Multi-temporal InSAR Research Articles

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

The complexity of the atmosphere renders the modelling of the atmospheric delay in multi temporal InSAR difficult. This limits the potential of achieving millimetre accuracy of InSAR-derived deformation measurements. In this paper we review advances in tropospheric delay modelling in InSAR, tropospheric correction methods and integration of the correction methods within existing multi temporal algorithms. Furthermore, we investigate ingestion of the correction techniques by different InSAR applications, accuracy performance metrics and uncertainties of InSAR derived measurements attributed to tropospheric delay. Spatiotemporal modelling of atmospheric delay has evolved and can now be regarded as a spatially correlated turbulent delay with varying degree of anisotropy random in time and topographically correlated seasonal stratified delay. Tropospheric corrections methods performance is restricted to a case by case basis and ingestion of these methods by different applications remains limited due to lack of their integration into existing algorithms. Accuracy and uncertainty assessments remain challenging with most studies adopting simple statistical metrics. While advances have been made in tropospheric modelling, challenges remain for the calibration of atmospheric delay due to lack of data or limited resolution and fusion of multiple techniques for optimal performance.

Joint exploitation of space-borne and ground-based multitemporal InSAR measurements for volcano monitoring: The Stromboli volcano case study

We present a joint exploitation of space-borne and ground-based Synthetic Aperture Radar Interferometry (InSAR) and Multi Temporal (MT) InSAR measurements for investigating the Stromboli volcano (Italy) deformation phenomena. In particular, we focus our analysis on three periods: a) the time interval following the 2014 flank eruption, b) the July–August 2019 eruption and c) the following post-eruptive phase. To do this, we take advantage from an unprecedented set of space-borne and ground-based SAR data collected from April 2015 up to November 2019 along two (one ascending and one descending) Sentinel-1 (S-1) tracks, as well as, in the same period, by two ground-based systems installed along the Sciara del Fuoco northern rim. Such data availability permitted us to first characterize the volcano long-term 3D deformation behavior of the pre-eruptive period (April 2015–June 2019), by jointly inverting the space-borne and ground-based InSAR measurements. Then, the GB-SAR measurements allowed us to investigate the sin-eruptive time span (3rd July 2019 – 30th August 2019) which revealed rapid deformation episodes (e.g. more than 30 mm/h just 2 min before the 3rd July 2019 explosion) associated with the eruptive activity, that cannot be detected with the weekly S-1 temporal sampling. Finally, the S-1 measurements permitted to better constrain the post 2019 eruption deformations (31st August 2019 – 5th November 2019), which are mainly located outside the GB-SAR sensed area. The presented results demonstrate the effectiveness of the joint exploitation of the InSAR measurements obtained through satellite and terrestrial SAR systems, highlighting their strong complementarity to map and interpret the deformation phenomena affecting volcanic areas.

Detecting peat extraction related activity with multi-temporal Sentinel-1 InSAR coherence time series

Monitoring of when, where and in which quantity peat is harvested is currently based on manual declarations. Synthetic Aperture Radar (SAR) is a powerful tool for change detection and monitoring. The aim of this study was to evaluate whether Sentinel-1 6-day interferometric SAR (InSAR) temporal coherence could allow peat extraction monitoring from satellite. We demonstrate that temporal median coherence enables to detect harvest related surface altering works and therefore also spatially explicitly determine active and inactive extraction areas. A polygon-based multi-orbit time series approach is sufficient for the task. Hereby, vertical–vertical polarisation (VV) is more sensitive to the changes compared to vertical-horizontal (VH). During the main harvest season the peat extraction area has median VV coherence lower than 0.2 while the abandoned area and open bog which serve as reference for undisturbed extraction area have close to 0.6. Also, the potential for coherence based milled peat extraction intensity estimation is demonstrated and an indication is given how partially extracted areas could be distinguished from fully harvested and not harvest areas, by the use of coherence standard deviation. Regarding the influence of rainfall, only heavy rain on one of the acquisitions of the image pair whereas the other is from dry conditions seems to cause decorrelation comparable to surface altering works. Moreover, deploying images from multiple consecutive orbits or introducing backscatter intensity σ0 or reference polygons of undisturbed area helps to reduce risk for rain induced false positives. Developing an operational algorithm for peat extraction identification could be undertaken in future studies.

Integrated use of archival aerial photogrammetry, GNSS, and InSAR data for the monitoring of the Patigno landslide (Northern Apennines, Italy)

The morphological changes of unstable areas can be identified using different methodologies that allow repeated surveys over time. The integration between the data obtained from different remote sensing and ground-based techniques, characterized by different coverage, resolution, and precision, allows to describe the kinematic motion of landslides with high accuracy and details. The aim of this work is to monitor the displacements of the Patigno landslide, a deep-seated gravitational slope deformation located in the Northern Apennines (Zeri, Massa Carrara, Italy), using archival aerial photogrammetry (1975–2010), continuous GNSS observations (2004–2018), and multi-temporal InSAR data (2015–2019). The results obtained adopting the different techniques were cross-validated and integrated in order to better explain the kinematics of the landslides: the GNSS data analysis shows horizontal movements of about 43 mm/yr in the S-E direction and vertical deformations of 6.5 mm/yr, in agreement with the average displacement rates obtained from photogrammetry and InSAR processing. The analysis of multi-temporal aerial photogrammetric images allowed us to observe three sectors of the landslide body characterized by different velocities rates and planimetric directions, in agreement with the LOS InSAR displacement field. Furthermore, the correlation between the rainfall distribution and the GNSS time series shows an acceleration of the sliding movements after about 3–4 months of a strong rainfall period. This integrated approach allowed us to overcome the limitations of each technique and to provide a 44-year long monitoring of the Patigno landslide. We also show that a synergic use of ground-based and remote sensing methodologies can provide useful information for the planning of more effective landslide risk mitigation strategies.

Multi-Temporal Small Baseline Interferometric SAR Algorithms: Error Budget and Theoretical Performance

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques are well recognized as useful tools for detecting and monitoring Earth’s surface temporal changes. In this work, the fundamentals of error noise propagation and perturbation theories are applied to derive the ground displacement products’ theoretical error bounds of the small baseline (SB) differential interferometric synthetic aperture radar algorithms. A general formulation of the least-squares (LS) optimization problem, representing the SB methods implementation’s core, was adopted in this research study. A particular emphasis was placed on the effects of time-uncorrelated phase unwrapping mistakes and time-inconsistent phase disturbances in sets of SB interferograms, leading to artefacts in the attainable InSAR products. Moreover, this study created the theoretical basis for further developments aimed at quantifying the error budget of the time-uncorrelated phase unwrapping mistakes and studying time-inconsistent phase artefacts for the generation of InSAR data products. Some experiments, performed by considering a sequence of synthetic aperture radar (SAR) images collected by the ASAR sensor onboard the ENVISAT satellite, supported the developed theoretical framework.

Ground deformation of Dongsheng mining area revealed by multi-temporal InSAR

Coal is one of the most important mineral resources in the industrial development of China. In order to minimize the losses directly or indirectly caused by the ground subsidence, it is necessary to accurately retrieve the information of the ground subsidence distribution during the mining process and make the accurate impact assessment of changes in the ecological environment. In this paper, we selected Sentinel-1A data of 22 scenes from July 2018 to March 2019, which covers Dongsheng coalfield of Ordos, Inner Mongolia. Then, PS-InSAR is used to obtain the ground deformation information. The results show that the PSs with high deformation rate and large accumulation are mainly located in the centre of the focused area, where the velocity along line of sight (LOS) mainly ranges from - 17 mm/year to - 5 mm/year with the extreme value reaching - 33.5 mm/year. Meanwhile the deformation rate of the majority of the PSs ranges from - 5 mm/year to 5 mm/year. It is highly recommended to reduce the ground deformation and mitigate the impact of land subsidence on the ecological environment caused by mining through programming logical mining schemes.

Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements

Mass movements represent a serious threat to the stability of human structures and infrastructures, and cause loss of lives and severe damages to human properties every year worldwide. Built structures located on potentially unstable slopes are susceptible to deformations due to the displacement of the ground that at worst can lead to total destruction. Synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites and processed by multi-temporal interferometric SAR (MT-InSAR) techniques can measure centimeter to millimeter-level displacement with weekly to monthly updates, characterizing long-term large-scale behavior of the buildings and slopes. However, the spatial resolution and short wavelength weaken the performance of Sentinel-1 in recognizing features (i.e., single buildings) inside image pixels and maintaining the coherence in mountainous vegetated areas. We have proposed and applied a methodology that combines Sentinel-1 interferometry with ground-based geomatics techniques, i.e., global navigation satellite system (GNSS), terrestrial laser scanning (TLS) and terrestrial structure from motion photogrammetry (SfM), for fully assessing building deformations on a slope located in the north-eastern Italian pre-Alps. GNSS allows verifying the ground deformation estimated by MT-InSAR and provides a reference system for the TLS and SfM measurements, while TLS and SfM allow the behavior of buildings located in the investigated slope to be monitored in great detail. The obtained results show that damaged buildings are located in the most unstable sectors of the slope, but there is no direct relationship between the rate of ground deformation of these sectors and the temporal evolution of damages to a single building, indicating that mass movements cause the displacement of blocks of buildings and each of them reacts differently according to its structural properties. This work shows the capability of MT-InSAR, GNSS, TLS and SfM in monitoring both buildings and geological processes that affect their stability, which plays a key role in geohazard analysis and assessment.

Estimation of Coseismic Deformation With Multitemporal Radar Interferometry

Differential interferometric synthetic aperture radar (DInSAR) has been widely used as one of the most important technologies for determining coseismic deformation. However, DInSAR processing is often perturbed by errors including atmospheric effects and those in topographic models, SAR satellite orbits, and phase unwrapping operation. These nuisance components can degrade the accuracy of the measurements and, therefore, distort the inversion of fault slips especially for moderate earthquakes. We propose in this letter a multitemporal InSAR (MTInSAR) method aiming to accurately determine coseismic deformation. By jointly analyzing a set of preseismic SAR images and one postseismic image, the method allows effective separation of coseismic deformation from topographic and satellite orbital errors deformation based on the distinct spatio-temporal characteristics of the signals. Since the solution is achieved directly from wrapped differential phases, the retrieved deformation is also immune to phase unwrapping errors. The October 6, 2008 Mw 6.3 Dangxiong, China earthquake is studied with the proposed method as an example. The slip inverted, respectively, from the MTInSAR and DInSAR coseismic deformation measurements shows up to 34-cm differences, indicating that the topographic error and inaccurate removal of orbital errors can bias the fault slip inversion.

Detecting the Deformation Anomalies Induced by Underground Construction Using Multiplatform MT-InSAR: A Case Study in To Kwa Wan Station, Hong Kong

Underground constructions are implemented in cities to relieve the pressure of urbanization. Ground deformation often occurs with improper construction activities. Such deformation can be measured with techniques such as multitemporal interferometric synthetic aperture radar (MT-InSAR). However, the deformation anomalies, such as the building horizontal tilt can cause the MT-InSAR line-of-sight deformation to tend to zero, the deformed area is likely to be ignored when the individual SAR dataset is applied. In this article, we explore deformation induced by the underground construction of To Kwa Wan (TKW) station in Hong Kong with an improved MT-InSAR method. Multiplatform SAR datasets from TerraSAR-X, COSMO-SkyMed, and Sentinel-1A are adopted to retrieve comprehensive deformation of ground buildings for stability investigation. The results are validated through cross-validation between SAR datasets. Both spatial and temporal variations of ground deformation are revealed. Ground buildings suffered significant deformation in vertical and horizontal directions, the maximum deformation is around 30 and 40 mm, respectively. Due to the anomalous deformation, the ground buildings around the station are dumped towards the street center. In view of temporal evolution, the deformation is strongly related to the construction stages. The deformation turned to be stable after the completion of the main framework of the TKW station in December 2016. This article suggests that the deformation obtained from individual SAR datasets may sometimes is not noticeable or reliable in the area with complex underground construction. Such complex phenomena can be revealed by using the multiplatform SAR datasets with MT-InSAR.

Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning

Abstract Multi-temporal InSAR (MT-InSAR) techniques proved to be very effective for deformation monitoring. However, decorrelation and other noise sources, can be limiting factors in MT-InSAR. The obtained observations (PS - Persistent scatterers) are usually very demanding from a computational perspective, as they can reach hundreds of thousands of observations. To simplify and speed up the classification process, in this study we present an approach based on Convolutional Neural Networks (CNN) classification models, for the detection of MT-InSAR outlying observations. For each PS, the corresponding MT-InSAR parameters, its neighbouring scatterers parameters and its relative position are considered. Tests in two independent PS datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models are robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained.

Development of an Imagewise Stacking Method to Mitigate Atmospheric Effect With an Application to Lianyan Railway

Much attention has been paid to the subsidence monitoring of large-scale man-made linear features, which are usually characterized by long-distance, large-scale, and nonuniform distribution. The multitemporal interferometric synthetic aperture radar (InSAR) technique can provide ground deformation maps for a large area with high spatial resolution and millimeter precision at low costs. The technique overcomes the limitations of ground-based survey instruments. However, the variable atmospheric turbulent component in flat areas has impacts on InSAR detection capability, especially over low-amplitude quasi-stable deformation areas. We propose an imagewise stacking method, which can improve the InSAR time-series analysis by reducing residual noise while retaining the ground deformation signal. We integrate persistent scatterer and distributed scatterer in our processing strategy and conduct a tentative test on the roadbed subsidence monitoring of the Lianyan high-speed railway using 47 medium-resolution Sentinel-1 images. Comparing the InSAR displacement time series and the results with a continuous BeiDou navigation satellite system network, the standard deviation of 3.3 mm and the mean absolute error of 2.2 mm are obtained. The proposed method reduced the above two error measures by 66.3% and 65.6%, respectively, compared with the spatiotemporal filtering. Moreover, the analysis of the spatiotemporal characteristics of the railway deformations also tells that the railway under study is stable.

An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

The multitemporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this article. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis

Landslide susceptibility maps (LSMs) are generally prepared by integrating multiple prominent thematic layers, including DEM derived products (elevation, slope, and aspect), and other parameters such as lithology, geomorphology, LULC, etc. These parameters can be assigned optimum weights using the analytic hierarchy process (AHP) method, followed by a GIS-based weighted overlay analysis. In recent years, multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been rigorously explored, for land deformation detection and monitoring, by extracting highly stable measurement pixels using tens of SAR acquisitions simultaneously. In this research work, a GIS-based multi-criteria decision analysis to prepare LSMs is proposed, with MT-InSAR derived displacement estimates used as a critical input parameter. An LSM is generated by processing 20 ERS-1/2 and Envisat ASAR images, acquired over ∼120 sq. km wide river basin, located in Uttarakhand, India. The generated LSM is found to be congruent with the susceptible maps made available by the Geological Survey of India (GSI) under the National Landslide Susceptibility Mapping (NLSM) program. Preliminary results indicate that the majority of the unstable zones along the Alaknanda River are correctly identified. The approach is further implemented to generate an updated susceptibility map using 60 scenes of freely available Sentinel-1A dataset, followed by validation through actual field survey. This resulted in the generation of an updated susceptibility map, which helped in the identification of 44.5% new landslide susceptible zones (LSZs). Furthermore, the status of previously identified zones is also quantified. The performance of the proposed approach suggests its usability in generating and updating near-real-time LSMs.

Mapping Surface Deformation Over Tatun Volcano Group, Northern Taiwan Using Multitemporal InSAR

The Tatun Volcano Group (TVG) is located at the northern coast of Taiwan Island with only 15 km to the Taipei metropolis. Recent geothermal and geochemical studies suggest that the TVG is dormant-active rather than extinct, implying the eruption potential to devastate the nearby area of 7 million inhabitants. Although some geodetic tools (e.g., global positioning system and precise leveling) have been utilized to analyze the activity associated with volcanism, it is still challenging for conventional interferometric synthetic aperture radar (InSAR) technique to obtain clear ground surface movement at the TVG due to decorrelation and complex atmospheric delay. In this study, we present an improved multi-temporal InSAR (MTInSAR) approach to explore the temporal evolution and spatial extent of displacement at the TVG from 19 L-band ALOS/PALSAR images acquired between 2007 and 2011. Stratified atmospheric delays and orbit error are corrected with a patch-based joint model as different phase components can be separated according to their distinct spatiotemporal characteristics. The spatial deformation patterns indicate an uplift of 10 mm/year at the SW-NE ridge and subsidence with a rate of 10 mm/year at the E-W ridge. The temporal deformation variations at Mt. Chihshin and Huangzuei have high similarity with correlation coefficients of 0.9 above. This indicates that the subsidence at Mts. Chihshin and Huangzuei might be caused by released hydrothermal fluids related to tectono-magmatic activity within the hydrothermal system beneath the TVG.

Determining Suitable Spaceborne SAR Observations and Ground Control Points for Surface Deformation Study in Rugged Terrain With InSAR Technique

We aim to address two primary issues using the multitemporal (MT) spaceborne SAR observations and InSAR technique to monitor slope stability in rugged terrains. One is how to maximize the observation and application effectiveness based on the geometric distortion in an area of interest for determining appropriate ascending- and descending-orbit SAR observations. The other is how to optimize the selection of ground control points (GCPs) to correct orbital phase errors and reference in deriving valid ground surface displacements in the SBAS InSAR analysis. Four criteria were proposed to identify valid GCPs efficiently and effectively. The 2017 Xinmo landslide event, China, was showcased. The tear-away zone was 100% and ∼50% distorted when imaged by ascending- and descending-orbit Sentinel-1A/B SAR sensors. The descending SAR datasets were preferred. Then, we identified four spatially dispersed GCP clusters with the selection criteria using the SBAS InSAR technique and 45 descending-orbit MT SAR datasets. One GCP per cluster was chosen. With the four GCPs and SBAS analysis, we successfully detected the stable status in the landslide-free area and the prelandslide acceleration movement within the tear-away zone. Valid InSAR-derived results have been obtained. In turn, the GCP selection criteria have been validated, and the two issues resolved.

L 1-Norm Sparse 2-D Phase Unwrapping Algorithm Based on Reliable Redundant Network

Multitemporal interferometric synthetic aperture radar (InSAR) technology has become an important tool for digital elevation model (DEM) generation and surface deformation monitoring. The prerequisite is to obtain the correct unwrapped phase of the interferograms. However, it is still challenging to obtain reliable results by traditional 2-D phase unwrapping (PU) algorithms and even 3-D PU algorithms, when the phase continuity assumption fails due to large deformation gradients or abrupt topographic changes. This letter proposes an L¹-norm sparse 2-D PU algorithm based on reliable redundant network in the phase discontinuous region, which is obtained by implementing ambiguity detector on free network within a given distance. Experiment on real synthetic aperture radar (SAR) interferograms covering urban tall buildings verified that the algorithm can obtain more reliable results in phase discontinuous regions than traditional PU algorithms.

An approach for retrieving complete three-dimensional ground displacement components from two parallel-track InSAR measurements

In the investigation and evaluation of geological hazards such as those caused by co-seismic displacement, displacement due to underground mining, landslides, volcanic eruptions, it is important to retrieve the complete three-dimensional ground displacement components. However, both conventional D-InSAR (differential synthetic aperture radar interferometry) and MT-InSAR (multi-temporal InSAR) can only measure the displacement component along the radar LOS (line of sight) direction, which greatly limits the application of InSAR technique. Over the past decades, great endeavors have been made to overcome its limitation of the one-dimensional LOS measurements, and several methods have been proposed to retrieve complete three-dimensional displacement components from InSAR measurements. On the whole, the commonality of the existing methods is characterized by using both ascending and descending SAR data. In this paper, we put forward an approach for retrieving complete three-dimensional ground displacement components from two parallel-track InSAR measurements and present the analytical expressions for the vertical, the North–South, the East–West displacement components. It reveals that the vertical displacement component is just a function of the two parallel-track InSAR LOS displacements, i.e., the vertical displacement component can be retrieved from the two parallel-track InSAR LOS displacements, while the North–South or the East–West displacement component is a function of both the two parallel-track InSAR LOS displacements and the azimuthal displacement. What is more, the proposed approach can effectively improve the accuracy of the azimuthal measurement by using a redundant measurement. As a result, the accuracies of the derived North–South and East–West displacement components are improved as well, which can provide more reliable results for characterizing ground movement associated with geological hazards. In terms of their geographic coverage, there is a strong complementary relationship between the proposed approach and the existing methods. Thus, they can be integratedly utilized in the practical applications. Finally, the approach is illustrated through the retrieval of the complete three-dimensional co-seismic displacement components in an area to the west of Bam, Iran, where a strong earthquake occurred in 2003.

Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis

Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

The Gobi Desert in southern Mongolia has been identified as the strongest dust storm hot spot threatening public health and socio-economic activities in East Asian countries. Despite its significance, the complete mapping of the aeolian surface erosion in southern Mongolian remains unresolved because of the extensive region of interest that cannot be interpreted easily by conventional approaches. Therefore, in this study, we built a mapping scheme to define undergoing aeolian erosion and applied it over the southern Gobi Desert. The remote sensing approach applied here was based on an interferometric synthetic-aperture radar (InSAR) time series technique. A number of Sentinel-1 InSAR pairs that generate phase coherences for a certain period were synthesized via the means of principle component analysis (PCA) to extract the topographic persistence indicative of surface erosion rates. Validation analyses performed through inter-comparisons of phase coherence signals over landmark areas and residuals between global digital elevation models (DEMs) confirmed the reliability of outputs. The results revealed the geological lineaments in southern Mongolia confining sandy deposits and the sediment transportation pathways. Apparently, such bounded aeolian deposits and transportation mechanisms within geological structures have significantly contributed to dust generation in the Gobi Desert over southern Mongolia. In addition, this study demonstrated that the newly developed InSAR time series technique has great potential for identifying intensified land erosion and dust sources.

Adaptive Multilooking of Multitemporal Differential SAR Interferometric Data Stack Using Directional Statistics

In this article, we present an innovative space–time adaptive multilooking technique that operates on a sequence of multitemporal, differential synthetic aperture radar interferograms. The developed approach relies on the application of the fundamentals of directional statistics theory. At variance with other methods that identify the set of statistically homogenous pixels (SHPs) within a multilooking (complex averaging) window based on the statistics of the single-look-complex (SLC) SAR images, the proposed method is exclusively based on the analysis of the multitemporal sequence of full resolution DInSAR interferograms. The SHPs are then used to generate spatially adaptive multilooked interferograms both at the native, full-scale grid of the SLC images and at the multilooked resolution scale. The algorithm is effective and simple to implement, only requiring the availability of a sequence of full-scale differential SAR interferometry (DInSAR) interferograms. The interferograms can then be used to generate ground displacement time-series through advanced multitemporal interferometric SAR (MTInSAR) approaches. Experimental results obtained by applying the adopted technique to two SAR data sets acquired at X- and L-band, respectively, demonstrate the validity of the developed method.