Interferometric Synthetic Aperture Radar Datasets Research Articles

Preventing Subsidence Reoccurrence in Tianjin: New Preconsolidation Head and Safe Pumping Buffer.

Tianjin, a coastal metropolis in north China, has grappled with land subsidence for nearly a century. Yet, emerging evidence suggests a notable decrease in subsidence rates across Tianjin since 2019. This trend is primarily attributed to the importation of surface water from the Yangtze River system via the South-to-North Water Diversion Project, initiated in December 2014. Utilizing Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) data (2014-2023), this study reveals that one-third of the Tianjin plain has either halted subsidence or experienced land rebound. As a result, the deep aquifer system (~-200 to -450 m) beneath one third of the Tianjin plain has completed a consolidation cycle, leading to the establishment of new, locally specific preconsolidation heads. The identification of the newly established preconsolidation head seeks to answer a crucial question: How can we prevent the reoccurrence of subsidence in areas where it has already ceased? In essence, subsidence will stop when the local hydraulic head elevates to the new preconsolidation head (NPCH), and permanent subsidence will not be reinitiated as long as hydraulic head remains above the NPCH. The difference of the depth between current hydraulic head and the NPCH defines the safe pumping buffer (SPB). This study outlines detailed methods for identifying the NPCHs in the deep aquifer system from long-term InSAR and groundwater-level datasets. Determining NPCHs and ascertaining SPBs are crucial for estimating how much groundwater can be safely extracted without inducing permanent subsidence, and for developing sustainable strategies for long-term groundwater management and conservation.

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

Elucidating the magma plumbing system of Ol Doinyo Lengai (Natron Rift, Tanzania) Using satellite geodesy and numerical modeling

Ol Doinyo Lengai, located in the southern Eastern Branch of the East African Rift had several eruptive episodes with ash falls and lava flows (VEI 3) that caused damage to the nearby communities between 2007 and 2010. The volcano is remote and access is difficult. Although this volcano has been studied for decades, its plumbing system is still poorly understood, in part, because of the lack of precise observations of surface deformation during periods of quiet and unrest. This study investigates the volcanic plumbing system of Ol Doinyo Lengai and its surroundings using data from the network of permanent Global Navigation Satellite System (GNSS) sites monitoring the volcano (the TZVOLCANO network) around the flanks of the volcano and Interferometric Synthetic Aperture Radar (InSAR) observations. We constrain surface motions using 6 GNSS sites distributed around Ol Doinyo Lengai, operating between 2016 and 2021, and InSAR data covering nearly the same time period. Because of the complex local tectonics, the interpretation of the deformation pattern is not straightforward. We first invert the GNSS deformation and InSAR observations independently to infer potential deformation sources. Then we perform a joint inversion of both GNSS and InSAR datasets to verify our findings. We compare the results from the joint inversion with the results from inverting each dataset independently. The GNSS, InSAR, and joint inversion results point to a deflating source, located east of Ol Doinyo Lengai and southwest of the dormant volcano Gelai at a depth of 3.49 ± 0.03 km (GNSS inversion), 5.2 ± 1.2 km (InSAR inversion) and 3.49 ± 0.06 km (joint inversion) relative to the summit (vent) and with a volume change ∆V of −0.04 ± 0.05 × 106 m3 (GNSS inversion), −0.39 ± 0.29 × 106 m3 (InSAR inversion), and − 0.04 ± 0.01 × 106 m3 (joint inversion). Although this is non-unique modeling of geodetic datasets with small signals, the inversion results suggest that Ol Doinyo Lengai could be fed by an offset multi-reservoir system that includes a shallow magma reservoir (<5 km) east of Ol Doinyo Lengai, possibly connected to a deeper magma reservoir.

Groundwater Volume Loss in Mexico City Constrained by InSAR and GRACE Observations and Mechanical Models

AbstractGroundwater withdrawal can cause localized and rapid poroelastic subsidence, spatially broad elastic uplift of low amplitude, and changes in the gravity field. Constraining groundwater loss in Mexico City, we analyze data from the Gravity Recovery and Climate Experiment and its follow‐on mission (GRACE/FO) and Synthetic Aperture Radar (SAR) Sentinel‐1A/B images between 2014 and 2021. GRACE/FO observations yield a groundwater loss of 0.85–3.87 km3/yr for a region of ∼300 × 600 km surrounding Mexico City. Using the high‐resolution interferometric SAR data set, we measure &gt;35 cm/yr subsidence within the city and up to 2 cm/yr of uplift in nearby areas. Attributing the long‐term subsidence to poroelastic aquifer compaction and the long‐term uplift to elastic unloading, we apply respective models informed by local geology, yielding groundwater loss of 0.86–12.57 km3/yr. Our results suggest Mexico City aquifers have been depleting at faster rates since 2015, exacerbating the socioeconomic and health impacts of long‐term groundwater overdrafts.

Sentinel-1 InSAR and GPS-Integrated Long-Term and Seasonal Subsidence Monitoring in Houston, Texas, USA

For approximately 100 years, the Houston region has been adversely impacted by land subsidence associated with excessive groundwater withdrawals. The rapidly growing population in the Houston region means the ongoing subsidence must be vigilantly monitored. Interferometric synthetic aperture radar (InSAR) has become a powerful tool for remotely mapping land-surface deformation over time and space. However, the humid weather and the heavy vegetation have significantly degraded the performance of InSAR techniques in the Houston region. This study introduced an approach integrating GPS and Sentinel-1 InSAR datasets for mapping long-term (2015–2019) and short-term (inter-annual, seasonal) subsidence within the greater Houston region. The root-mean-square (RMS) of the detrended InSAR-displacement time series is able to achieve a subcentimeter level, and the uncertainty (95% confidence interval) of the InSAR-derived subsidence rates is able to achieve a couple of millimeters per year for 5-year or longer datasets. The InSAR mapping results suggest the occurrence of moderate ongoing subsidence (~1 cm/year) in nothwestern Austin County, northern Waller County, western Liberty County, and the city of Mont Belvieu in Champers County. Subsidence in these areas was not recognized in previous GPS-based investigations. The InSAR mapping results also suggest that previous GPS-based investigations overestimated the ongoing subsidence in southwestern Montgomery County, but underestimated the ongoing subsidence in the northeastern portion of the county. We also compared the InSAR- and GPS-derived seasonal ground movements (subsidence and heave). The amplitudes of the seasonal signals from both datasets are comparable, below 4 mm within non-subsiding areas and over 6 mm in subsiding (&gt;1 cm/year) areas. This study indicates that groundwater-level changes in the Evangeline aquifer are the primary reason for ongoing long-term and seasonal subsidence in the Houston region. The former is dominated by inelastic deformation, and the latter is dominated by elastic deformation. Both could cause infrastructure damage. This study demonstrated the potential of employing the GPS- and InSAR-integrated method (GInSAR) for near-real-time subsidence monitoring in the greater Houston region. The near-real-time monitoring would also provide timely information for understanding the dynamic of groundwater storage and improving both long-term and short-term groundwater resource management.

An Improved Source Model of the 2021 Mw 6.1 Yangbi Earthquake (Southwest China) Based on InSAR and BOI Datasets

The azimuth displacement derived by pixel offset tracking (POT) or multiple aperture InSAR (MAI) measurements is usually used to characterize the north-south coseismic deformation caused by large earthquakes (M &gt; 6.5), but its application in the source parameter inversion of moderate-magnitude earthquakes (~M 6.0) is rare due to the insensitive observation accuracy. Conventional line-of-sight (LOS) displacements derived by the Interferometric Synthetic Aperture Radar (InSAR) have limited ability to constrain the source parameters of the earthquake with near north-south striking. On 21 May 2021, an Mw 6.1 near north-south striking earthquake occurred in Yangbi County, Yunnan Province, China. In this study, we derive both the coseismic LOS displacement and the burst overlap interferometry (BOI) displacement from the Sentinel-1 data to constrain the source model of this event. We construct a single-segment fault geometry and estimate the coseismic slip distribution by inverting the derived LOS and BOI-derived azimuth displacements. Inversion results show that adding the BOI-derived azimuth displacements to source modeling can improve the resolution of the slip model by ~15% compared with using the LOS displacements only. The coseismic slip is mainly distributed 2 to 11 km deep, with a maximum slip of approximately 1.1 m. Coulomb stress calculation shows a maximum Coulomb stress increment of ~0.05 Mpa at the north-central sub-region of the Red River Fault. In addition, there is a small Coulomb stress increase at the Southern end of the Weixi-Weishan fault. The potential seismic risks on the Weixi-Weishan and Northwest section of the Red River faults should be continuously monitored.

BM3D Denoising for a Cluster-Analysis-Based Multibaseline InSAR Phase-Unwrapping Method

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, the cluster analysis (CA)-based MBPU method often suffers from the problem of low noise robustness. Therefore, the block-matching and 3D filtering (BM3D) algorithm, one of the most effective filtering methods for image denoising, is applied to improve the performance of the method. Five different filtering strategies for applying BM3D are proposed in the paper: interferogram filtering (IFF), intercept filtering (ICF), cluster number filtering (CNF), unwrapped phase filtering (UPF), and simultaneous filtering (STF). In particular, while keeping the general structure of BM3D, four different similarity measures are defined for interferograms, intercepts, clusters, and unwrapped phases to accommodate the special characteristics of different filtering objects. Experiments on synthesized and real InSAR datasets prove their feasibility and effectiveness, and the experiment results show that (1) the PU accuracy and robustness of the CA-based MBPU method can be greatly improved by adding BM3D denoising; (2) simultaneous filtering of interferograms, intercepts, cluster numbers, and unwrapped phases works best, but with the worst time complexity; (3) when filtering is performed for only one object of the CA-based MBPU method, the filtering effect of CNF and UPF is better than that of IFF and ICF; and (4), considering the three indicators of PUSR, NRSE, and time consumption, CNF and UPF should be the best choices.

Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin, California

Subsidence induced by groundwater depletion is a grave problem in many regions around the world, leading to a permanent loss of groundwater storage within an aquifer and even producing structural damage at the Earth’s surface. California’s Tulare Basin is no exception, experiencing about a meter of subsidence between 2015 and 2020. However, understanding the relationship between changes in groundwater volumes and ground deformation has proven difficult. We employ surface displacement measurements from Interferometric Synthetic Aperture Radar (InSAR) and gravimetric estimates of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellite pair to characterize the hydrological dynamics within the Tulare basin. The removal of the long-term aquifer compaction from the InSAR time series reveals coherent short-term variations that correlate with hydrological features. For example, in the winter of 2018–2019 uplift is observed at the confluence of several rivers and streams that drain into the southeastern edge of the basin. These observations, combined with estimates of mass changes obtained from the orbiting GRACE satellites, form the basis for imaging the monthly spatial variations in water volumes. This approach facilitates the quick and effective synthesis of InSAR and gravimetric datasets and will aid efforts to improve our understanding and management of groundwater resources around the world.

Joint inversion of InSAR, local seismic and teleseismic data sets for the rupture process of the 2020 Mw 6.3 Yutian earthquake and its implications

On 25 June 2020, a Mw 6.3 earthquake struck Yutian county. The earthquake nucleated on a normal fault in the northwestern Tibetan Plateau. Through jointly inverting local seismic and teleseismic waveform data as well as Interferometric Synthetic Aperture Radar (InSAR) Line-of-Sight (LOS) displacement data, the spatiotemporal rupture properties of this earthquake were resolved to investigate and better understand regional crustal extension mechanisms. Inversion results show that this event ruptured along one main asperity concentrated within a depth range of 4.1–13.3 km with a maximum slip of ~0.9 m. The rupture front propagated initially around the hypocenter and then unilaterally toward the north. The whole process lasted approximately 8 s and released a total scalar seismic moment of 3.2 × 1018 Nm (Mw 6.3). The energy-based average stress drop was estimated in the range of 1.9–3.0 MPa. The radiation efficiency was estimated to be 10.0–15.8%, which is relatively low, demonstrating that most of the accumulated strain energy dissipated during rupturing. We suggest that the earthquake was possibly related to the nearby Cenozoic volcano in consideration of the dissipated rupture of the source and the west-dipping causative fault located on the western edge of the volcano. Both the westward motion of the western Kunlun block and the eastward motion of the Kunlun-Qaidam block were responsible for the EW-trending crustal extension in the Yutian district. We supposed that the 2020 normal faulting earthquake was primarily attributed to the different velocities of the Kunlun-Qaidam block moving eastward. Additionally, according to an analysis of coseismic Coulomb stress interactions, the previous 2008 Mw 7.1, 2012 Mw 6.2, and 2014 Mw 6.9 Yutian earthquakes contribute small Coulomb stress disturbance on the hypocenter of the 2020 Mw 6.3 Yutian earthquake which suggest that previous Yutian earthquakes have a limited effect on triggering the 2020 event.

Surface Displacement and Source Model Separation of the Two Strongest Earthquakes During the 2019 Ridgecrest Sequence: Insights From InSAR, GPS, and Optical Data

AbstractSince the occurrence of the 2019 Mw 6.4 and Mw 7.1 Ridgecrest earthquake sequence in the Eastern California Shear Zone, coseismic deformation following the two earthquakes has been intensively studied, and source modeling with interferometric synthetic aperture radar (InSAR), Global Positioning System (GPS) and seismological datasets has been favored. However, we recently found that the coseismic modeling of the two earthquakes constrained by the dense near‐field Planet‐Lab optical measurements can be more detailed than the previously estimated, which requires an accurate Planet‐Lab displacement. In this study, we improve the long wavelength orbital ramps correction method for obtaining a more accurate Planet‐Lab horizontal displacement field. The corrected dense near‐field Planet‐Lab data are firstly used together with the intermediate‐field GPS data to invert and distinguish the fault slip distribution for the two earthquakes. The same scheme is used to simultaneously invert the InSAR, SAR, GPS, and optical datasets, to improve the constraints on seismic source parameters. Our inversion results show that the joint‐event slip model is rougher than the two single‐event slip models, but it has a more concentrated slip pattern and larger slip amplitude in some zones. We show that adding a near‐field constraint of the Planet‐Lab data in the combined‐data inversion can reduce the slip parameter uncertainty and enhance the model resolution. The Coulomb failure stress changes on the southeastern Blackwater, the southern Owens Valley, and the central Panamint Valley faults are enhanced by about 0.4–0.8 bar by the 2019 Ridgecrest earthquake sequence.

Two-dimensional phase unwrapping method using a refined D-LinkNet-based unscented Kalman filter

Two-dimensional phase unwrapping (2-D PU) has a strong influence on the accuracy of interferometric synthetic aperture radar (InSAR) data processing results. Phase gradient estimation (PGE) is one of the key steps in the processing of 2-D PU. Moreover, the accuracy of the PGE will directly affect the accuracy of the final PU result. The phase continuity assumption is an important prerequisite for the PGE of traditional 2-D PU methods. However, the accuracy of PGE is not ideal in areas with high-noise and large-gradient changes. To address this issue, in this article, we propose a 2-D PU method of an unscented Kalman filter (UKF) using a refined LinkNet with a pretrained encoder and dilated convolution (D-LinkNet). To the best of our knowledge, this article is the first time to combine deep-learning and UKF for 2-D PU. First of all, this article analyzes the distribution characteristics of different terrains. To ensure the accuracy of the training model, we use shuttle radar topography missions (SRTMs) with different terrains to obtain simulated learning training data. Then, the refined d-LinkNet method is used to accurately estimate the gradient ambiguity numbers and is combined with the small window median filter to obtain the vertical and horizontal gradients. Finally, the UKF model is used for 2-D PU. Experiments are conducted with simulated and TanDEM-X InSAR datasets. In addition, compared with the existing PGE methods and 2-D PU methods, the experimental results show that the proposed method can obtain more accurate results than the existing methods.

InSAR Phase Unwrapping Error Correction for Rapid Repeat Measurements of Water Level Change in Wetlands

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> field observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

ALADDIn: Autoencoder-LSTM-Based Anomaly Detector of Deformation in InSAR

In this study, we address the challenging problem of automatic detection of transient deformation of the Earth’s crust in time series of differential satellite radar [interferometric synthetic aperture radar (InSAR)] images. The detection of these events is important for a wide range of natural hazard and solid earth applications, and InSAR is an ideal data source for this purpose due to its frequent and global observational coverage. However, the size of this dataset precludes a systematic manual analysis, and a low signal-to-noise ratio makes this task difficult. We present a novel method to address this problem. This approach requires the development of a novel network architecture to take advantage of the unique structure of the InSAR dataset. Our unsupervised deep learning model learns the “normal” unlabeled spatiotemporal patterns of background noise signals in 3-D InSAR datasets and learns the relationship between the input difference images and the underlying unknown set of individual 2-D fields of noise from which the InSAR images are constructed. The detection head of our pipeline consists of two complementary methods, semivariogram analysis and density-based clustering. To evaluate, we test and compare three increasingly complex network architectures: compact, deep, and bi-deep. The analysis demonstrates that the bi-deep architecture is the most accurate, and so it is used in the final detection pipeline [autoencoder long short-term memory-based anomaly detector of deformation in InSAR (ALADDIn)]. The analysis of experimental results is based on the detection of a synthetic deformation test case, achieving a 91.25% overall performance accuracy. Furthermore, we show that the ALADDIn can detect a real earthquake of magnitude 5.7 that occurred in 2019 in southwest Turkey.

Learning From Synthetic InSAR With Vision Transformers: The Case of Volcanic Unrest Detection

The detection of early signs of volcanic unrest preceding an eruption, in the form of ground deformation in Interferometric Synthetic Aperture Radar (InSAR) data is critical for assessing volcanic hazard. In this work we treat this as a binary classification problem of InSAR images, and propose a novel deep learning methodology that exploits a rich source of synthetically generated interferograms to train quality classifiers that perform equally well in real interferograms. The imbalanced nature of the problem, with orders of magnitude fewer positive samples, coupled with the lack of a curated database with labeled InSAR data, sets a challenging task for conventional deep learning architectures. We propose a new framework for domain adaptation, in which we learn class prototypes from synthetic data with vision transformers. We report detection accuracy that amounts to the highest reported accuracy on a large test set for volcanic unrest detection. Moreover, we built upon this knowledge by learning a new, non-linear, projection between the learnt representations and prototype space, using pseudo labels produced by our model from an unlabeled real InSAR dataset. This leads to the new state of the art with 97.1% accuracy on our test set. We demonstrate the robustness of our approach by training a simple ResNet-18 Convolutional Neural Network on the unlabeled real InSAR dataset with pseudo-labels generated from our top transformer-prototype model. Our methodology provides a significant improvement in performance without the need of manually labeling any sample, opening the road for further exploitation of synthetic InSAR data in various remote sensing applications.

Adapting InSAR Phase Linking for Seasonally Snow-Covered Terrain

Interferometric synthetic aperture radar (InSAR) time series analysis of natural terrain allows for characterization of long-term geophysical trends over extended areas and, in the case of distributed scatterers (DSs), is significantly enhanced by methods that exploit the full complex-valued scattering statistics. Phase-linking (PL) estimators impose a phase-closure constraint in order to estimate the temporal wrapped-phase history of a DS directly from its complex backscatter sample coherence matrix. Some PL methods, such as the SqueeSAR and maximum-likelihood-estimator of Interferometric phase (EMI) estimators, rely on knowledge of the coherence magnitude matrix. The true coherence magnitude is <i>a priori</i> unknown and must therefore be estimated from the data. Bias in these estimated coherence magnitudes reduces PL performance when the true coherence magnitude is low. Many areas of the Earth are seasonally snow-covered and, for natural terrain, this leads to severe cross-season decorrelation. This poses a significant challenge for PL estimators due to bias of the near-zero cross-season coherence magnitude estimates. We introduce a clustering approach to mitigate the PL estimator bias problem that exploits the fact that in natural terrain, many DSs decorrelate similarly. This allows for averaging over large numbers of same-behaving DS, which provides robust debiasing of the coherence magnitudes used during PL. We apply our method to a RADARSAT-2 spotlight-mode InSAR dataset over a site in the western Canadian Arctic and demonstrate significant reductions in <i>a posteriori</i> phase variance when compared to existing PL methods.

Kinematic Slip Model of the 2021 M 6.0 Antelope Valley, California, Earthquake

Abstract We present a kinematic slip model of the 8 July 2021 Antelope Valley earthquake from a finite-source inversion based on regional seismic waveforms and static offsets from Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). Seismic waveforms are employed at 6 s dominant period out to 100 km from the epicenter, and the combined GPS and InSAR datasets cover the near field and far field out to ∼100 km and constrain the overall rupture size. The aftershock pattern defines a nearly north-striking, 50° east-dipping fault plane. We find a unilateral rupture along this fault plane propagating southward and updip with predominantly normal slip up to ∼1.5 m. The estimated seismic moment of 8.47×1017 N·m is equivalent to Mw 5.92. A finite-source inversion that retains seismic waveforms and GPS static offsets but omits InSAR range changes yields a seismic moment of 1.08×1018 N·m (Mw 5.99). Despite vigorous aftershock activity between 10 km and Earth’s surface, coseismic slip is concentrated in the depth interval 7–10 km.

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Extensive Investigation of the Land Subsidence Impressions on Gedebage District, Bandung, Indonesia

Gedebage district is presently experiencing rapid and mass infrastructure development and becoming one of the developed districts in Bandung, Indonesia. A football stadium, several luxury housing, the grand mosque of West Java province, and a business center have been built in this district. However, it is well known that the Gedebage district has turned into one of the Bandung districts that suffers from land subsidence phenomena. Since 2000, the Gedebage district has suffered land subsidence at an average rate of 10 cm per year and becoming one of the fastest sinking districts in Bandung. This fast land subsidence phenomenon is suspected of affecting the infrastructure in this district. Therefore, this work aims to capture the current subsidence rate in the Gedebage district using the geodetic approach of the combination of the Global Navigation Satellite System (GNSS) with Interferometric Synthetic Aperture Radar (InSAR) and investigate the impact of land subsidence on infrastructures in Gedebage district. We use GNSS campaign datasets from the years 2016 and 2019. Each GNSS campaign was performed at least 10-12 hours of observations. We also utilize a similar period of 2016 to 2019 for the InSAR datasets. Utilizing both GNSS and InSAR datasets, we can capture the subsidence with the rate reaching 4 -15 cm per year between 2016 and 2019, and it occurs uniformly in this district. The impact of land subsidence occurred in almost all urban areas in the Gedebage district. These impacts include cracks in buildings, bridges and roads, and also tilted buildings.

Improving the Robustness of the MTI-Estimated Mining-Induced 3D Time-Series Displacements with a Logistic Model

The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous MTI method, no redundant observations are provided to estimate 3D displacements, causing poor robustness and further degrading the accuracy of the 3D displacement estimation. This study presents an improved MTI method to significantly improve the robustness of the 3D mining displacements derived by the previous MTI method. In this new method, a fused-track strategy, instead of the previous track-by-track one, is proposed to process the multi-track InSAR measurements by introducing a logistic model. In doing so, redundant observations are generated and further incorporated into the prior model to solve 3D displacements. The improved MTI method was tested on the Datong coal mining area, China, with Sentinel-1 InSAR datasets from three tracks. The results show that the 3D mining displacements estimated by the improved MTI method had the same spatial–temporal resolution as those estimated by the previous MTI method and about 33.5% better accuracy. The more accurate 3D displacements retrieved from the improved MTI method can offer better data for scientifically understanding the mechanism of mining deformation and assessing mining-related geohazards.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross‐comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.