Comment on tc-2022-9

Interferometric synthetic aperture radar (InSAR) analysis is a radar technique for generating large-area maps of ground deformation using differences in the phase of microwaves returning to a satellite. In recent years, high-resolution SAR sensors have been developed that enable small-scale slope deformation to be detected, such as the partial block movement of a landslide. The L-band SAR (PALSAR-2) is mounted on Advanced Land Observing Satellite-2 (ALOS-2), which was launched on 24 Mar. 2014. Its main improvements compared with ALOS are enhanced resolution of as high as 3 m with a high-frequency recurrence period (14 days). Owing to its high resolution and the use of the L-band, PALSAR-2 can obtain reflective data passing through a tree canopy surface, unlike the other synthetic aperture radars. Therefore, the coherence of InSAR in mountainous forest areas is less likely to decrease, making it advantageous for the extraction of slope movement. In this study, to verify the accuracy of InSAR analysis using PALSAR-2 data, we compared the results of InSAR analysis and the measurement of the displacement in a landslide by global navigation satellite system (GNSS) observation. It was found that the average difference between the displacements obtained by InSAR analysis and the field measurements by GNSS was only 15.1 mm in the slant range direction, indicating the high accuracy of InSAR analysis. Many of the areas detected by InSAR analysis corresponded to the locations of surface changes due to landslide activity. Additionally, in the areas detected by InSAR analysis using multiple datasets, the ground changes due to landslide movement were confirmed by site investigation.

Land deformation caused by the overexploitation of groundwater has become a potential geological hazard in Changzhou, China. To reduce the potential damage from deformation disasters, it is quite necessary to monitor them. In this study, C-band ENVISAT ASAR synthetic aperture radar (SAR) datasets acquired from 2004 to 2010 were processed with the small baseline subset (SBAS) interferometric SAR (InSAR) method to investigate the spatial–temporal distribution of land deformation. Levelling survey data were used to assess the deformation accuracy measured by the InSAR method, and groundwater level data were used to analyse the incentives for deformation. The SBAS InSAR results found several subsidence regions in the Wujin district and the central part of the Zhonglou district. The annual deformation rate calculated from the C-band interferograms ranged from −65 to +35 mm/yr in the line of sight (LOS) direction, and the cumulative subsidence ranged from −0.35 to 0.13 m in the vertical direction. The levelling survey data show that the two measurements are generally consistent. Correlation analysis between the groundwater table and InSAR measurements at six groundwater well stations shows that groundwater recharges can cause land rebound within the Changzhou urban region. However, subsidence was also observed simultaneously with a rising water table, which might have been caused by the status of consolidation of the strata. Our study provides scientific evidence on the management of groundwater extraction and the assessment of land-subsidence hazard.

Ili Lewotolo Volcano, a stratovolcano in East Nusa Tenggara, Indonesia, erupted on November 27, 2020, following eight years of dormancy, releasing significant ash and sulfur dioxide (SO₂) emissions. This study is the first to combine multi-temporal Interferometric Synthetic Aperture Radar (InSAR) and SO₂ imaging for monitoring volcanic activity, offering a comprehensive pre- and post-eruption analysis approach. The New Small Baseline Subset (NSBAS)-InSAR method, using Sentinel-1 SAR data (from November 2019 to November 2020), detected notable ground deformation, while the Tropospheric Monitoring Instrument (TROPOMI) sensor on Sentinel-5P (from January 2020 to December 2021) captured fluctuations in SO₂ emissions. InSAR time-series analysis revealed significant ground uplift, indicating a magma source around 2000 m depth, confirmed through parametric optimization using the Mogi model. Furthermore, SO₂ levels surged by 512 % during the eruption compared to pre- and post-eruption periods, demonstrating a clear correlation between ground deformation and gas emissions. This combined approach provides deeper insights into volcanic behaviour and underscores the value of combining remote sensing techniques to enhance hazard mitigation strategies, paving the way for an effective early warning system for monitoring volcanic activity. • First study combining NSBAS InSAR & Sentinel-5P for Ili Lewotolo volcano dynamics, showing potential for global application. • NSBAS InSAR shows a 35 mm/ year ground deformation produced by over-pressurized magma at around 2000 m depth. • SO2 emissions peaked at 1.4 DU post-eruption, declining below 0.4 DU by 2021's end.

Over the last decade, Fengcheng oil field rich in heavy oil geological reserves has had a broad prospect of exploration. The reservoirs are mainly composed of high porosity, high permeability sandstone and conglomerate in study area. The reservoir characteristics led to poor fluid and gas injection causing ground surface deformation with the injection of gas and fluid and threatened the safety of nearby construction facilities. Interferometric Synthetic Aperture Radar (InSAR) technology has been used to identify ground surface deformation with high precision, which is widely used in various fields. This paper adopted Small Baseline Subset InSAR (SBAS-InSAR) method to monitor oil field for the period of February 2007 to September 2010. In this study, the Fengcheng oil field in Xinjiang served as an example; we measured subsurface fluid and gas injection-induced ground surface deformation by SBAS-InSAR technique and investigated the causes of observed deformation. The results showed significant uplift over Fengcheng oil field and testified the feasibility of SBAS-InSAR method. The Phased Arrary L-band Synthetic Aperture Radar (PALSAR) datasets outcome revealed ground surface maximum cumulative uplift for 4 years was 40 cm in the line of sight (LOS) direction, and annual average deformation rates were ranging from − 80 to 120 mm/year. Further, the time series analysis based on monitoring results found that the causes of ground surface uplift were related to the increase in pore pressure under the reservoir, and the surface deformation area corresponded to the production well position of study area. Therefore, InSAR technique is an essential tool for the ground surface deformation monitoring due to fluid and gas injection in heavy oil field, ensuring the safety of personnel and property.

Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

<p indent="0mm">Under the dual effect of global warming and human activities, permafrost degradation in the Qinghai-Tibet Engineering Corridor (QTEC), located in the hinterland of Qinghai-Tibet Plateau (QTP), has been intense, triggering a series of geohazards and posing significant challenges to the maintenance and safety of linear engineering infrastructures such as the Qinghai-Tibet Highway (QTH) and Qinghai-Tibet Railway (QTR). Ground deformation, as the primary indicator of common embankment and pavement distresses as well as related environmental geological hazards, can be as an effective proxy for engineering problems. While until now, there is no relevant ground deformation dataset covering the whole QTEC permafrost section, especially in the vertical (up and down, UD) deformation. In this study, LiCSAR (Looking Into Continents from Space with Synthetic Aperture Radar) products were firstly processed using time-series InSAR (Synthetic Aperture Radar Interferometry) via the LiCSBAS toolkit (an open-source InSAR time-series analysis package developed by the Looking inside the Continents from Space project) to obtain one-dimensional line-of-sight (LOS) deformation results of the surface ascending and descending orbits in the permafrost zone of the QTEC. Then, using SAR image acquisition dates as a constraint, we filtered out the ascending and descending orbit time-series InSAR deformation datasets with the same or similar acquisition dates for two-dimensional surface deformation, namely surface east-west (EW) and vertical (UD) directions, while ignoring north-south deformation. As a result, both one-dimensional (ascending and descending LOS) and two-dimensional (EW and UD) deformation velocities and cumulative time-series deformation for 51 periods from 2017 to 2022 were obtained. Comparison with field-based monitoring indicates that the vertical deformation results show an accuracy mostly within <sc>10 mm,</sc> with a maximum error not exceeding <sc>30 mm.</sc> This dataset can not only be used as a benchmark for subsequent InSAR deformation monitoring, but also provide data support and effective suggestions for the normal operation and maintenance of transportation linear projects, such as QTH and QTR. In addition, it can be used as input for the inversion of permafrost active layer thickness and other related geotechnical analyses.

On 21 May 2021, an earthquake of moment magnitude (Mw) 7.4 occurred near Maduo county in Qinghai Province China. This is the first major earthquake that occurred in the interior of the Bayan Har block in the Tibetan Plateau over the past 70 decades. Focusing on this event, we conducted a study on three-dimensional (3D) coseismic displacement reconstruction and its tectonic implication. We acquired both Synthetic Aperture Radar (SAR) and optical imaging satellite imagery, including SAR images from Sentinel-1 and Advanced Land Observation Satellite-2 (ALOS-2) as well as the multi-spectrum images from the Sentinel-2 (S2) satellite. We applied the Interferometric SAR (InSAR) and pixel-offset tracking (POT) techniques to coseismic SAR data pairs and reconstructed two dimensional displacements. With the constructed displacement fields in multiple viewing directions, we resolved the 3D coseismic displacements (north-south, east-west, and up components) by integrating the regional strain model with variance components estimation (SM-VCE). We recommend using the standard deviation value at each grid cell, which can be calculated in the resampling, as the initial weight. Based on the resolved 3D coseismic displacements, we further estimated the dip angles for the two segments (F4 and F5) in the east of the rupture zone. The associated moment magnitude is about Mw7.4, which corresponds to the released energy of ∼ 1.74×1020 Nm.

Over the past three decades, synthetic aperture radar (SAR) and SAR interferometry (InSAR) have revolutionized Earth observation, allowing for geophysical monitoring of Earth's surface deformation with centimeter‐to‐millimeter precision. InSAR time series are successive maps of ground motion used for natural hazards assessment with the monitoring of processes slowly deforming the Earth's surface, such as landslides, subsidence, or slow tectonic motion. In particular, the detection of transient displacements of millimeter amplitude is of utmost importance to better capture the dynamics of active tectonic faults, for a better understanding of the underlying physics governing the earthquake cycle. However, despite tremendous advances over the last two decades, noise affecting the data, primarily due to atmospheric delay, still challenges the detection of small deformation signals in InSAR time series. Consequently, an unknown amount of slow tectonic deformation signals remains concealed within the noise, raising the need for novel methodologies for signal extraction. We introduce InSARDenoiser, a spatiotemporal attentive convolutional U‐Net, designed to extract small‐scale deformation in noisy InSAR time series. The combination of a deep spatial U‐Net with a spatiotemporal Transformer tracks transient deformation in space and follows its temporal signature through time. We develop a mathematical formulation to generate realistic multi‐scale atmospheric noise and build a synthetic data set. When applied to a real InSAR time series along the North Anatolian Fault, the method effectively extracts millimeter‐scale slow deformation. The extracted deformation aligns with independent creepmeter measurements, thereby validating our method and opening the possibility of its application to diverse tectonic settings globally.

There is limited research focusing on Interferometric Synthetic Aperture Radar (InSAR) applications in the Great Lakes coastal wetlands with large water level fluctuations. In this study, we investigated the potential of using C-band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes. InSAR analysis was conducted using Radarsat-2 and Sentinel-1 data collected at Long Point, Ontario, Canada over the period of 2016–2018. Observations indicated that both backscattering coefficients and coherence from tall plants (e.g. cattail/Phragmites), short plants (e.g. grass), and water varied with different sensor modes (incidence angles and polarizations) in response to changes in phenology, disturbance, and water level. InSAR phase changes were closely related to fluctuations in water level and flow direction. We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error (RMSE). It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site, type of wetland vegetation, incidence angle and polarization. Although results from some sensor modes provided good correlation at a few locations, the low fringe rate and RMSE between 9 and 28 cm indicated that InSAR observations of water level changes were generally underestimated.

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes.

The long continuity of Interferometric Synthetic Aperture Radar (InSAR) can provide high space and resolution data for ground deformation investigations. The ground deformation in this paper appeared in the city’s development, although it is close to the Erhai region, which is different from a water-deficient city. Therefore, the analysis and prediction of ground deformation using a new method is required. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) images from 2015 to 2018 were used to study the characteristics of ground deformation in the Erhai region using the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. The results were cross-validated using ascending and descending direction images to ensure the accuracy. In addition, the results showed that there was little ground deformation in the northern part of the Erhai region, while there was obvious ground deformation in the southern part. Four influencing factors—including the building area, water level, cumulative precipitation, and cumulative temperature of the southern Erhai region—were used together to predict the cumulative ground deformation using back-propagation (BP). The R of all the involved data was 0.966, and the root mean square errors (RMSEs) between the simulated values using BP and the true measured values were 3.063, 1.003, and 1.119, respectively. The results showed that BP has great potential in predicting the change tendency of ground deformation with high precision. The main reason for ground deformation is the continuous increase of building area; the water level followed. The cumulative precipitation and cumulative temperature are the reasons for the seasonal ground deformation. Some countermeasures and suggestions are given to face the challenge of serious ground deformation.

Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering

Mapping high spatial resolution ionospheric total electron content by integrating Time Series InSAR with International Reference Ionosphere model

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR images with wider geographic coverage can be used to monitor ground deformation from local to continental scale. In such a large-scale application scenario, the ocean tide loading displacement introduces a long-wavelength error, which increases with distance, from millimetre to decimetre-level in InSAR interferograms over coastal areas. Despite great efforts being made to investigate the impacts of OTL on InSAR, these works are limited to individual interferograms and seldomly used in time-series analysis. In this study, we fully explore the OTL effects on Sentinel-1 InSAR time-series data along the western coast of the UK. We adopted wavelet analysis to indicate that the OTL displacement creates periodic signals with major cycles of 15 and 64 days under the 6- and 12-day Sentinel-1 sampling rates, respectively. These periodic signals are responsible for high noise magnitude of time-series displacement up to ~1cm and ~1 cm/yr bias on estimated velocities. An example is shown in Figure 1, where large velocity bias (a1-a3) and time-series standard deviation (b1-b3) can be seen along the western coastline of non-OTL corrected deformation fields, which are considerably eliminated after OTL correction. Our further validation against GNSS observations reveals that OTL correction improves the accuracy of large-scale InSAR time-series analysis by 25%.