Synthetic Aperture Radar Technique Research Articles

Using Multisensor Earth Observation Data to Understand the Development of the Kourtéré Kori Alluvial Fan and Its Impact on the Hydrology of the Niger River

The Niger River (NR) is threatened with silting up due to the evolution of alluvial fans (AFs) in the Niamey region. The most significant is the Kourtéré AF. This study aims to map the spatial extent of this AF and analyze its progression and impact on the NR using a machine learning algorithm such as support vector machine on Landsat and Sentinel-2 images from between 1985 and 2021. Sentinel-1 data were then used to generate a digital terrain model at 10 m resolution through the Interferometric Synthetic Aperture RADAR (InSAR) technique to understand the Kourtéré watershed hydrology. Results show that the AF area increased from 55.19 ha in 1985 to 303.86 ha in 2003 and 237.18 ha in 2021. The AF has invaded a section of the NR since 2000 and began to invade its main bed in 2016. More than 73.71 ha of NR were converted into AF between 1985 and 2016. The flow direction of the NR has been disturbed by changes in AF, making it easier to overflow at flows much lower than previously. The Kourtéré watershed is dominated by a dense network of gullies with a Sediment-Transport Index of 424.4, which facilitates significant sediment transport into the AF and NR and leads to its silting up.

Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.

Ground Deformation Monitoring over Xinjiang Coal Fire Area by an Adaptive ERA5-Corrected Stacking-InSAR Method

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

Three-dimensional coseismic displacements and slip distribution of the 2021 Mw 7.4 maduo earthquake: Synergy of SAR, InSAR and optical images

On 21 May 2021, an earthquake of moment magnitude (Mw) 7.4 occurred near Maduo county in Qinghai Province China. This is the first major earthquake that occurred in the interior of the Bayan Har block in the Tibetan Plateau over the past 70 decades. Focusing on this event, we conducted a study on three-dimensional (3D) coseismic displacement reconstruction and its tectonic implication. We acquired both Synthetic Aperture Radar (SAR) and optical imaging satellite imagery, including SAR images from Sentinel-1 and Advanced Land Observation Satellite-2 (ALOS-2) as well as the multi-spectrum images from the Sentinel-2 (S2) satellite. We applied the Interferometric SAR (InSAR) and pixel-offset tracking (POT) techniques to coseismic SAR data pairs and reconstructed two dimensional displacements. With the constructed displacement fields in multiple viewing directions, we resolved the 3D coseismic displacements (north-south, east-west, and up components) by integrating the regional strain model with variance components estimation (SM-VCE). We recommend using the standard deviation value at each grid cell, which can be calculated in the resampling, as the initial weight. Based on the resolved 3D coseismic displacements, we further estimated the dip angles for the two segments (F4 and F5) in the east of the rupture zone. The associated moment magnitude is about Mw7.4, which corresponds to the released energy of ∼ 1.74×1020 Nm.

Radar-Based Microwave Breast Imaging Using Neurocomputational Models

In this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.

A Geo-Hazard Risk Assessment Technique for Analyzing Impacts of Surface Subsidence within Onyeama Mine, South East Nigeria

This paper proposes a geo-hazard risk assessment technique to analyze the impacts of surface subsidence monitored in a major coal mine in Nigeria. In many developing countries, disaster risk management schemes have mainly focused on traditional singular hazard assessment, vulnerability assessment, or risk assessment. However, it is difficult to use a singular application to adequately address hazard assessment due to the variation in data requirements, factors associated with the hazards, and the various elements at risk. Most times, hazard assessment schemes heavily rely on data and techniques from different global organizations that collate data on disasters, using various scales and objectives to make informed decisions. Several challenges seemingly arise from total reliance on these kinds of data due to standardization, the exact number of potential victims, and the purpose of the data collection. This makes disaster information collected at the local level unique and assessment schemes more complete; however, the coverage is limited worldwide. The proposed approach combines the spatial relationship between vulnerability assessment and elements at risk to highlight the grave consequences of potential disasters. Thus, the aim is to underscore the importance of integrating local-level inputs in analyzing risk factors and vulnerability indicators for hazard assessment. This study was conducted at the Onyeama coal mine in South East Nigeria. This area has experienced severe negative impacts of subsidence over the years. We exploit data from Sentinel-1 Synthetic Aperture Radar (SAR) Satellites and Small-Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) technique to map the study area. The results generate an elements-at-risk database with a particular focus on population density, road networks, and building networks identified as indices for loss estimation.

Relationship between Crustal Deformation and Thermal Anomalies in the 2022 Ninglang Ms 5.5 Earthquake in China: Clues from InSAR and RST

On 2 January 2022, an earthquake of Ms 5.5 occurred in Ninglang County, Lijiang City, the earthquake-prone area of northwestern Yunnan. Whether this earthquake caused significant deformation and thermal anomalies and whether there is a relationship between them needs further investigation. Currently, multi-source remote sensing technology has become a powerful tool for long-time-series monitoring of earthquakes and active ruptures which mainly focuses on single crustal deformation and thermal anomaly. This study aims to reveal the crustal deformation and thermal anomaly characteristics of the Ninglang earthquake by using both Interferometric Synthetic Aperture Radar (InSAR) and Robust Satellite Techniques (RST). First, Sentinel-1A satellite SAR data were selected to obtain the coseismic deformation field based on Differential InSAR (D-InSAR), and the Small Baseline Set InSAR (SBAS-InSAR) technique was exploited to invert the pre- and post-earthquake displacement sequences. Then, RST was used to extract the thermal anomalies before and after the earthquake by using Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST). The results indicate that the seismic crustal deformation is dominated by subsidence, with 23 thermal anomalies before and after the earthquake. It is speculated that the Yongning Fault in the deformation area is the main seismogenic fault of the Ninglang earthquake, which is dominated by positive fault dip-slip motion. Meanwhile, the seismic fault system composed of NE- and NW-oriented faults is an important factor in the formation of thermal anomalies, which are accompanied by changes in stress at different stages before and after the earthquake. Moreover, the crustal deformation and seismic thermal anomalies are correlated in time and space, and the active rupture activities in the region produce deformation accompanied by changes in thermal radiation. This study provides clues from remote sensing observations for analyzing the Ninglang earthquake and provides a reference for the joint application of InSAR and RST for earthquake monitoring.

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

Study of land subsidence by radar interferometry and hot spot analysis techniques in the Peshawar Basin, Pakistan

Land subsidence caused by anthropogenic activities is frequently reported in many megacities worldwide, affecting over 500 million residents. For the first time, this work is reporting substantial ground subsidence in the fifth largest city of Pakistan. Observation of surface deformation using the Small Baseline Subset (SBAS), Interferometric Synthetic Aperture Radar (InSAR) technique on Sentinel-1A datasets from 2015 to 2019 show subsidence up to 3.23 cm. Geologically, this region is going through active deformation associated with Himalayan orogeny. From August 1973 till December 2018, around ten thousand low- to moderate-magnitude earthquakes are reported.We used the emerging hot spot analysis on InSAR and earthquake data to assess the role of seismicity on subsidence. Four variables were analyzed; ground displacement measured by InSAR, earthquake count, earthquake depth, and earthquake magnitude. Analysis of InSAR data indicates that the vast majority of the area is subsiding. This investigation also reveals that earthquake activity near Peshawar is decreasing. In contrast, the earthquake activity is unstable but high in the direction of Tarbela Dam, which is the world's largest earth-filled dam.No systematic groundwater monitoring program has been implemented, and limited published data suggest that the water demand quadrupled in the last decade. A decline in the water table is also reported. Optimized hot spot analysis of water pumping data for 2017 shows a few isolated mild hot spots in the urban center. Excessive groundwater demand and Land-use change from agriculture to urban may have played a significant role in land subsidence in this region.

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.

Investigating ground deformation due to a series of collapse earthquakes by means of the PS-InSAR technique and Sentinel 1 data in Kandy, Sri Lanka

Time series interferometry synthetic aperture radar (InSAR) techniques have rarely been applied to detect displacement due to low-magnitude (5.5 ≥ M) earthquakes. This study exploits the combined permanent scatterer (PS) interferometry (PS-InSAR) and differential interferometry (D-InSAR) methods to investigate the deformation rates due to low-magnitude partial series of collapse earthquakes. Kandy, the hill capital of Sri Lanka, was experiencing a series of collapsed earthquakes. Historical geological evidence of dynamic topography with prolonged vertical movement further promotes the initiatory InSAR investigation. A series of Sentinel 1A images from the years 2019 to 2021 were employed. Initial D-InSAR near-zero baseline analysis suggested possible displacement in the range of −10 mm / year and +2 mm / year for subsidence and uplift, respectively. Using these prior motion velocities, the temporal coherence was optimized in PS-InSAR. Variogram models and ordinary Kriging (OK) were employed to predict deformation for the areas with limited PS detection. Descending orbits images PS show a dominant uplift of +20 mm / year, which are concentrated over Kandy urban areas. Movements along the ascending line of sight at Victoria Dam in the south are in the range of −40 mm / year. Slopes along the Mahaweli river lineament to the east show subsidence in the range of −29 to 36 mm / year. The coregistered landslide hazard map of Kandy shows deformation areas are exposed to landslide risk. Changes in terrestrial and ground water levels measured with the gravity recovery and climate experiment/GRACE-Follow-On during the period reveal significant irregularities. The study can be considered a prototype example that can be extended to investigate low-magnitude incomplete collapse earthquakes in different geological and geotechnical setups for ground deformation.

A preliminary analysis of the mining-induced rock movement characteristics in the Xinli deposit of the Sanshandao gold mine

Commensurate with economic globalization, the demand for mineral resources is increasing. With increased mining activity, problems related to ground subsidence and rock movement are becoming increasingly prominent, even affecting mining production activities. However, the physical mechanisms behind the ground subsidence phenomenon have been poorly studied, especially for metal mines with a steep dip. This paper applies the Interferometric Synthetic Aperture Radar (InSAR) technique and the numerical simulation method to deduce the characteristics of rock movement in the Xinli deposit of the steeply inclined Sanshandao gold mine. The InSAR results indicate that more subsidence has occurred in the southern part of the Xinli Village coastline area than in the northern part. This is also supported by the numerical simulation results obtained by the fast Lagrangian analysis of continua in three dimensions (FLAC3D). Notably, the range of ground subsidence obtained by numerical simulation shows an obvious asymmetry. The monitoring data of the No. 55 prospecting profile offer a plausible explanation for this, as the surrounding rock of the fault’s hanging wall has a wider range of rock movements. Furthermore, the sublevels of the No. 55 prospecting profile at different depths show different rock movement characteristics, and a logistic function can be well applied to the right part of the settlement curve; the parameter “a” in the function formula is very close to the maximum subsidence value for each sublevel. We defined the ratio “r” to measure the difference between the maximum subsidence value and the corresponding parameter “a” and found this value to be positively correlated with the fractal dimension value of deeper sublevels (−320 m, −400 m, −480 m, and −600 m) and negatively correlated with the fractal dimension value of lower sublevels (−200 m and −240 m).

Time series phase unwrapping algorithm using LP-norm optimization compressive sensing

Time series phase unwrapping is an essential step in the time series interferometric synthetic aperture radar technique for deformation monitoring. However, traditional unwrapping algorithms are prone to unwrapping errors in the case of intense noise and steep deformation gradients, which directly affect the accuracy of the deformation information interpretation. To address this problem, a time series phase unwrapping algorithm using LP-norm optimization compressive sensing is proposed in this paper. This algorithm first transforms the minimizing L0-norm describing compressive sensing technique into a nonconvex problem with weaker constraints, that is, minimizing the LP-norm. Then, an optimized compressed sensing model was proposed by combining the constraint criterion of the phase triplet closure in the temporal domain and the solution strategy of the iterative reweighted least squares method. Improved temporal dimensional phase unwrapping and unwrapping error correction algorithms were developed based on this optimized model. Finally, the proposed algorithm was perfected by combining the above improved methods and the pseudo-three-dimensional phase unwrapping framework. Simulated and Sentinel-1A real datasets verify that the proposed algorithm has better noise robustness, unwrapping stability, and efficiency than traditional algorithms, particularly in the case of a steep deformation gradient and intense noise. The temporal coherence estimated by the proposed algorithm for the real dataset is generally greater than 0.99, which is far superior to traditional algorithms. Furthermore, the proposed algorithm facilitates high-precision interpretation of deformation information. The cross-validation results show that the deformation results obtained by traditional algorithms are underestimated by at least 73 mm/a and 68 mm at the maximum subsidence than the DSInSAR. However, the deformation result obtained by the proposed algorithm is highly consistent with that of the DSInSAR.

A Stretched Deramping Radar Technique for High-Resolution SAR Processing in Ka-Band Using the Extended Integration Time

To increase the image quality of Synthetic Aperture Radar (SAR) is one of the hot-issue for high-performance detection/reconnaissance. With this interest in the SAR technique, it generates various techniques according to the purposed platforms (Auto-SAR, ViSAR, InSAR, PolSAR, etc.). In this paper, we introduce high efficiency and useful techniques, which can be applied to various SAR applications for generating high-quality images in Ka-band. We propose stretched de-ramping hardware system applicable in Ka-band, and design the Range-Doppler algorithm which well matches the proposed radar system with the modified signal parameters. Thus, we can extend the integration time of the convolution process in slow-time and it leads to the improvement of the SAR image quality. To verify the performance of our proposed technique, we process the practical experiments at conventional outdoor fields. Furthermore, the proposed method is operated on Range Doppler Algorithm (RDA), which is mostly used for standard SAR.

Application of Multi-Temporal InSAR (MT-InSAR) for structural monitoring: the case study of Scrovegni Chapel in Padova

In the last years, the use of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique for structural health monitoring (SHM) of buildings and infrastructures has spread. Compared to traditional monitoring methods, MT-InSAR technique involves a number of advantages, e.g. perform back-analysis and extend the monitoring to the surrounding area, that makes it a very cost-effective tool. On the other hand, some drawbacks still have to be fully overcome, such as the issues related to the geocoding of the measurement points and the noisiness in the time series. In this article, the application of MT-InSAR monitoring to a case study is presented. The structure of interest is the Scrovegni Chapel in Padova, recently declared a world heritage site by UNESCO as it contains numerous frescos executed by Giotto. Using images acquired by COSMO-SkyMed constellation, the deformation of the area surrounding the Chapel was estimated, by means of spatial interpolation algorithms. In addition, time series referred to measurement points located on the rooftop were analyzed and compared to the environmental data (temperature and humidity) acquired by on-site physical sensors already installed.

Phase Cost Indicator-Guided Central Difference Information Filter 2-D Phase Unwrapping Approach

Phase unwrapping (PU) as an ill-posed inverse scientific problem has invariably been the significant difficulty in interferometric synthetic aperture radar (InSAR) technique for topographic mapping. Conventional PU approaches may produce a local or global unwrapping bias in severe phase changes caused by noise and large gradient terrain because the phase continuity assumption is not satisfied. To address this issue, we have created a novel phase quality-guided integrated filtering and unwrapping process for PU. In particular, the PU path is guided by an innovative phase cost indicator, which can automatically coordinate contributions from several phase quality estimation methods so that the unwrapping path can circumvent phase discontinuity regions and delay unwrapping these pixels. Since the central difference transform is another extra effective nonlinear transformation mode based on sigma points and only has one regulation parameter, we have employed the central difference information filter (CDIF) for the first time to realize the dynamic estimation of the unwrapped phase in the PU process. We tested the proposed approach using two real datasets with different mountainous areas. The results demonstrate that the PU accuracy of the proposed approach based on these two datasets is improved by 25.2% and 40.4% compared to the unscented information filtering (UIF) PU method, and the anti-noise capacity and unwrapping efficiency of the CDIF are both slightly superior to the UIF.

An Efficient and Fully Refined Deformation Extraction Method for Deriving Mining-Induced Subsidence by the Joint of Probability Integral Method and SBAS-InSAR

This study proposes a novel approach to derive the mining-induced goaf deformation, named the fully refined deformation extraction method (FRDEM). In this method, the Probability Integral Method (PIM) and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique are geographically integrated in a low-cost way to derive the fully refined goaf deformation and therefore to improve the detection ability for mining subsidence with fast deformation rate and large-gradient. To achieve this, we first established a functional relationship model to connect the 3-D parameters of PIM with SBAS-InSAR. Then, an improved genetic algorithm (IGA) was presented for parameter inversion, thus the optimal parameter set for PIM and the predicted displacements of the mining goaf was achieved economically. Afterward, we developed a geographically weighted data fusion model by presenting a data fusion strategy to eliminate the spatial heterogeneity in goaf boundary areas, and the fully refined goaf deformation field for the working face 2302 in the Guotun coal mining area was finally derived. Results demonstrate that FRDEM-derived displacements are highly consistent with field measurements, with RMSE decreasing to 0.053, 0.057, and 0.044 m respectively for the entire goaf, goaf center, and goaf boundary field, compared with those of ~0.092, ~0.095, and ~0.084 m for PIM and ~0.578, ~0.696, and ~0.046 m for SBAS-InSAR, respectively. This implies that the proposed FRDEM has significantly improved detection ability in deriving the mining-induced deformation, and thus can be a very promising tool to forecast and evaluate potential geohazards in the coal mining area.

Permafrost Dynamics Observatory (PDO): 2. Joint Retrieval of Permafrost Active Layer Thickness and Soil Moisture From L‐Band InSAR and P‐Band PolSAR

AbstractSeasonal subsidence induced by ground ice melt can be measured by interferometric synthetic aperture radar (InSAR) techniques to infer active layer thickness (ALT) in permafrost regions. The magnitude of subsidence depends on both how deep the soil thawed and how much ice/water content existed in the active layer soil. To provide the later, P‐band polarimetric synthetic aperture radar (PolSAR) backscatter is used due to its sensitivity to subsurface soil moisture and freeze/thaw conditions. In this study, which is the second in a two‐part series of Permafrost Dynamics Observatory (PDO), we exploit L‐band InSAR subsidence and P‐band PolSAR backscatter in a joint retrieval scheme to simultaneously estimate ALT and soil moisture profile of permafrost active layer. Both subsidence and backscatter are explicitly characterized by physics‐based models and share a common set of soil parameters including porosity and water saturation profiles. The PDO joint retrieval has been applied to the L‐ and P‐band SAR data acquired by National Aeronautics and Space Administration/Jet Propulsion Laboratory's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and western Canada during the 2017 Arctic‐Boreal Vulnerability Experiment (ABoVE) airborne campaign. This high‐resolution (30 m) regional estimates of ALT and soil moisture profile spanning over the ABoVE study domain can help link the ground‐based field surveys with satellite observations to further understand the permafrost and active layer soil process dynamics to disturbances and climate change occurring across the northern circumpolar region.

Application of Interferometric Synthetic Aperture Radar (InSAR) Technique in Monitoring the Deformation of Large Oil Storage Tanks

Application of Interferometric Synthetic Aperture Radar (InSAR) Technique in Monitoring the Deformation of Large Oil Storage Tanks

Spatial and Temporal Evolution of Ground Subsidence in the Beijing Plain Area Using Long Time Series Interferometry

Due to the overexploitation of water resources, ground subsidence is becoming increasingly problematic in Beijing, China's political, economic, and cultural capital. This article aims to investigate the relationship between ground subsidence and changes in groundwater depth, and water supply from a long-term point of view. Multisource synthetic aperture radar (SAR) data using the interferometric SAR (InSAR) technique were adopted in this research, combined with a set of leveling and ground subsidence data in the Beijing Plain area from 2003 to 2020. The InSAR results demonstrate that ground subsidence in the plain area increased steadily from 2003 to 2015, expanding from sporadic to continuous laminar dispersion and producing five major subsidence centers. The South-to-North Water Diversion Project (SNWDP) that was completed in 2008 and 2015 considerably reduced the demand for groundwater supply in the Beijing Plain area. Since then, the groundwater level depth has continued to increase. However, since 2016, the ground subsidence rate has dramatically slowed down. The obtained results showed that, thanks to the SNWDP, which resulted in a decline in groundwater exploitation and an increase in renewable water recycling, the ground subsidence in Beijing's plain area has been effectively managed.