Multi-temporal InSAR Research Articles

InSAR as a tool for monitoring hydropower projects: A review

This paper provides a review of using Interferometric Synthetic Aperture Radar (InSAR), a microwave remote sensing technique, for deformation monitoring of hydroelectric power projects, a critical infrastructure that requires consistent and reliable monitoring. Almost all major dams around the world were built for the generation of hydropower. InSAR can enhance dam safety by providing timely settlement measurements at high spatial-resolution. This paper provides a holistic view of different InSAR deformation monitoring techniques such as Differential Synthetic Aperture Radar Interferometry (DInSAR), Ground-Based Synthetic Aperture Radar (GBInSAR), Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR), Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPSInSAR) and Small BAseline Subset (SBAS). PSInSAR, GBInSAR, MTInSAR, and DInSAR techniques were quite commonly used for deformation studies. These studies demonstrate the advantage of InSAR-based techniques over other conventional methods, which are laborious, costly, and sometimes unachievable. InSAR technology is also favoured for its capability to provide monitoring data at all times of day or night, in all-weather conditions, and particularly for wide areas with mm-scale precision. However, the method also has some disadvantages, such as the maximum deformation rate that can be monitored, and the location for monitoring cannot be dictated. Through this review, we aim to popularize InSAR technology to monitor the deformation of dams, which can also be used as an early warning method to prevent any unprecedented catastrophe. This study also discusses some case studies from southern India to demonstrate the capabilities of InSAR to indirectly monitor dam health.

Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications

Monitoring large areas of the Earth&#x0027;s surface is possible thanks to the availability of remote sensing data obtained by a large collection of diverse satellites orbiting the Earth. Specifically, multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques supply the structural community with time series of line-of-sight displacements of terrain or structures, such as bridges or buildings, resulting from causes such as thermal expansion, contraction, and terrain deformation. The analysis of the different deformation signals observed is crucial in order to identify the different phenomena that cause the deformation. In this article, we explore the possibility of applying blind source separation algorithms to MT-InSAR, with the aim of developing methods towards automatic identification of different deformation patterns both in buildings and structures. We validate the proposed methodology using both synthetically generated datasets and real MT-InSAR data. We also provide a comparison with other similar methods. Our results demonstrate that InSAR time-series analysis can benefit from the use of the proposed blind source separation approach. Furthermore, the proposed technique is robust to the noise which is usually present in MT-InSAR data. This opens the door for monitoring infrastructure at scale over very large areas, helping to monitor civil infrastructures, and providing relevant insights to asset owners regarding the performance of such structures over time.

Isolating Orbital Error From Multitemporal InSAR Derived Tectonic Deformation Based on Wavelet and Independent Component Analysis

Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce the reliability of deformation monitoring. In this letter, we propose a method to isolate the orbital error from the multitemporal InSAR (MTInSAR) derived tectonic deformation based on the wavelet multiresolution analysis and independent component analysis (ICA). Starting from the sequential interferometric phase of unwrapping, the tectonic deformation and orbital error are firstly extracted from the interferometric phase by wavelet analysis based on their longwavelength spatial patterns, and ICA is then used to isolate the orbital error from the tectonic deformation according to the different temporal characteristics of the two types of signals. In the simulation experiment, the root-mean-square error (RMSE) of the isolated orbital error is 2.6 mm. Experiments with real data in Southern California show that the proposed method can successfully separate the orbital error from the tectonic deformation, and the InSAR deformation rates are in good agreement with the GPS observations.

Measuring Subsidence Over Soft Clay Highways Using a Novel Time-Series InSAR Deformation Model With an Emphasis on Rheological Properties and Environmental Factors (NREM)

Long-term monitoring of highways in soft soil areas, especially during the postconstruction period, is of great significance to ensure transportation safety and the quality of highway construction. Multitemporal interferometric synthetic aperture radar (MTInSAR) provides an effective tool for soft clay highway monitoring. However, most time-series models used in MTInSAR modeling are empirical mathematical functions, which ignores the physical properties of the observed objects and may limit the accuracy of the retrieved deformation and the understanding of the underground settlement dynamics. We propose a novel InSAR time-series deformation model (namely, NREM) with an emphasis on the rheological mechanisms for soft soil highways and environmental factors (temperature, humidity, and precipitation) to improve the accuracy of the traditional InSAR model and assist in the analyzing the rheological properties of soft soil. The NREM is constructed based on a combination of the seasonal model and the Burgers model introduced from the field of rheology. The primary parameters (i.e., viscosity and elastic modulus) are introduced in the NREM and estimated with the generation of time-series surface deformation. In the real data experiments, two highways are selected as the test areas. The results show that the standard deviations (STDs) of the high-pass deformation, which can reflect the modeling accuracy, derived by the NREM are lower than those of the three traditional models, yielding an improvement of 45&#x0025; for the Lungui Highway (LH) and 50&#x0025; for the G1508 Highway (GH). The root mean square errors (RMSEs) for deformation results derived from NREM are estimated to be &#x00B1;5.1 mm compared with the leveling measurements, which outperforms the traditional models. The obtained rheological parameters can broaden the application of InSAR technology and provide a reference for highway engineering.

Allometric Vegetation Modeling and SAR Image Simulation for Polarimetry and Interferometry

This paper establishes a low degrees-of-freedom allometric vegetation model based on the Biomass and allometry database (BAAD). It consists of a 5-bit species encoding scheme and a 5-parameter relationship derived from the BAAD data records. Combined with opensource fractal tree generation engine, it can produce realistic tree samples of a large variety of species. A coherent electromagnetic scattering calculation method is developed for the vegetation model where the generalized Rayleigh-Gans (GRG) approximation and the infinite cylinder approximation are used to calculate the scattering matrix of leaves and branches/trunk. Four-path multiple scattering mechanisms between vegetation and the ground are considered and attenuations through vegetation canopy are also considered. The scattering model is validated against numerical methods. In addition, an end-to-end simulation tool is developed. Optical image is used to extract individual trees with center positions and crown diameters. The rest parameters are generated using the derived allometry model. Virtual 3D scene with vegetation on digital elevation map (DEM) can be generated and SAR images can be simulated. Several case studies are carried out for both polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) and multi-temporal interferometric SAR (InSAR). One case of the Mount Fuji area is simulated and validated against ALOS-2 data and demonstrates an average scattering coefficient error of less than 3dB. Additional cases of Southwest China, Hainan Island and the Great Khingan Mountain demonstrate the feasibility of the proposed simulation scheme for various application scenarios.

Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

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.

Mapping Floods in Urban Areas From Dual-Polarization InSAR Coherence Data

Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor’s line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.

Atmospheric Phase Screen Compensation on Wrapped Ground-Based SAR Interferograms

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

A Multigrid InSAR Technique for Joint Analyses at Single-Look and Multi-Look Scales

This work proposes a multigrid differential synthetic aperture radar (SAR) interferometry (InSAR) technique for the detection of ground displacements at different spatial scales. The method relies on efficient phase-unwrapping (PhU) operations performed at the native spatial scales. In particular, a set of multi-look interferograms are first unwrapped using conventional (or advanced) PhU algorithms at the regional scale. Subsequently, ML unwrapped interferograms are used to facilitate the PhU operations performed at the local scale (single-look). Specifically, the unwrapped multi-look interferograms are resampled to the single-look grid and modulo- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi $ </tex-math></inline-formula> subtracted to the single-look interferograms. These phase residuals are then unwrapped and added back to the multi-look resampled interferograms. To accomplish these operations, at variance with alternative multiscale methods, no (linear/nonlinear) models are used to fit the spatial high-pass phase residuals. Finally, the unwrapped single-look interferograms are properly inverted to retrieve the ground displacement time series using any small baseline (SB)-oriented multitemporal InSAR tool. Experimental results are performed by processing a set of SAR data acquired by the X-band COSMO-SkyMed sensor over the coastal area of Shanghai, China.

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.

Kotrupi landslide deformation study in non-urban area using DInSAR and MTInSAR techniques on Sentinel-1 SAR data

The DInSAR (Differential Interferometric SAR) and MTInSAR (Multi-Temporal Interferometric SAR) techniques are used to examine a surface displacement in the Kotrupi Region in this paper. DInSAR and MTInSAR are capable of determining the surface deformation of a rapidly moving landslide. The Vertical Line of Sight (LOS) displacements were estimated with millimeter accuracy using a DInSAR technique followed by phase unwrapping. MTInSAR approaches using APS (Atmospheric Phase Screen) and sparse point processing, on the other hand, measure earth surface displacements and track their trends over time. DInSAR is limited by several difficulties, including phase decorrelation errors, phase unwrapping errors, and atmospheric phase disturbances, all of which reduce the quality of deformation effects. MTInSAR approaches, on the other hand, use data from several different periods within the same temporal baseline. As a result, the effect atmospheric phase disturbances and unwrapping error is greatly decreased when compared to classic DInSAR approaches. After the Kotrupi landslide of August 13, 2017, conventional DInSAR with a single pair of SLC (Single Look Complex) Sentinel-1 SAR images showed a maximum displacement value of about −60 mm to 60 mm in the Kotrupi landslide runoff region. While the advanced MTInSAR results indicate a maximum rate of velocity of about −45 mm/year for the continuous deformation happening in the Kotrupi landslide area. The coherence values along Pathankot National Highway (NH) −154 before and after the landslide were 0.857 and 0.019, respectively. Deformation studies before and after the landslide occurrence are also conducted using a DEM (Digital Elevation Model). Based on the DEM obtained, erosional and depositional deformations of −3.5 m and +3.0 m have been recorded in the Kotrupi landslide runoff area, resulting in 6–7 m change and material loss. The Root Mean Square Error (RMSE) for MTInSAR DEM 0.787 m and DInSAR DEM 1.026 m was estimated after the DEMs produced from DInSAR and MTInSAR processing were compared with TanDEM-X. MTInSAR produces much better results than DInSAR in non-urban areas.

A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives

Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

Interferometric coherence and seasonal deformation characteristics analysis of saline soil based on Sentinel-1A time series imagery

Affected by the natural environmental and human activity factors, significant seasonal differences appear on the regional scattering characteristic and ground deformation of saline soil. Interferometric decorrelation due to season replacement limits the conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and its application in such areas. To extend the monitoring capability in the salt desert area, we select a vast basin of saline soil around Howz-e-Soltan Salt Lake of Iran as the study area and present an improved MT-InSAR for experimental research. Based on 131 C-band Sentinel-1A images collected between October 2014 to July 2020,1896 refined interferograms in total are selected from all interfero-gram candidates. Interferometric coherence analysis shows that the coherence in the saline soil area has an apparent seasonal variation, and the soil moisture affected by the precipitation may be the main factor that leads to the seasonal variation. Subsequently, the deformation characteristics of saline soil under different environmental conditions and human activity factors are compared and analyzed in detail. Related deformation mechanisms of different saline soil types are initially revealed by combining interferometric coherence, meteorological data, and engineering geological characteristics of saline soil. Related results would provide reference for the large-scale infrastructure construction engineering in similar saline soil areas.

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.

Modified statistically homogeneous pixel selection for use with a non-local adaptive multilook approach for small SAR datasets

ABSTRACT Multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is an important approach for surface deformation monitoring. Nonlocal adaptive multilooking approach has potential benefits for the filtering and coherence estimation in the data preprocessing steps of MT-InSAR technique. The kernel of nonlocal adaptive multilooking approach lies in the selection of statistically homogeneous pixels (SHPs). Various amplitude-based strategies, such as DespecKS and its variations, have been proposed for selecting SHPs. However, the detection rates of these methods are usually unsatisfactory in the case of small data sets. To overcome this limitation, SHPs are selected based on the adaptive joint data vector, which encompasses both time dimensional samples and spatial information. In this letter, the outliers of the time dimensional samples are removed first based on a revised boxplot method. Additionally, image segmentation and L1 norm are both introduced to adaptively construct joint data vector in the preset window. Experiments on five TSX images are used to verify the reliability and efficiency of the proposed algorithm.

Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.

Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau

Permafrost on the Qinghai-Tibet Plateau (QTP) undergoes significant thawing and degradation, which affects the hydrological processes, ecosystems and infrastructure stability. The ground deformation, a key indicator of permafrost degradation, can be quantified via geodetic observations, especially using multi-temporal InSAR techniques. The previous InSAR studies, however, either rely on data-driven models or Stefan-equation-based models, which are both lacking of consideration of the spatial-temporal variations of freeze-thaw processes. Furthermore, the magnitudes and patterns of the permafrost-related ground deformation over large scales (e.g., 1×105km2 or larger) is still insufficiently quantified or poorly understood. In this study, to account for the spatial heterogeneity of freeze-thaw processes, we develop a permafrost-tailored InSAR approach by incorporating a MODIS-land-surface-temperature-integrated ground deformation model to reconstruct the seasonal and long-term deformation. Utilizing the approach to Sentinel-1 SAR images on the vast regions of about 140,000km2 of the central QTP during 2014–2019, we observe widespread seasonal deformation up to about 80mm with a mean value of about 10mm and linear subsidence up to 20mm/year. We apply the geographical detector to determine the controlling factors on the permafrost-related deformation. We find that the slope angle is the primary controller on the seasonal deformation: strong magnitudes and variations of seasonal deformation are most pronounced in flat or gentle-slope regions. The aspect angle, vegetation and soil bulk density exhibit a certain correlation with seasonal deformation as well. Meanwhile, we find that a linear subsidence is higher in the regions with high ground ice content and warm permafrost. It indicates that warm and ice-rich permafrost regions are more vulnerable to extensive long-term subsidence. We also observe that the cold permafrost regions experience lower linear subsidence even with high ground ice content, which indicate ice loss is limited. Thus, we infer that under continuously warming, the transition from cold permafrost to warm permafrost may lead to more extensive ground ice melting. Moreover, the strong subsidence/uplift signals surrounding some lakes suggesting that the change of local hydrological conditions may induce localized permafrost degradation/aggradation. Our study demonstrates the capability of the permafrost-tailored InSAR approach to quantify the permafrost freeze-thaw dynamics as well as their spatial-temporal patterns over large scales in vast permafrost areas.