Batch filtering of multi-baseline SAR interferograms

<p>The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Additionally, the process of calculating interferograms itself directly affects the accuracy of the SBAS-InSAR measurements, whereby the selection of high-quality interferogram pairs is crucial for SBAS data processing. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. However, there are some methods including simulated annealing (SA) searching strategy, the graph theory (GT) and others. Until now, the most effective approach of high-quality interferogram selection still relies on the traditional manual method. Due to the high degree of human interaction and a large risk of repetitive work, this traditional manual method increases the instability and inconsistency of the deformation calculation.<br>Considering that the different qualities of interference pairs show different color characteristics, the DCNN method is adopted in this study. The ResNet50 model (one of DCNN models) has the advantages of representing a standard network structure and easy programming. The idea is based on the fact that interferograms less contaminated by different noise sources display smaller color phase changes within a certain phase range. Hence, training sets containing almost 3000 interferograms obtained from land subsidences in several subregions of Shenzhen in China with varying contaminations of noise were established. Up next, the ResNet50–DCNN model was set up, the respective parameters were determined through analysis of the data sets trained, and traditional interferogram selection methods were used to evaluate the performance. For simulation experiments and the evaluation and validation of real data, phase unwrapping interferograms obtained by the time-spatial baseline threshold method are used to classify high and low quality interferograms based on the ResNet50 model. The quantity of high quality interferograms extracted by the ResNet50–DCNN method is above 90% for the simulation experiment and above 87% concerning the real data experiment, which reflects the accuracy and reliability of the proposed method. A comparison of the overall surface subsidence rates and the deformation information of local PS points reveals little difference between the land subsidence rates obtained by the ResNet50–DCNN method and the actual simulations or the manual method. <br>The proposed advanced method provides an automatized and fast interferogram selection process for high quality data, which contributes significantly to the application of SBAS-InSAR engineering. For future research, we will expand the training samples and study DCNN models to further improve the general accuracy for a wider applicability of this method.</p>

GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM.

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Slow-moving landslides in high-mountain regions pose a significant natural hazard and are capable of delivering large sediment volumes to the fluvial system. Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) allows us to identify unstable and potentially dangerous areas prone to landsliding, but this technique also helps quantify seasonal dynamics for predicting landslide behavior.Our study in the Eastern Cordillera of the Argentine Andes focuses on enhancing InSAR's reliability for landslide mapping. This region is characterized by moisture changes along the topographic gradient across the orogen and seasonal variability associated with the South American Summer Monsoon. We extract InSAR time series data from Sentinel-1A/B's C-band (2014-2022) and ALOS1 PALSAR's L-band (2006-2011). Tropospheric delay is caused by atmospheric turbulence and vertical stratification changes. These delays can introduce significant errors in deformation measurements, thus impacting the quality of maps portraying landslide deformation rates. To address this problem, we apply various correction techniques, ranging from spatial and temporal filtering to water-vapor estimation from an atmospheric model. Fading signal noise, another challenge caused by multi-looking and short temporal baselines in the Small Baseline Subset (SBAS) technique, additionally compromises InSAR time series accuracy. We investigate the pattern and magnitude of fading signals in landslide areas using Small Baseline Subset (SBAS) with different neighboring connections and non-linear phase inversion methods, such as the Eigenvalue Decomposition-based Maximum Likelihood (EMI), Eigenvalue Decomposition (EVD), and the Phase Triangulation Algorithm (PTA).Our research evaluates both statistical methods and Global Atmospheric Models for correcting tropospheric delays and fading signal noise. We explore statistical methods, such as double-difference filtering and corrections based on phase elevation, for different spatial windows, including individual catchments, moving windows, and adaptive window sizes. The efficiency of these methods varies with the environmental and topographic conditions in the orogen. Both stratified and turbulent components of the troposphere, along with fading signal noise, can significantly influence tropospheric delay and time series quality. In the context of the factors that influence deformation signals and the combined array of methods to obtain robust measurements, we can identify the spatial and temporal characteristics of slow-moving landslides and assess the different impacts on rate changes.

Ground subsidence is a common geological phenomenon occurring in mining areas. As an important Chinese gold mine, Sanshandao Gold Mine has a mining history of 25 years, with remarkable ground subsidence deformation. Mining development, life security, property security and ecological protection all require comprehension of the ground subsidence characteristics and evolution in the mining area. In this study, the mining subsidence phenomenon of the Sanshandao Gold Mine was investigated and analyzed based on Persistent Scatterer Interferometry (PSI) and small baseline subset (SBAS). The SAR (synthetic aperture radar) images covering the study area were acquired by the Sentinel-1A satellite between 2018 and 2021; 54 images (between 22 February 2018 and 25 May 2021) were processed using the PSI technique and 24 images (between 11 April 2018 and 12 July 2021) were processed using the SBAS technique. In addition, GACOS (generic atmospheric correction online service) data were adopted to eliminate the atmospheric error in both kinds of data processing. The interferometric synthetic aperture radar (InSAR) results showed a basically consistent subsidence area and a similar subsidence pattern. Both InSAR results indicated that the maximum LOS (line of sight) subsidence velocity is about 49 mm/year. The main subsidence zone is situated in the main mining area, extending in the northwest and southeast directions. According to the subsidence displacement of several representative sites in the mining area, we found that the PSI result has a higher subsidence displacement value compared to the SBAS result. Mining activities were accompanied by ground subsidence in the mining area: the ground subsidence phenomenon is exacerbated by the increasing mining quantity. Temporally, the mining subsidence lags behind the increase in mining quantity by about three months. In summary, the mining area has varying degrees of ground subsidence, monitored by two reliable time-series InSAR techniques. Further study of the subsidence mechanism is necessary to forecast ground subsidence and instruct mining activities.

Land deformation caused by the overexploitation of groundwater has become a potential geological hazard in Changzhou, China. To reduce the potential damage from deformation disasters, it is quite necessary to monitor them. In this study, C-band ENVISAT ASAR synthetic aperture radar (SAR) datasets acquired from 2004 to 2010 were processed with the small baseline subset (SBAS) interferometric SAR (InSAR) method to investigate the spatial–temporal distribution of land deformation. Levelling survey data were used to assess the deformation accuracy measured by the InSAR method, and groundwater level data were used to analyse the incentives for deformation. The SBAS InSAR results found several subsidence regions in the Wujin district and the central part of the Zhonglou district. The annual deformation rate calculated from the C-band interferograms ranged from −65 to +35 mm/yr in the line of sight (LOS) direction, and the cumulative subsidence ranged from −0.35 to 0.13 m in the vertical direction. The levelling survey data show that the two measurements are generally consistent. Correlation analysis between the groundwater table and InSAR measurements at six groundwater well stations shows that groundwater recharges can cause land rebound within the Changzhou urban region. However, subsidence was also observed simultaneously with a rising water table, which might have been caused by the status of consolidation of the strata. Our study provides scientific evidence on the management of groundwater extraction and the assessment of land-subsidence hazard.

Modern Yellow River Delta has become one of the areas with serious land subsidence disaster in the plain area of Shandong Province. In this paper, Small Baseline Subsets - Interferometry Synthetic Aperture Radar (SBAS-InSAR) technology was used to process the Sentinel-1A data from August 2018 to October 2019 in the modern Yellow River Delta, in order to obtain the range and rate of land subsidence in the study area. At the same time, use the Leveling results of the same period to verify the accuracy. By selecting four larger land subsidence rate as feature points to analyze the characteristics of surface time series deformation, the influencing factors of land subsidence in this area are found out. The results show that during the past 2018-2019 years, the land subsidence in the the Yellow River Delta area was mainly distributed in Xianhe Town, southern the Huanghekou Town, Hubin Town, Guangrao County and its surrounding areas, DaWang Town, Yingli Town, Yangkou Town and the western part of Binhai Township, the most serious subsidence area is located in Xianhe Town, with an annual settlement rate of -204.7 mm/a. The land subsidence in the Yellow River Delta is mainly affected by groundwater overdraft, and the distribution characteristics of land subsidence funnel and deep groundwater depression funnel are basically consistent. In addition, natural factors such as consolidation of Quaternary loose sediments and regional tectonic movement also exacerbate the occurrence of land subsidence.

The southwestern part of Shandong province has become one of the most serious land subsidence disasters in Shandong Province. Through a long series of land subsidence monitoring in this area, the land subsidence status in this area can be fully grasped, and scientific basis for the prevention and treatment of land subsidence can be provided. In this paper, Small Baseline Subsets - Interferometry Synthetic Aperture Radar (SBAS-InSAR) technology was used to process the Sentinel-1A data from September 2019 to October 2020 in southwestern Shandong, in order to obtain the range and rate of ground subsidence in this area. At the same time, the precision is verified by the leveling results of the same period. Three feature points are selected to analyze the temporal deformation characteristics of the surface, and the influencing factors of the ground subsidence in the study area are analyzed. The results indicate that during the past 2019-2020 years, the ground subsidence in the southwestern Shandong province was mainly distributed in the east of Mudan District of Heze City, the north of Dingtao District of Heze City, Yuncheng County, Shan County, Jinxiang County of Jining City and its surrounding areas. The most severe subsidence area is located in Yuncheng County, with an annual subsidence rate of 129.1 mm/a. The ground subsidence in southwest Shandong is significantly affected by groundwater overexploitation and coal mining.

Snow Water Equivalent (SWE) is a critical parameter in the global and regional water cycle and climate system. However, accurately measuring SWE change using satellite remote sensing remains a challenge. While the Interferometric Synthetic Aperture Radar (InSAR) is a promising technique to retrieve SWE from space, its application has been constrained until recently by the lack of spaceborne observations combining optimal low-frequency (e.g., L-band) radar frequencies with short temporal baselines. Furthermore, interferometric coherence is a key factor that affects the accuracy of the unwrapped phase and the subsequent SWE retrieval. However, the repeat-pass InSAR coherence modelling over snow has not been sufficiently investigated.Our study presents the first demonstration of SWE change retrieval using spaceborne repeat-pass L-band InSAR observations from the Chinese Lutan-1 mission. The study area focused on the Altay region in Xinjiang, China, during the winter of 2023–2024. Continuous interferometric pairs with 4/8-day temporal baselines are processed for phase changes and then estimate SWE variations. The retrieved SWE change shows a good agreement with in-situ SWE observations during the dry snow period (January 12 to February 9, 2024), with a Root Mean Square Error (RMSE) of 9 mm and a correlation coefficient (R) of 0.48 for the 4-day temporal baselines. However, a heavy snowfall event observed from February 9 to 17, 2024, induced severe decorrelation, leading to phase unwrapping errors that pose a challenge to SWE retrieval. To address the decorrelation mechanism of snow, the InSAR coherence model for snow is established based on the assumption of a bivariate Gaussian distribution for the ground and snow surface. The time-series modeled coherence shows a consistent trend with the observed Lutan-1 coherence, capturing effectively the decorrelation process caused by snowfall events and snow compaction processes. Furthermore, validation of the modeled coherence against Lutan-1 observations shows a strong agreement (R=0.87) over the entire study period from January 12 to March 28, 2024.Overall, this study demonstrates the capability of spaceborne L-band InSAR with short revisit intervals to effectively retrieve SWE change under appropriate snow conditions. However, the retrieval accuracy is significantly constrained by severe decorrelation during heavy snowfall events. These results highlight both the potential and challenges of operational SWE monitoring from existing and upcoming L-band SAR missions such as Chinese Lutan-1, NASA’s NISAR, JAXA’s ALOS-4, and ESA’s ROSE-L, which are characterized by short repeat cycles, wide swath coverage, and high spatial resolution.

The water level change has a great impact on the composition of plant species, the variety of the species and the succession of the colony of the Yellow River Delta wetland. However, due to the lack of enough hydrological sites, it is very difficult to obtain the water level change of the wetlands, especially those covered with vegetation. Synthetic aperture radar Interferometry (InSAR) has been proposed and used to solve the problem. However, with limited InSAR pairs, water level change time series cannot be obtained. In this paper, an InSAR time series analysis method called small baseline subset approach (SBAS) has been used to solve this problem. A total of 13 scenes of PALSAR L band SAR images are used for small baseline subset analysis. Only interferometric pairs with average coherence above 0.3 in the region of interest (wetlands with vegetation cover) are selected. Then small baseline subset approach was applied to interferograms after flattening, removal of the phase of terrain, filtering and phase unwrapping using minimum cost flow algorithm. Finally time series of relative water level change from June 28th, 2007 to December 31th, 2008 was obtained. Preliminary results on test sites showed an approximate linear-rate water level change. Our experiments showed the feasibility and potential of wetland water level change monitoring using SBAS.

In this work we investigate the deformation signals affecting the coastal region of the megacity of Shangai (China) where, to satisfy the growing land demand for industrial and urban development, man-made lands reclaimed from the sea have been retrieved and used to build airports, harbors, and industrial areas. The presented analysis is carried out through the application of spaceborne Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) techniques. A general picture of the ground deformation field is provided by applying the Small Baseline Subset (SBAS) as well as the Permanent Scattereres (PS) approaches to archives of SAR images collected by ENVISAT and COSMO-SkyMed sensors from 2007 to 2014 over the investigated area.

This study presents the application of interferometric synthetic aperture radar (InSAR), small baseline subset (SBAS) algorithm and GIS for subsidence analysis in a mining area located at Gangwon-do, Korea. Several mining areas at the Samchuk coalfield had operating coalmines until 2002–2003. Ground deformation has occurred as a form of subsidence due to the continuous mining activity or the collapse of residual cavities. Twenty-three scenes from a JERS-1 satellite acquired from 1992 to 1998 are used to detect the ground movement by means of the differential InSAR process. Distinctive phase variations induced by subsidence were observed in several interferograms. The SBAS algorithm was applied for a detailed analysis. As a result, it was confirmed that deformation was occurring between September 21, 1992 and September 30, 1998 and attained 22 cm at the most significantly subsiding site. The mean and standard deviation of RMS error were 2.0 cm and 0.78 cm, respectively, showing the level of the error due to the used digital elevation model, unwrapping and atmospheric effect. The SBAS-derived cumulative subsidence map was validated with the underground workings and geological maps in order to find the spatial relations and subsiding mechanism. From the spatial comparison with these data, it was shown that the subsidence has taken place above excavations and fault lines.

Phase unwrapping (PU) is a key processing step in interferometric synthetic aperture radar (InSAR). To date, a number of skillful single-baseline (SB) and multibaseline (MB) PU methods exhibiting different advantages have been proposed. However, as the basic principles of SB and MB PUs are essentially different, it is difficult to effectively and systematically compare the performance of SB and MB PUs, despite the knowledge that this type of analysis is important for allowing the ever-increasing number of InSAR practitioners to choose a suitable approach for practical applications and to optimally plan future InSAR satellite missions. Recently, the framework of the two-stage programming approach (TSPA) was proposed, and this allows for the majority of the existing SB PU methods to be transplanted into the MB domain, to allow practitioners to feasibly and comprehensively compare SB and MB PU methodologies. In this study, the digital elevation model (DEM) reconstruction accuracy is compared between the classical SB PU methods and their corresponding TSPA-framework-based MB PU methods using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L^{p}$ </tex-math></inline-formula> -norm model. Interestingly, we observed that although the number of PU residues in the MB case is larger than that in the SB case, the MB PU performance is better. The reason for this is that the type of MB residue is typically dipole, so the average length of the required MB branch-cut is shorter than that of SB. It is also demonstrated that the TSPA framework can effectively improve the PU accuracy of many existing SB PU methods when the number of input interferograms is sufficient.

A ground‐based synthetic aperture radar (GB SAR) interferometer was employed to detect small terrain movements which occurred over long periods of time on a landslide hazard zone in the Citrin Valley in northern Italy. Three measurement campaigns were carried out from September 2003 to September 2004. The radar instrumentation was carefully re‐installed at each campaign on the same observation point in order to avoid geometric decorrelation. Because of the global loss of coherence due to the long temporal separation between the observations, the retrieved displacements are limited to a number of very coherent points throughout the surveyed scenario. Radar data are locally compared with data provided by a global positioning system (GPS) network installed on the area interested by the landslide. Digital elevation model, photographic images, interferometric and GPS data are integrated within GIS environment providing a very effective geophysical knowledge tool.

Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain’s responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.