InSAR Methods Research Articles

Surface deformation analysis due to the Poso earthquake on May 29, 2017 using the DInSAR method

Abstract Deformation can be a significant parameter in determining the presence and impact of an earthquake. The amount of deformation can be obtained from SAR image data using the InSAR and DinSAR methods. The DinSAR (Differential Interferometric Synthetic Aperture Radar) method is a well-developed method for observing subsidence and uplift with high accuracy in centimeters (cm). In this study, SAR imagery is used to detect surface deformation caused by the earthquake was happened on May 29, 2017, at 21:35:23 WIB in the Poso Regency, Central Sulawesi. The data used is Sentinel-1 satellite imagery data in single-look complex (SLC) format consisting of Master image recorded on May 19, 2017 (10 days before the main earthquake occurred) and Slave image recorded on May 31, 2017 (2 days after the main earthquake occurred). The interferogram formed during the Poso earthquake is around the main earthquake directions from the Southwest, South, to the Southeast. The amount of deformation generated in the deformation phase (wrapped phase) is in the form of negative and positive phase values. Negative phase values indicate areas experiencing deformation in the form of subsidence. The positive phase values indicate areas experiencing uplift. The results of unwrapping the SAR imagery obtained a maximum uplift deformation value of 0.0663 meters, spread over the areas of North Pesisir Poso District, Lore Peore District, Central Lore District, South Coastal Poso District, and East Lore District. The maximum subsidence deformation values (0.076 meters) that are quite large are spread in the North Coastal Poso District, Poso Coastal District, Lage District, and Lore Tengah District.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Wide-Area Subsidence Monitoring and Analysis Using Time-Series InSAR Technology: A Case Study of the Turpan Basin

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Active landslide detection using integrated remote sensing technologies for a wide region and multiple stages: A case study in southwestern China

While significant progress has been achieved in utilizing remote sensing technologies for landslide investigation in China, there remains a notable gap in consolidating information on applicable conditions, application stages, and workflows across various remote sensing methodologies. This paper proposes a comprehensive framework for active landslide detection, incorporating multiple stages and data sources, successfully implemented in a vast region of southwestern China. Furthermore, detailed discussions are provided on the effects of the geometric distortion, land cover type, and various InSAR methods on the accuracy of active landslide identification results. Additionally, the paper delves into the advantages of integrated remote sensing technology in active landslide investigation, encompassing the assessment of current landslide activity status, precise delineation of boundaries, identification of different deformation stages, and determination of damage patterns. Through comprehensive analysis of multisource data, it enhances understanding of the active landslide process, ultimately contributing to the mitigation of casualties and property damage.

Mining Deformation Monitoring Based on Lutan-1 Monostatic and Bistatic Data

Coal mining leads to surface subsidence, landslides, soil erosion and other problems that seriously threaten the life and property safety of residents in mining areas, and it is urgent to obtain mining subsidence information using high-frequency, high-precision and large-scale monitoring methods. Therefore, this paper mainly studies the deformation monitoring of the Datong mining area using Lutan-1 monostatic and bistatic SAR data. Firstly, the latest Lutan-1 bistatic data are used to reconstruct the DSM, and the interferometric calibration method is used to improve the accuracy of the DSM. Then, the surface deformation monitoring of the mining area is implemented by using DInSAR, SBAS-InSAR and Stacking-InSAR with the reconstructed DSM data and Lutan-1 monostatic SAR data. Finally, the deformation monitoring results are compared with the surface deformation results based on the TanDEM data, and both the results are evaluated using the filed leveling data. Taking 20 images covering the Datong mining area as the data sources, the surface deformation results obtained using different InSAR methods in the mining area were quantitatively evaluated and analyzed. The results indicated that: (1) the DSM obtained using the Lutan-1 bistatic SAR data was assessed and demonstrated with the ICESat laser altimetry data an error of 2.8 m, which meets the Chinese 1:50,000 scale DEM cartographic accuracy standard, and the difference analysis with the TanDEM data shows that the terrain changes are mainly distributed in mountainous areas; (2) Due to the improvement in resolution, the registration accuracy of the SAR images and LT-DSM is higher than that of the TanDEM data in the range direction and azimuth direction; (3) Via evaluation with the filed leveling data, it is found that the surface deformation measurement results based on LT-DSM are less affected by terrain, and the accuracy of LT-DSM-SBAS and LT-DSM-DInSAR is improved by 11.5% and 16.3%, respectively, compared with TanDEM-SBAS and TanDEM-DInSAR, which demonstrates the effectiveness of the Lutan-1 bistatic and monostatic data for mine deformation monitoring.

INTEGRIRANI PRISTUP KOD ANALIZA RIZIKA OD ODRONA

The paper presents some elements of intergated approach in rockfall risk analyses prepared for zone of Macedonian village Staro selo in Polog area. The wider zone of Polog is characterized with pronounced rockfall, landslide and flood hazards in general terms, where several critical locations exists. The results from rockfall simulation analyses, contemporary and innovative terrain surveying techniques used in detection of hazardous zones with LIDAR and INSAR methods and other data are presented. Analyses confirmed the possibility for detachment of rockfalls, which is observed in reality, and not only in the exact zone of the village. Integrating findings from rockfall simulations with benefit and cost analysis of suggested protection mesaures, some recomendations from the aspect of tolerable level of risks including possible trends suggested in new Eurocode 7 generation are presented. The main findings leads to the conclusion, that interaction and integration of technical, economic and social aspects and involving different perpectives is necesary in process of rockfall risk management.

Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning.

Coseismic and Early Postseismic Deformation of the 2020 Mw 6.4 Petrinja Earthquake (Croatia) Revealed by InSAR

The largest earthquake (Mw 6.4) in northwestern Croatia ruptured the faults near the city of Petrinja on 29 December 2020, at 11:19 UTC. The epicenter was located ~3 km southwest of Petrinja, ~40 km southeast of Zagreb, the capital of the Republic of Croatia. Here we investigated the geometric and kinematic properties of the 2020 Mw 6.4 Petrinja earthquake using a joint inversion of ascending and descending interferograms from three tracks of Sentinel-1 Single-Look Complex (SLC) images. The coseismic and early postseismic surface displacements associated with the Petrinja earthquake were imaged using standard DInSAR and SBAS time-series InSAR methods, respectively. The distributed slip model was inverted based on the ground surface displacements with maximum slip patch in 5 km depth. The early postseismic deformation occurred on the northwestern extent of coseismic slip, and it cannot be well modeled by the coseismic model. We thus suggested that the postseismic deformation was caused by a combined effect of the postseismic afterslips and aftershocks occurring in this area. Based on the inverted slip model, we calculated the Coulomb stress change in the region, and found a good correlation between positive Coulomb failure stress ∆CFS and the distribution of aftershocks. Based on these results, we identified which faults are more active, and then better estimated the seismic hazards in the region.

Effectiveness evaluation of DS-InSAR method fused PS points in surface deformation monitoring: a case study of Hongta District, Yuxi City, China

InSAR technology provides a powerful tool for detecting large-scale surface deformation. In particular, the newly developed DS-InSAR method fused PS points has obvious advantages in monitoring bare land and vegetation covered areas, but at present, there is a lack of effectiveness evaluation of this method. Therefore, this paper takes Hongta District of Yuxi City as an example. The 29 scenes sentinel-1A data from January 2019 to December 2019 were processed and analyzed using the DS-InSAR method fused PS points. The research results show that the position and deformation trend of the inversion results of the three methods are highly consistent, the correlation between DS-InSAR method fused PS points and PS- InSAR and SBAS-InSAR methods is 0.9473 and 0.8583, respectively. But the spatial density of the measurement points (MPs) obtained by the DS-InSAR method of PS points is 11 times and 4 times that of the PS-InSAR method and SBAS-InSAR method respectively. It clearly shows that DS-InSAR method fused PS points has greater advantages than PS-InSAR and SBAS-InSAR methods in deformation mapping displacement, because the MPs density is higher. It is conducive to the detailed analysis of the spatio-temporal characteristics and deformation mechanism of deformation.

A New Data Processing Method for High-Precision Mining Subsidence Measurement Using Airborne LiDAR

Coal resources are the principal energy in China, and the surface subsidence caused by coal mining has a serious impact on the safe production and life of human beings. The traditional observation method of rock movement is slow and laborious, while the accuracy of airborne LiDAR, InSAR and other methods is relatively low. In this paper, aiming at the problem of the low accuracy of deformation monitoring of airborne LiDAR, the data registration of LiDAR point cloud is analyzed by combining theoretical analysis with field experiment. An advanced distribution mode of control points is discussed, and a current method of multi-period point cloud registration using seven-parameter transformation is proposed to obtain a surface subsidence model for mining area with high precision. The results show that the RMSE of airborne LiDAR is decreased from 0.013 m to 0.008 m by using the new method for data registration, and the maximum error value is reduced from 0.022 m to 0.014 m, which effectively enhances the deformation monitoring capability of airborne LiDAR.

Three-Dimensional Surface Displacements of the 8 January 2022 Mw6.7 Menyuan Earthquake, China from Sentinel-1 and ALOS-2 SAR Observations

The 8 January 2022 Mw6.7 Menyuan earthquake was generated in the transition zone between the western Lenglongling fault and the eastern Tuolaishan fault, both being part of the Qilian–Haiyuan fault system with an important role in the adjustment of the regional tectonic regime. In this study, four pairs of SAR (synthetic aperture radar) data from Sentinel-1 and ALOS-2 (Advanced Land Observation Satellite-2) satellites were used to derive the surface displacement observations along the satellite line-of-sight (LOS) and azimuth directions using the differential interferometric SAR (InSAR, DInSAR), pixel offset-tracking (POT), multiple aperture InSAR (MAI), and burst overlap InSAR (BOI) methods. An SM-VCE method (i.e., a method for measuring three-dimensional (3D) surface displacements with InSAR based on a strain model and variance component estimation) was employed to combine these derived SAR displacement observations to calculate the 3D co-seismic displacements. Results indicate that the 2022 Menyuan earthquake was dominated by left-lateral slip, and the maximum horizontal and vertical displacements were 1.9 m and 0.6 m, respectively. The relative horizontal surface displacement across the fault was as large as 2–3 m, and the fault-parallel displacement magnitude was larger on the southern side of the fault compared with the northern side. Furthermore, three co-seismic strain invariants were also investigated, revealing that the near-fault area suffered severe deformation, and two obviously expanding and compressed zones were identified. We provide displacements/strains derived in this study in the prevailing geotiff format, which will be useful for the broad community studying this earthquake; in addition, the SM-VCE code used in this study is open to the public so that readers can better understand the method.

Signals of Surface Deformation Areas in Central Chile, Related to Seismic Activity—Using the Persistent Scatterer Method and GIS

Interferometric synthetic aperture radar is an effective means of measuring changes in the altitude of the Earth’s surface. In this research, the areas of surface deformation associated with low- and medium-intensity seismic events in Central Chile were analyzed using SENTINEL 1 satellite radar interferograms and geographical information system (GIS) tools. The persistent scatterer method was used to reduce noise from conventional InSAR methods. The results revealed that the coastal zone of Central Chile has a high density of daily earthquakes with a prevalence (93.03%) of low- and medium-intensity earthquakes. Monthly deformation maps were developed for the coast of the Biobio region in Central Chile. A clear deformation pattern is defined along the coast, being greater in the Arauco, Lota and Lebu areas. It was also shown that there was a slight upward trend in the north and northeast zone (i.e., δup ~3 mm/year), while there was an obvious accentuated upward trend (i.e., δup ~24 mm/year) in the southern part. This movement increases as latitude increases. This pattern is related to the daily seismic activity, the product of the movement between plates, and the geological faults located in the area. The deformation and trend maps provide certainty in terms of where hotspots are located, e.g., the most hazardous areas in the study zone, which can be applied to urban planning and/or safety assessment.

On the Phase Nonclosure of Multilook SAR Interferogram Triplets

This work explores the properties characterizing the phase non-closure of multi-look synthetic aperture radar (SAR) interferograms. Specifically, we study the implications of multi-look phase time incongruences on the generation of ground displacement time-series through small baseline (SB) multi-temporal InSAR (Mt-InSAR) methods. Our research clarifies how these phase inconsistencies can propagate through a time-redundant network of SB interferograms and contribute, along with phase unwrapping (PhU) errors, to the quality of the generated ground displacement products. Moreover, we analyze the effects of short-lived phase bias signals that could happen in sequences of short baseline (SB) interferograms and propose a strategy for their mitigation. The developed methods have been tested using both simulated and real SAR data. The latter were collected by the Sentinel-1A/B (C-band) sensors over the study areas of Nevada state, U.S., and Sicily Island, Italy.

Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran

ABSTRACT InSAR processing is vastly used for land deformation monitoring from the space. Machine learning methods are known as strong tools for data modeling as well as predicting. In this study, we are going to model and predict the future behavior of land subsidence by InSAR processing and leveraging deep learning methods over the lands in the vicinity of Lake Urmia (located in the northwest of Iran). Accordingly, Sentinel-1 data over 56 months from November 2014 to June 2019 and small baseline subsets (SBAS) InSAR methods were utilized. Several regions with a high rate of subsidence were identified (maximum monthly subsidence of 13.3 mm). Furthermore, environmental factors affecting subsidence were considered. Therefore, parameters such as rainfall, groundwater, and lake area variations were measured using TRMM, GRACE, and MODIS satellite data, respectively. In order to determine and assess the relation between land deformations and environmental variations, several machine learning methods were implemented. The environmental parameters were used as the input of models and ground deformations as the target to be predicted. Eventually, ground deformations were estimated using multi-layer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) networks, in which each network had strengths and weaknesses on different occasions. Thus, by blending the forecast of the three models, a weighted ensemble was constructed, which outperformed the single models and reached the root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) of 8.2 mm, 6.4 mm, and ±5.2 mm, respectively. The result indicated that although each single model had proper accuracy, an ensemble model can improve land deformation anticipation using the strength of networks in various conditions.

A distributed scatterers InSAR method based on adaptive window with statistically homogeneous pixel selection for mining subsidence monitoring

Statistically homogeneous pixels (SHP) selection is one of the primary steps in time-series InSAR technologies based on distributed scatterers. However, using a fixed window to extract SHP in mining areas may fail to effectively protect and restore the deformation phase in subsequent phase optimization. Therefore, we propose a method based on iterative adaptive window to extract SHP. This approach uses the variation coefficient to judge the intensity of fringes to realize the adaptive window adjustment in and outside the subsidence areas based on different ranges. Moreover, we use eigenvalue decomposition to optimize the phase, and use small baseline subset (SBAS) interferometry to perform time-series modelling and deformation calculations for the selected high-coherence points. The reliability of this method is verified from comparisons with leveling data. Compared with traditional time-series InSAR methods, the proposed method significantly improves the monitoring point density, deformation accuracy and has broad prospects in mining subsidence monitoring.

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway.

A structure knowledge-synthetic aperture radar interferometry integration method for high-precision deformation monitoring and risk identification of sea-crossing bridges

Deformation monitoring and risk identification of sea-crossing bridges are essential to mitigate hazards and prevent loss of human life and property. Satellite-based Synthetic Aperture Radar Interferometry (InSAR) technology can detect millimeter-scale deformation, showing unique advantages in the safety monitoring of sea-crossing bridges. However, the existing InSAR methods only extract point-like targets (PTs) based on the coherent index, but ignores the analysis of multiple SAR incoherent information and the foreground-background scattering characteristics differences of bridges, leading to low-density and low-accuracy of PTs on sea-crossing bridges. Moreover, most InSAR-based studies identified structural risks according to deformation measurements without fully considering the various safe deformation ranges of different structural components, resulting in high false-alarm/miss-detection rates in structural risk identification of sea-crossing bridges. To address these issues, a structure knowledge-InSAR integration approach is developed for high-precision deformation monitoring and reliable risk identification of sea-crossing bridges. Firstly, the SAR incoherent information and foreground-background scattering characteristics of the bridge structure are analyzed and applied to improve the density of extractable PTs and remove the incorrect noise signals. Then, the bridge structural mechanics model is combined with the InSAR time-series displacements to analyze the mechanical property degradation of different bridge components, improving the reliability of InSAR-based structural risk identification. This approach is applied to the Stonecutters Bridge and Tsing Ma Bridge using the TerraSAR-X and COSMO-SkyMed images from 2011 to 2012 and the Sentinel-1A images from 2015 to 2017. The results indicate that the densities of PTs extracted on the two bridges increased by about 40% using the new approach, and incorrect noise signals are removed. Moreover, the mechanical properties of different bridge components can be evaluated through the analysis of their structural stress and time-series displacements, helping to decrease the false-alarm/miss-detection rates of InSAR-based structural risk identification. The bridge deformation is correlated with the temperature variation when the temperature difference is large (≥10 °C), but no longer dominated by thermal dilation when the temperature difference is less than 10 °C due to the influence of environmental effects.

Induced Seismic Events—Distribution of Ground Surface Displacements Based on InSAR Methods and Mogi and Yang Models

In this article, we present a possible approach to use satellite radar data for a complete description of the formation process of a subsidence trough resulting from an induced seismic event—a mining tremor. Our main goal was to verify whether SAR data allow for the calculation of the basic indicators for the trough (w—subsidence, T—trough slope, K—curvature, u—horizontal displacements, ε—horizontal deformations). We verified the extent to which the Mogi and Yang models can be fitted to match the actual displacements recorded after an induced seismic tremor. The calculations were performed for the Legnica-Glogow Copper Belt (LGCB) area in southwest Poland. Due to intensive mining operations and specific geological and tectonic conditions, the area shows a high level of induced seismic activity. Our detailed analysis focused on four powerful mining tremors: the first tremor occurred on 29 November 2016 (MW3.4), the second on 7 December 2017 (MW3.3), the next on 26 December 2017 (MW3.6) and the last tremor on 29 January 2019 (MW3.7). For each analyzed event, we determined the displacements based on the Differential Interferometric Synthetic Aperture Radar (DInSAR) method and Sentinel 1 synthetic aperture radar (SAR) data from two paths (22 and 73). Additionally, for the period from November 2014 to October 2020, we calculated the displacements using the Small Baseline Subset method (SBAS) time series method. In all cases, the tremor was followed by the development of long-lasting surface deformations. The obtained results allowed us to conclude that it is possible to calculate indicators that result from a specific induced mining event. Considering the full moment tensor and nature of the tremor source, we demonstrated that the Mogi and Yang models can be employed to describe the influence of an induced tremor on the surface in an area of mining activity. We also confirmed the global character of the influence of the reduced troposphere on SAR data calculations. Our conclusions indicate that accounting for the tropospheric correction does not distort horizontal and vertical displacement values in regions influenced by mining activity/tremors.

Benchmarking and inter-comparison of Sentinel-1 InSAR velocities and time series

Different InSAR algorithms and methods produce velocities and times series that are not identical, even using the same data for the same area. This inconsistency can cause confusion and be a barrier to uptake and widespread use of the data in the commercial sector. With the widespread availability of Sentinel-1 SAR data and a suite of new algorithms in the commercial and academic sectors, it is timely to develop a method for comparison of different results. In this study, we focus on developing and testing an independent and robust methodology for assessment of different InSAR processing results. Our proposed method is adapted from the Terrafirma Process Validation project; we compare geocoded line-of-sight velocities and time series, density and coverage, as well as some qualitative metrics. We use Sentinel-1 data from an area in Glasgow (UK) processed using 4 different approaches modified RapidSAR, SqueeSAR, GAMMA-IPTA and conventional StaMPS. The main areas of ground motion are detected using all approaches, with the average standard deviation of velocity differences for all inter-comparison pairs in all polygons equal to 1.1 mm/yr. Sentinel-1 InSAR therefore provides comparable results that are independent of processing approaches. However, there are considerable differences in some aspects of the results, in particular in their density and coverage. We discuss the reasons for these differences and suggest a framework for validation that could be used in future national or pan-national ground motion services.

Automated estimation of forest height and underlying topography over a Brazilian tropical forest with single-baseline single-polarization TanDEM-X SAR interferometry

Forest height is an important variable for modeling terrestrial carbon storage and global carbon cycle dynamics. Spaceborne SAR Interferometry (InSAR) has the sensitivity to measure canopy height and the underlying topography. In this paper, we refine and automate an interferometric ground finding approach that exploits few-look (2- to 4-look) averaged interferograms and incorporates the use of a coherent electromagnetic simulator and field inventory data. Using the coherent electromagnetic simulator, an InSAR simulation based on field data is performed to study the true ground position as a function of the statistics of few-look InSAR phase heights from a model perspective. With this statistical model, both the underlying topography and the canopy height (mean and top canopy height) can be estimated. Using German Aerospace Center's (DLR) single-baseline single-polarization TanDEM-X InSAR data, we validate the approach over a Brazilian tropical forest (Tapajós National Forest) with both field inventory and lidar data. As validated against lidar data, the underlying topography is estimated to an accuracy of 3 m. At one hectare aggregated pixel size, InSAR phase-center height is best compared with the field/lidar mean canopy height with an accuracy of 2–3 m, while InSAR-inverted total height best characterizes the lidar top canopy height with an accuracy of 4–5 m. Given the global data availability of TanDEM-X and the future TanDEM-L, this approach has the potential for wall-to-wall mapping of forest height as well as underlying topography and also serves as a complementary tool to other existing InSAR, Polarimetric InSAR (PolInSAR) and SAR tomography (TomoSAR) methods when only single polarization and/or baseline data are available.