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Background: The cloud-penetrating and fog-penetrating capability of Synthetic Aperture Radar (SAR) give it the potential for application in forest fire progress monitoring; however, the low extraction accuracy and significant salt-and-pepper noise in SAR remote sensing mapping of the burned area are problems. Aims: This paper provides a method for accurately extracting the burned area based on fully exploiting the changes in multiple different dimensional feature parameters of dual-polarised SAR images before and after a fire. Methods: This paper describes forest fire progress monitoring using dual-polarisation SAR images combined with multi-scale segmentation and unsupervised classification. We first constructed polarisation feature and texture feature datasets using multi-scene Sentinel-1 images. A multi-scale segmentation algorithm was then used to generate objects to suppress the salt-and-pepper noise, followed by an unsupervised classification method to extract the burned area. Key results: The accuracy of burned area extraction in this paper is 91.67%, an improvement of 33.70% compared to the pixel-based classification results. Conclusions: Compared with the pixel-based method, our method effectively suppresses the salt-and-pepper noise and improves the SAR burned area extraction accuracy. Implications: The fire monitoring method using SAR images provides a reference for extracting the burned area under continuous cloud or smoke cover. This paper describes a method to monitor forest fire progress using dual-polarisation Synthetic Aperture Radar (SAR) images combined with multi-scale segmentation and unsupervised classification. We aimed to take full advantage of the many different dimensions of feature parameter changes caused by forest fires, relying on time-series dual-polarised SAR imagery to achieve burned area extraction and forest fire progress monitoring. [ABSTRACT FROM AUTHOR]

Monitoring and modeling ground surface deformation are crucial for the dynamic assessment of geothermal resources and sustainable exploitation in a geothermal field. In this study, we extract the deformation in the Yangbajing geothermal field by the small baseline-synthetic aperture radar interferometry (SBAS-InSAR) method using 141 Sentinel-1A images collected between March 2017 and November 2021. The InSAR result indicates both uplift and subsidence in the geothermal field. Subsequently, we use a dual-source model combining a dipping ellipsoid and a rectangular surface to model the shallow reservoir that contracted to cause the complex subsidence field in the north of the geothermal field. The shallow reservoir that expanded to cause the uplift in the south is modeled by an ellipsoid source. The parameters inversion is processed by the nonlinear Bayesian inversion method which has been applied to search the optimal parameters setting in a priori space and evaluate the uncertainties by the confidence intervals. To validate our inversion results, we collect the data from 31 wells including extraction and injection wells, detailed tectonic survey data and geothermal isotherms of the Yangbajing geothermal field. The modeled shallow reservoir in the north is within a 140 °C ground isotherm and a 160 °C underground isotherm. The thickness and depth of the reservoir slightly exceed the data from producing wells, probably due to the structural subsidence of the reservoir. The geometric structure is consistent with the channel faults that control the formation and development of the shallow reservoirs. The modeled reservoir under the rebound area in the south is also validated by comparing the data. Furthermore, we estimate the volume loss and recovery of the contracting and expanding reservoirs. We analyse the deformation mechanisms by considering the tectonic formation, the reservoir structure, and the extraction and rechargeability of the reservoirs. This research suggests the deformation pattern of shallow porous reservoirs that have similar layer composition and tectonic structure elsewhere in the world. Moreover, it provides a theoretical model to explore the parameters and volume change of geothermal reservoirs in plateau-embedded basins around the world. Our work is significant for the conservation and development of geothermal energy in geothermal fields that experience deformation, both subsidence and uplift. [ABSTRACT FROM AUTHOR]

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM's accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%. [ABSTRACT FROM AUTHOR]

Mapping the outlines of glaciers has primarily relied on the interpretation of satellite optical images. However, the accurate delineation of glaciers in complex terrain mountain regions remains challenging, mainly because the supraglacial debris-covered ablation zones and snow-covered accumulation zones often exhibit the same spectral properties as their adjacent grounds in optical images. This study presents a novel approach by exploring both the satellite synthetic aperture radar (SAR) amplitude and interferometric coherence to map mountain glaciers. This method explores the deviation of the glacier surface signal in the SAR time series to distinguish glacier ice from the surrounding stable ground. To this end, we explored the classifying capabilities of two indices from a set of SAR images, SAR interferometric coherence and amplitude deviation index (ADI), to determine glacier boundary. We found that the two indices complement each other for mapping glaciers. A ratio map based on ADI and SAR coherence (ACR) was then derived, from which the glacier outline was automatically tracked using a specified threshold, followed by manual modification. We validated this approach on two typical valley glaciers, the debris-covered Hailuogou Glacier and debris-free Mozigou Glacier, in Mount Gongga in the southeastern Tibetan Plateau. The results show that the proposed ACR criteria can significantly enhance the contrast between glaciers and their surroundings. By comparing our results with manually delineated glacier outlines from high-resolution cloud-free satellite optical imagery, we found that the misclassification rate and difference rate for our results were 2.6% and 4.2%, respectively. The approach presented in this study can be easily adapted to map the outlines of mountain glaciers worldwide efficiently and is useful for inferring glacier boundary changes in a climate warming context. [ABSTRACT FROM AUTHOR]

Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone. [ABSTRACT FROM AUTHOR]

Multiple aperture interferometric synthetic aperture radar (MAI) can measure ground displacements along SAR track. However, MAI measurements may suffer from severe ionospheric error, especially for long-wavelength SAR sensors. This study presents a new approach, azimuth split-spectrum interferometry (AziSSI), to correct ionospheric errors in MAI measurements. The proposed method can straightforwardly resolve ionospheric phase delay by exploiting two subband MAI interferograms with different centroid frequencies. We utilized two groups of ALOS-1 PALSAR images, which cover the 2008 Wenchuan earthquake containing strong coseismic ground deformation and a stable coast region in Chile, respectively, to test the proposed method. Experimental results show that the AziSSI method can successfully recover the coseismic displacements induced by the Wenchuan earthquake and circumvent the azimuth stripes for the Chile case’s MAI measurements. The maximum ionosphere-induced azimuth shifts are about 2.2 m for the Wenchuan case and 2.5 m for the Chile case. We validated the correction performance of the AziSSI method with the Wenchuan case by comparing the compensated azimuth displacements with the simulated deformation from a forward fault slip model and coseismic global positioning system (GPS) measurements. The validations show that the deformation patterns after the ionospheric correction are consistent with the simulations of the sinistral slip of the causative fault. The root-mean-square error between the GPS and MAI measurements is reduced from 0.77 to 0.28 m before and after the ionospheric correction, indicating that AziSSI can significantly compensate ionospheric errors for MAI. [ABSTRACT FROM AUTHOR]

Highlights • A nonlinear inversion of InSAR-observed deformation is developed to map fault dip. • Fault geometry of Wenchuan earthquake is retrieved with varying dips along depth. • Coseismic slip distribution reveals variable motion features on fault surface. Abstract Satellite Interferometric Synthetic Aperture Radar (InSAR) is playing an increasingly important role in the observation of coseismic surface deformation caused by earthquakes, and has been used to invert for subsurface fault structure and reveal earthquake source mechanisms. However, the mapping of complex non-planar or curved (e.g., listric-shaped) faults still remains a challenging task due to variable dips along the underground depth and the impenetrability of the deep crust. Here, we develop a set of new inversion algorithms to determine the listric fault geometry with InSAR- and GPS-observed surface deformation as the significant constraints. The fault surface with variable dip angles is discretized into consecutive sub-fault layers along the down-dip direction. A nonlinear iteration algorithm is used to minimize the objective function to determine the dip angle for each sub-fault layer. The proposed method is first tested using synthetic data to show its effectiveness for retrieval of varying fault geometry dips, and then applied to the 2008 Mw 7.9 Wenchuan earthquake that ruptured the Yingxiu-Beichuan fault for over 320 km along the southwest-northeast strike. The inversion shows that the dip angle of the seismogenic fault is up to 76° near the surface layer, and gradually decreases along the down-dip direction. A significant decrease in dip occurs within the depths of 6–15 km with a dip of 32° at a depth of 15 km. The dip angle decreases to 2° at a depth of 20 km, and finally merges with the subparallel PengGuan fault, which is basically consistent with geological investigations and seismic waveform data inversion. Using the inferred fault geometry, the slip model associated with the event is estimated. Five high-slip concentrations along the strike of the Yingxiu-Beichuan fault are recognized. The inversion misfit of InSAR data is reduced to 7.1 cm with a significant improvement compared to previous studies. [ABSTRACT FROM AUTHOR]

The new land observation satellite Sentinel-1A was launched on 25 April 2014 with a C-band synthetic aperture radar (SAR) sensor, which has the significant enhancements in terms of revisit period and high resolution. The Mw 6.1 Napa, California earthquake occurring on 24 August 2014, almost 4 months after the launch, is the first moderate earthquake imaged by the Sentinel-1A. This provides an opportunity to map the coseismic deformation of the event and evaluate the potential of Sentinel-1A SAR for earthquake study. Two techniques including the interferometric SAR (InSAR) and pixel offset-tracking (PO) are, respectively, employed to map the surface deformation along the radar line of sight (LOS), azimuth and slant-range directions. The cross comparison between Sentinel-1A InSAR LOS deformation and GPS observations indicates good agreement with an accuracy of ~2.6 mm. We further estimate the earthquake source model with the external COSMO-SkyMed InSAR and GPS data as constraints, and forward calculate the surface deformation as cross validation with the Sentinel-1A observations. The comparison between the observed and modeled deformation shows that the Sentinel-1A measurement accuracy can achieve 1.6 cm for InSAR technique along LOS direction, and 6.3 and 6.7 cm for PO along azimuth and range directions, respectively. [ABSTRACT FROM AUTHOR]

The persistent scatterer interferometric synthetic aperture radar (PSI) has proven to be a powerful tool for monitoring surface deformation. However, the conventional deformation models cannot be fully adapted to analysis of the complicated deformation process. Taking into account seasonal response and tectonic movement, this letter presents an integrated deformation model for separating deformation components through PSI time series, thus obtaining the linear deformation rate, the deformation acceleration and the seasonal deformation amplitude at each PS. An iterative solution method is proposed to estimate the parameters in the integrated model. The experiments are carried out for subsidence detection over the northwestern part of Tianjin (China) by using the 40 high-resolution TerraSAR-X SAR images acquired between 2009 and 2010 and the ground truth data collected by precise leveling at seven benchmarks and six man-made corner reflectors. The testing results indicate that the integrated model has better adaptability to the subsidence process in the study area than the conventional models. The further analysis shows that the deformation results derived from the iterative solution are in better agreement with the ground truth data. These demonstrate that the proposed methodology is effective for monitoring the surface deformation with remarkable nonlinear property. [ABSTRACT FROM PUBLISHER]

Ground subsidence is a major concern for land use planning and engineering risk assessment. This paper explores subsidence detection by the persistent scatterer (PS) interferometric synthetic aperture radar (InSAR) technique using the multitemporal high resolution spaceborne SAR images. We first describe the mathematical models and the data reduction procedures of the PS solution. The experiments of subsidence detection are then carried out over the Jinghai County in Tianjin (China) which has been sinking due to overuse of groundwater. The time series of high resolution SAR images collected by the X-band radar sensor onboard the satellite TerraSAR-X (TSX) are utilized for the PS detection, PS networking and subsidence estimation. The experimental results demonstrate that the high resolution of TSX SAR images can dramatically increase the PSs' density and coverage extent, especially in the built-up areas. Subsidence values can be extracted on the individual objects like buildings, street lamps and manhole covers, and on the linear engineering structures like the Jinghu high-speed railway. The PS InSAR with short radar wavelength (3.1 cm) is quite sensitive to ground displacement in the radar line-of-sight direction, and the derived subsidence measurements are in good agreement with the in situ data taken by optical leveling. [ABSTRACT FROM PUBLISHER]

Abstract: The Ms8.0 Wenchuan earthquake (in China) occurred on 12 May 2008 as a result of slip on the northeastern-striking Longmen Shan (LMS) faults beneath the rugged margin between the Qinghai-Tibet Plateau and Sichuan Basin. The catastrophic event caused significant surface ruptures and permanent ground displacement in a wide area. This paper concentrates on mapping surface deformation caused by the main shock with the interferometric synthetic aperture radar (InSAR) technology. The coseismic interferogram covering an area of over 83,000km2 is computed with use of 46 SAR images that were collected along 6 adjacent ascending orbits by the L-band SAR sensor onboard the Japanese Advanced Land Observing Satellite (ALOS). The displacements measured at 16 GPS sites are used to check the accuracy of the InSAR deformation measurements. The radar coherence is computed and analyzed in relation to the topography and the normalized difference vegetation index (NDVI) estimated from the Landsat-7 imagery. The results show that the coseismic surface deformation can be mapped up to a centimeter-accuracy level even over the highly mountainous and heavily vegetated area with the L-band interferometer. It is also demonstrated that the L-band interferograms with time interval of months to years can still maintain acceptable radar coherence for deformation extraction over the area under the extreme conditions. The extracted InSAR deformation measurements show that the lands in the Sichuan Basin had moved 0.1–1.3m toward the satellite along the radar line of sight (LOS) direction with an azimuth of 349.8° and an elevation angle of 51.3°, while the lands in the LMS area had moved 1.4m at most away from the satellite. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Investigates the accumulating ground settlement at the Chek Lap Kok International Airport using satellite synthetic aperture radar (SAR) interferometry in Hong Kong, China. Reclamation of the land occupied by the airport from the sea; Use of a pair of ERS-2 SAR images; Background of interferometric SAR; Outline of the data processing methods.

Interferometric Synthetic Aperture Radar (InSAR) monitors surface change and displacement over a large area with millimeter-level precision and meter-level resolution. Anar fault, with a length of ~200 km, is located in central Iran. Recent seismological studies on the fault indicated that it is approaching the end of its seismic cycle. Although a large earthquake is imminent, the mechanism of the fault is not well understood. Therefore, understanding and discovering the mechanism of Anar fault remains a challenge. Here, we present an approach of displacement fault analysis utilizing a combination of InSAR data obtained from the persistent scatterer interferometry (PSI) method and 178 Sentinel-1 images (ascending and descending) (2017–2020). We incorporated groundwater samples from 40 wells, radon concentration anomaly mapping, Global Positioning System (GPS), and 3D displacement measurement acquired over four years (2016–2020). We investigated and monitored the deformation of the fault plate's behavior over the last three years (2017–2020) to explore new evidence and signature of displacement. The results show that the time series analysis in the fault range has an increasing displacement rate in all dimensions. We observed that the line-of-sight (LOS) displacement rate varied from −15 mm to 5 mm per year. Our calculations show that the E–W, N–S, and vertical displacement rates of the fault blocks are 2 mm to −2 mm, 6 mm to −6 mm, and 2 mm to −4 mm per year, respectively. An anomaly map of the radon concentration shows that the complete alignment of the high concentration ranges with the fault strike and the radon concentration increased on average from 23.85 Bq/L to 25.30 Bq/L over these three years. Therefore, we predict rising the radon concentration is due to the increase in activity which resulted in a deformation. Finally, our findings show that the Anar fault is an oblique and right-lateral strike-slip with a normal component mechanism. We validated the proposed method and our results by comparing the GPS field data and PSI measurements. The root mean square error (RMSE) of the PSI measurement is estimated to be 0.142 mm. Based on the supporting evidence and signature, we conclude that the Anar fault activity increased between 2017 and 2020. [ABSTRACT FROM AUTHOR]

As an approach with great potential, the interpretation of space-borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography-induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi-temporal SAR images. This paper presents an improved neighborhood-based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between October of 2014 and November of 2020. We first generated the reference intensity image (RII) by averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, then calculated the neighborhood-based ratio between RII and each SAR intensity image, and finally identified the boundaries of the glacial lakes by a ratio threshold determined statistically. The time series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February of the following year. We observed the expansion process with annual growth rates of 3.19% and 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in July 2018. The validation by comparing with the results derived from Sentinel-2A/B optical images indicates that the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision of SAR images to provide regular updates for the dynamic monitoring of glacial lakes. [ABSTRACT FROM AUTHOR]

Coal fire disasters generally occur in major coal-producing countries. They destroyed many coal resources and triggered various environmental problems. The identification and state assessment of coal fire areas are paramount to coal fire management. In this paper, a method for identifying coal fires by integrating land surface temperatures (LSTs) from multitemporal thermal infrared remote sensing and subsidence information from multitemporal interferometric synthetic aperture radar (MT-InSAR) is proposed. The method primarily consists of estimating the LSTs and subsidence values, normalizing and equidistantly partitioning the LSTs, estimating the thermal anomaly frequency (TAF), extracting high-subsidence areas, determining the thermal anomaly frequency threshold (TAFT), and identifying coal fire areas according to the TAFT. The coal fire ratio index (CRI) was developed to quantitatively evaluate the severity of coal fires and its variations. Midong District, Urumqi, Xinjiang, China, was selected as the study area. Forty-five Landsat 8 images and sixty-one Sentinel-1 SAR images were used to retrieve LST and subsidence time series, respectively. The proposed method and CRI were applied to identify and evaluate coal fires in the study area. The results demonstrate the satisfactory reliability of these methods compared with field surveys. The maximum CRI was 6.145 × 10-4 (July 28, 2019), and the minimum CRI was 2.685 × 10-5 (January 13, 2020). Coal fires in summer were more severe than those in winter. The CRI profile presented seasonal and annual periodicities and was affected by the local climate. The soil cohesion and humidity are higher in winter due to possible snowmelt, which weakens the interaction between oxygen and coal seams and the subsequent combustion of coal. The LST, thermal anomaly, and deformation information were combined in a time series overlay analysis to reveal the differences in the LSTs and subsidence values among mining areas, coal fire areas, and areas with both mining and coal fires. The results verify the applicability and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized. [ABSTRACT FROM AUTHOR]

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The sensitivity of synthetic aperture radar (SAR) coherence has been applied in delineating the boundaries of alpine glaciers because it is nearly unaffected by cloud coverage and can collect data day and night. However, very limited work with application of SAR data has been performed for the alpine glaciers in the Qinghai-Tibetan Plateau (QTP) of China. In this study, we attempted to investigate the change of coherence level in alpine glacier zone and access the glacier boundaries in the QTP using time series of Sentinel-1A SAR images. The DaDongkemadi Glacier (DDG) in the central QTP was selected as the study area with land cover mainly classified into wet snow, ice, river outwash and soil land. We utilized 45 Sentinel-1A C-band SAR images collected during October of 2014 through January of 2018 over the DDG to generate time series of interferometric coherence maps, and to further extract the DDG boundaries. Based on the spatiotemporal analysis of coherence values in the selected sampling areas, we first determined the threshold as 0.7 for distinguishing among different ground targets and then extracted the DDG boundaries through threshold-based segmentation and edge detection. The validation was performed by comparing the DDG boundaries extracted from the coherence maps with those extracted from the Sentinel-2B optical image. The testing results show that the wet snow and ice present a relatively low level of coherence (about 0.5), while the river outwash and the soil land present a higher level of coherence (0.8–1.0). It was found that the coherence maps spanning between June and September (i.e., the glacier ablation period) are the most suitable for identifying the snow- and ice-covered areas. When compared with the boundary detected using optical image, the mean value of Jaccard similarity coefficient for the total areas within the DDG boundaries derived from the coherence maps selected around July, August and September reached up to 0.9010. In contrast, the mean value from the coherence maps selected around December was relatively lower (0.8862). However, the coherence maps around December were the most suitable for distinguishing the ice from the river outwash around the DDG terminus, as the river outwash areas could hardly be differentiated from the ice-covered areas from June through September. The correlation analysis performed by using the meteorological data (i.e., air temperature and precipitation records) suggests that the air temperature and precipitation have a more significant influence on the coherence level of the ice and river outwash than the wet snow and soil land. The proposed method, applied efficiently in this study, shows the potential of multi-temporal coherence estimation from the Sentinel-1A mission to access the boundaries of alpine glaciers on a larger scale in the QTP. [ABSTRACT FROM AUTHOR]

The Mw7.3 Iraq earthquake on 12 November 2017 was the largest recorded earthquake in the Zagros Mountains since 1900. In order to quantitatively analyze the co-seismic deformation caused by this earthquake, both the ascending and descending SAR images from the Japan Aerospace Exploration Agency’s ALOS-2 and the European Space Agency’s Sentinel-1A satellites were collected to implement the conventional differential interferometric synthetic aperture radar (DInSAR), multiple aperture InSAR (MAI), and azimuth pixel offset (AZO) methods. Subsequently, the three-dimensional (3D) deformation field was reconstructed over an area of about 60 × 70 km2 by a combined use of the line-of-sight (LOS) motion (detected by the DInSAR method) and the along-track (AT) motion (detected by the MAI method) through the weighted least square method. The experiment indicates that the ALOS-2 satellite performs better than the Sentinel-1A sensor in larger-magnitude earthquake deformation monitoring. Furthermore, the MAI method based on phase differencing has a better performance than the AZO method based on SAR amplitude correlation, as long as the coherence of the interferograms is sufficient. The maximum co-seismic displacements in the up–down, north–south, and east–west directions are approximately 100 cm, 100 cm, and −50 cm, respectively. After comparative analysis between the obtained 3D deformation field and the simulated deformation field with the fault parameters published by the USGS (United States Geological Survey), both co-seismic deformation fields are highly coincident, and the residuals between both (in different directions/dimensional) are in the magnitude of centimeters. Considering the geological structure in the earthquake region and factors of the LOS and 3D co-seismic deformation—such as the trend and location of the deformation bound, the different sign of displacements in hanging wall and footwall, and the locations of mainshock and aftershock—the preliminary conclusion is that the Zagros Mountain Front fault is responsible for the earthquake. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q , which is used to generate three vegetation descriptors: the co-pol purity parameter (m cp ), the pseudo-scattering angle (θ cp ), and the pseudo-scattering entropy (H cp ). Further, a novel unsupervised clustering framework, founded on H cp and θ cp , has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that m cp , θ cp , and H cp exhibit regular changes at different growth stages of crops from planting to maturity, with m cp and θ cp gradually decreasing while H cp gradually increasing. Within the entire phenology window, θ cp changes by approximately 42°, while both m cp and θ cp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources. [ABSTRACT FROM AUTHOR]

Rock glaciers, unique creeping periglacial landforms, have garnered research interest for their valuable insights into hydrological resources and representing potential geological hazards in the context of climate warming. However, owing to the scarcity of high-spatiotemporal-resolution kinematic observations, the mechanisms driving rock glacier movements remain poorly understood. This study introduces a strategy for estimating three-dimensional (3D) velocities of rock glaciers by combining ascending and descending interferometric synthetic aperture radar (InSAR) observations with a surface parallel flow model. We mapped the 3D surface creeping velocities of 1084 rock glaciers in Daxueshan, southwest Tibetan Plateau, from January 2019 to December 2020. Subsequently, we employed the generalized boosted model (GBM), a machine learning approach, to link rock glacier creep to environmental factors. The rock glaciers in Daxueshan exhibited spatially heterogeneous creep, with maximum and average annual velocities of 307.86 mm∙a−1 and 40.37 mm∙a−1, respectively. About 92% of rock glaciers crept with a two-peak seasonal rhythm characterized by "spring acceleration, summer deceleration, autumn acceleration, and winter deceleration". GBM modeling suggested that precipitation and snowmelt were the predominant environmental factors controlling the kinematics of rock glaciers, with relative importance of 32.6% and 31.7%, respectively. Land surface temperature (LST) was the third significant driver at 17.1% of the relative importance. Slope angle and topographic wetness index (TWI) had the least impact. The modeling also quantitatively revealed the distinct nonlinear responses of rock glacier creep to environmental changes. Rock glaciers initiate acceleration at precipitation and snowmelt levels of about 2 mm, LST close to −2 °C, slope angle and TWI higher than 12° and 4.6, and stabilize when these factors reach certain thresholds. Furthermore, the seasonal variations in rock glacier creeping velocity can be primarily attributed to hydrological changes. Our study highlights the value of combining InSAR and machine learning to elucidate the environmental controls on mass-wasting phenomena in mountainous regions. • 3D surface displacements over 1000 rock glaciers were derived by InSAR data. • Rock glacier velocity shows spatial heterogeneity but with clear seasonal rhythms. • InSAR and GBM modeling reveals the environmental controls of rock glacier creep. • Precipitation and snowmelt are the main driving forces of rock glacier creep. [ABSTRACT FROM AUTHOR]

The spaceborne interferometric synthetic aperture radar (InSAR) with persistent scatterer (PS) is utilized to retrieve the spatial characteristics of the largest coseismic landslide Daguangbao, induced by the Ms 8.0 Wenchuan earthquake in Sichuan Province, China. The available twenty interferometric pairs with good coherence selected from the ALOS/PALSAR imagery data covering the Longmen Shan mountainous area are used in the study. We have constructed a natural geodetic observation network with numerous scattered bare rocks emerging after the earthquake and coseismic landslide events, which are effectively recognized as the radar persistent scattering objects. The spatial connections between adjacent PS are established to form observation baselines with differential parameters related to topography and deformation, which are determined by the least squares method from time-series interferometric phase analysis. The post-seismic topographic change relative to the pre-seismic over the landslide area is spatially mapped from the PS network adjustment solution. The quantitative estimation of local elevation change, mass sliding volume and deposit thickness associated with the landslide is conducted. The spatial pattern of mass movement suggests that the giant landslide is characterized by a major sliding length of 4350 m along the NE–SW directions with an extension width of 3400 m along the Huangdongzi gully, and a peak height change of 535 m in the vertical direction. The affected area of landslide mass movement reaches 7.2 km 2 with the volume up to 1.28 billion m 3 . The study also demonstrates the potential of persistent scatterer InSAR technique as an alternative to allow the quantitative measurement of mass wasting volume associated with earthquake-induced giant landslides. [ABSTRACT FROM AUTHOR]