal and vertical motions. The East-West (EW) displacement values showed a maximum value of -66.19 mm in the east direction, while the maximum value in the west direction is 75.23 mm. On the other hand, for the Up-Down (UD) displacement, there was a maximum subsidence of -73.40 mm and a maximum uplift of 67.28 mm. To study the characteristics of the transient postseismic deformation, observations were made at 14 points. Postseismic deformation was observed at all locations in the study area. During the period 2018-2021, the time series of the north and east components of the postseismic transients were analyzed using logarithmic and exponential functions. The modeling results showed that the Root Mean Square Error (RMSE) value for the exponential model is 81.80 mm, while for the logarithmic model is 81.38 mm. Therefore, the logarithmic model demonstrated a better fit, indicating that the postseismic deformation mechanism is influenced by afterslip.

A novel framework for combining polarimetric Sentinel-1 InSAR time series in subsidence monitoring - A case study of Sydney

The rapid growth of the city of Sydney, Australia over the last decades, has led to significant development of residential and transportation infrastructure. Land subsidence associated with the urban development can lead to serious issues which should be thoroughly understood and carefully managed. To address this challenge, an enhanced polarisation time-series InSAR (Pol-TS-InSAR) processing framework was developed, using the dual polarisation (DP) Sentinel-1 data to integrate information from different polarimetric channels with different weighting during the TS-InSAR deformation analysis. Ninety DP Sentinel-1 images acquired between 2019 and 2022 are analysed using Pol-TS-InSAR to map the land subsidence in Sydney, with the assistance of the GPS measurements. Improvement of measurement points density from Pol-TS-InSAR is observed compared to the single polarimetric TS-InSAR counterpart for all land use types (ranging between 68% and 208%). The comparison between the Pol-TS-InSAR measurements and GPS measurements shows an absolute mean difference and RMS difference of 0.75 mm/yr and 0.95 mm/yr, respectively, in vertical direction. The results of the ground subsidence analysis revealed that the main subsidence factors in Sydney are related to groundwater extraction, mining activities, underground tunnel construction and landfill. The latter two factors were less well-known prior to this study. In additional to these factors, land subsidence related to high-rise building construction has also been observed, even though the impact seems to be less significant than other factors.

Integrating MODIS LST and Sentinel-1 InSAR to monitor frozen soil deformation in the Qumalai-Zhiduo area, Qinghai-Tibet Plateau

ABSTRACT Owing to continuous global warming, frozen soil degradation has become a universal phenomenon, leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance. Geodetic observations, especially temporal InSAR, can quantify ground deformation. However, the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements. This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation. We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters, RSME, and characteristic targets. Our study found that the model incorporating MODIS LST performed better in areas far from weather stations, while both models produced similar results in areas of close proximity. Finally, we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.

Quantifying seasonal ground deformation in Taiyuan basin, China, by Sentinel-1 InSAR time series analysis

The presence of seasonal surface deformation in areas that are subsiding/uplifting due to groundwater pumping/recovery can indicate aquifer recharge. Understanding seasonal surface deformation and the existing relationship between triggering factors and the deformation supports effective groundwater management and subsidence mitigation. Here, we investigated seasonal ground displacement in response to the aquifer compaction and expansion due to groundwater extraction and recharge within the Taiyuan basin, Northern China. We obtained Sentinel-1 InSAR displacement time series data from 2017 to 2020 and quantitatively analyzed relationships between triggering factors (e.g., groundwater level and precipitation) and the associated displacements using wavelet-based methods. The results show that the seasonal deformation is concentrated in the irrigated areas within the central basin, with a 1-year periodicity. The annual peak-to-peak amplitude was up to 2 cm, peaking in March with troughs in August. This seasonality present in the displacement time series is found to be ‘in-phase’ with respect to groundwater levels and ‘anti-phase’ in relation to natural precipitation. These results are consistent with the irrigation pumping practices in the basin, with increased groundwater withdrawals to meet irrigation water demand prior to the monsoon precipitation season, resulting in water level decline and land subsidence from mid-March to mid-August. During the non-irrigated season, groundwater extraction decreases, and the aquifer systems are replenished by precipitation, causing water levels to recover and the ground to uplift from late August to mid-March. A geological analysis indicates that the seasonal deformation is controlled by the thickness of Quaternary strata and fault systems, which play a critical role in forming the earth fissures in the basin. The elastic and inelastic deformation was separated; the estimated elastic/inelastic ratio was about 0.6–0.7, indicating that the inelastic component dominates the main aquifer compaction in the central basin. Our analysis is of hydrologic importance which could be used to guide groundwater management and subsidence mitigation, given the inter-basin water diversion projects in this arid basin.

Time series analysis of the pre-seismic and post-seismic surface deformation of the 2017 Iran–Iraq earthquake derived from Sentinel-1 InSAR data

Time series analysis of the pre-seismic and post-seismic surface deformation of the 2017 Iran–Iraq earthquake derived from Sentinel-1 InSAR data

Sentinel-1 InSAR observations and time-series analysis of co- and postseismic deformation mechanisms of the 2021 Mw 5.8 Bandar Ganaveh Earthquake, Southern Iran

Sentinel-1 InSAR observations and time-series analysis of co- and postseismic deformation mechanisms of the 2021 Mw 5.8 Bandar Ganaveh Earthquake, Southern Iran

Coseismic deformation and stress triggering of the 2021 MS6.4 Yangbi earthquake inverted from integrating GNSS and InSAR displacement fields

On 21 May 2021, the MS6.4 Yangbi earthquake occurred at the Tibetan Plateau’s southeastern margin along the Weixi-Qiaohou-Weishan fault. We obtained the fine coseismic deformation fields by integrating 37 near-field GNSS observations and Sentinel-1 InSAR observations of the ascending and descending orbits. Considering that the earthquake did not rupture to the surface, the existing earth fault data is difficult to effectively constrain the deep underground fault. We used aftershock precision location data to constrain fault strike, dip angle, and depth, effectively integrate GNSS and InSAR data, and obtained the fault model closest to the real fault. Based on the coseismic slip distribution model, we calculated the Coulomb stress generated from the earthquake and discussed the triggering relationship between the change of coseismic Coulomb stress and the aftershock distribution. The results show that the maximum rupture occurred at the southeast side of the epicenter and the overall unilateral rupture characteristic. We calculated the stress change when the Yangbi earthquake ruptured, significantly reducing the stress difference between the fault and the surrounding area, which has a significant unloading effect on the surrounding faults and reduces the seismic risk of the surrounding faults.

Weather model based atmospheric corrections of Sentinel-1 InSAR deformation data at Turkish volcanoes

SUMMARYOne of the main constraints on the use of satellite radar data for monitoring natural hazards is the existence of atmospheric signals. In particular, volcanic deformation can be difficult to identify because atmospheric phase delays can mask or even mimic ground deformation signals. Eliminating atmospheric signals is particularly crucial for high-relief volcanoes such as Ağrı, Tendürek, Acigöl, Göllüdağ and Hasandağ in the Eastern and Central Anatolia. To overcome the atmospheric effects, we use high-resolution ECMWF weather models coupled with an empirical phase-elevation approach for correcting Sentinel-1 interferograms. We apply these methods to two areas of Turkey, the first of which covers three volcanoes in Central Anatolia (Acigöl, Göllüdağ, Hasandağ) between January 2016 and December 2018 and the second covers two volcanoes in Eastern Anatolia (Ağrı, Tendürek) between September 2016 and December 2018. The reduction in standard deviation (quality factor) is calculated for both ascending and descending tracks and the atmospheric corrections are found to perform better on descending interferograms in both cases. Then, we use a least-squares approach to produce a time-series. For Central Anatolia, we used 416 ascending and 415 descending interferograms to create 144 and 145 cumulative displacement maps, respectively, and for Eastern Anatolia, we used 390 ascending and 380 descending interferograms to produce 137 and 130 cumulative displacement maps, respectively. We find that the temporal standard deviation before atmospheric corrections ranges between 0.9 and 3.7 cm for the five volcanoes in the region and is consistently higher on ascending track data, which is acquired at the end of the day when solar heating is greatest. Atmospheric correction reduces the standard deviation to 0.5–2.5 cm. Residual signals might be due to the ice-cap at Ağrı and agriculture near Acigöl. We conclude that these volcanoes did not experience significant magmatic deformation during this time period, despite the apparent signals visible in individual uncorrected interferograms. We demonstrate that atmospheric corrections are vital when using InSAR for monitoring the deformation of high-relief volcanoes in arid continental climates such as Turkey.

Automatic detection and update of landslide inventory before and after impoundments at the Lianghekou reservoir using Sentinel-1 InSAR

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.