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The 2023 M7.8 and M7.5 earthquake doublet near Kahramanmaraş, Turkey, provides insight regarding how large earthquakes rupture complex faults. Here we determine the faults geometry using surface ruptures and Synthetic Aperture Radar measurements, and the rupture kinematics from the joint inversion of high-rate Global Navigation Satellite System (GNSS), strong-motion waveforms, and GNSS static displacement. The M7.8 event initiated on a splay fault and subsequently propagated along the main East Anatolian Fault with an average rupture velocity between 3.0 and 4.0 km/s. In contrast, the M7.5 event demonstrated a bilateral supershear rupture of about 5.0–6.0 km/s over an 80 km length. Despite varying strike and dip angles, the sub-faults involved in the mainshock are nearly optimally oriented relative to the local stress tensor. The second event ruptured a fault misaligned with respect to the regional stress, also hinting at the effect of local stress heterogeneity in addition to a possible free surface effect. The February 2023 Kahramanmaraş earthquake doublet reveals how pre-stress and local stress heterogeneity influence fault ruptures, with key insights into rupture kinematics, fault geometry, and stress interactions along complex fault systems. [ABSTRACT FROM AUTHOR]

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional  ω K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the  G r o u p - N s t r e a m  processing model, and the  F t h r e a d - G r o u p - N s t r e a m  processing model. The  F t h r e a d - G r o u p - N s t r e a m  processing model utilizes  F t h r e a d ,  G r o u p , and  N s t r e a m  for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm's performance in dynamic parallelism and alleviating the issue of serial execution within the  G r o u p - N s t r e a m  processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a method to invert the sea surface wind field from Sentinel‐1 synthetic aperture radar (SAR) interferometric wide swath (IW) band mode ground range detected (GRD) vertical polarization (VV) data based on the deep residual network (ResNet). Compared with the traditional algorithm (i.e., using geophysical model functions (GMFs)), the wind field inversion based on the ResNet deep learning method solves the problem of separate wind direction and wind speed inversion. The ResNet model is developed based on the results of 212 VV polarized IW images and cross calibrated multiplatform (CCMP) data collected in 2021 in the offshore waters of China. The root mean square error, standard deviation, and bias of the inverse wind direction of the ResNet model are 12.5°, 9.8°, and 7.4°, respectively, and the root mean square error, standard deviation, and bias of the inverse wind speed are 2.33, 1.495, and −1.375. The ResNet is better than the conventional local gradient and C‐band model models, further confirming the advantages shown by the deep learning algorithm in nonlinear fitting and its potential application in sea surface wind field inversion. [ABSTRACT FROM AUTHOR]

In SAR (Synthetic Aperture Radar) image ship detection tasks, the accuracy of detection is hindered by the presence of a significant amount of clutter and false targets. Ships in these images exhibit diverse scales, which poses a challenge for traditional models to capture the necessary features across these varying scales. Furthermore, the limited texture information available in SAR images complicates the task of distinguishing between targets and the background, leading to suboptimal performance when using conventional deep learning models. To address these challenges, this paper introduces several innovative components. Firstly, a novel dynamic convolution operator is proposed, which allows for adaptively adjusting the smoothing method to mitigate the impact of noise in the data. Additionally, the paper introduces a plug-and-play feature smoothing module as well as a visual module. The feature smoothing module contributes to reducing noise interference, enhancing the overall robustness of the model. Meanwhile, the visual module focuses on augmenting long-range dependency relationships within the data, facilitating a better understanding of the complex interplay between ships and the background. This effectively addresses the issue of texture scarcity in SAR images. Our method was compared with the most advanced Visual module Explicit Visual Center (EVC) using the YOLO7-obb object detection baseline. The experimental results unequivocally demonstrate that our proposed method achieved a notable 1.2% improvement in the Average Precision (AP) value. [ABSTRACT FROM AUTHOR]

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270 km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property. The main advantage of opting for the non-linear persistent scatterer interferometric SAR (PSInSAR) approach for this study is that it exposes minute movements without any prior consideration of conventional monitoring techniques, making it valid in continuously varying regions. An average vertical displacement range of − 170 to + 82 mm per year was found, which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience an annual subsidence of 170 mm, and the less populated western region experiences an uplift of 82 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. Groundwater table-depleting regions experienced high levels of land subsidence, and tectonic activities controlled vertical displacement in the region. Major variation was detected after an earthquake occurred along fault lines. This study was designed because a non-linear approach is required to address ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively. [ABSTRACT FROM AUTHOR]

Tropical forests are currently under pressure from increasing threats. These threats are mostly related to human activities. Earth observations (EO) are increasingly used for monitoring forest cover, especially synthetic aperture radar (SAR), that is less affected than optical sensors by atmospheric conditions. Since the launch of the Sentinel-1 satellites, numerous methods for forest disturbance monitoring have been developed, including near realtime (NRT) operational algorithms as systems providing early warnings on deforestation. These systems include Radar for Detecting Deforestation (RADD), Global Land Analysis and Discovery (GLAD), Real Time Deforestation Detection System (DETER), and Jica-Jaxa Forest Early Warning System (JJ-FAST). These algorithms provide online disturbance maps and are applied at continental/global scales with a Minimum Mapping Unit (MMU) ranging from 0.1 ha to 6.25 ha. For local operators, these algorithms are hard to customize to meet users' specific needs. Recently, the Cumulative sum change detection (CuSum) method has been developed for the monitoring of forest disturbances from long time series of Sentinel-1 images. Here, we present the development of a NRT version of CuSum with a MMU of 0.03 ha. The values of the different parameters of this NRT CuSum algorithm were determined to optimize the detection of changes using the F1-score. In the best configuration, 68% precision, 72% recall, 93% accuracy and 0.71 F1-score were obtained. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean region, a vital conduit for global maritime trade, faces significant environmental challenges due to marine pollution, particularly from oil spills. This is the first study covering the long period of comprehensive monitoring of oil pollution using the full mission of Sentinel-1 Synthetic Aperture Radar (SAR) data in the Mediterranean Sea, so this research aims to detect and analyze comprehensively the occurrence of oil spills in the Eastern Mediterranean over a decade (2014–2023). This study focuses on identifying geographical distribution patterns, proximity to shorelines, frequency across maritime zones, and potential sources of these spills, especially around major ports and maritime routes. This study utilizes SAR data from the Sentinel-1 satellite. The methodology included automated detection algorithms within the Sentinel application platform (SNAP) and integration with GIS mapping to study oil spill patterns and characteristics. Over 1000 Sentinel-1 scenes were investigated in the northern Mediterranean waters off the coast of Egypt, to detect and analyze 355 oil spill events with a total impacted area of more than 6000 km2. The analysis of temporal spill distribution reveals significant fluctuations from year to year. Within the entire timeline of the study, 2017 had the largest spatial areas covering one thousand square kilometers. In contrast, the single largest spill recorded during the study period occurred in 2020, covering 198.73 square kilometers. The results identified a non-uniform distribution of oil spills and primarily exhibiting elongated patterns aligned with the navigation routes. The distinct increase of oil spill incidents was within the Exclusive Economic Zone (EEZ), obviously drifted to the coastline and around major ports. The study emphasizes the critical role of remote sensing technologies in addressing environmental challenges caused by the maritime transport sector, advocating for enhanced monitoring and regulatory enforcement to protect marine ecosystems and support sustainable naval activities. The findings highlight the urgent need for targeted continuous monitoring and rapid response strategies in high-traffic maritime areas, particularly around the EEZ and major ports. [ABSTRACT FROM AUTHOR]

New satellite geodetic data challenge our knowledge of the deformation mechanisms driving the active deformations affecting southeastern Sicily. The PS-InSAR (Permanent Scatterer Interferometry Synthetic Aperture Radar) measurements evidence a generalized subsidence and an eastward tilting of the Hyblean Plateau, combined with a local relative uplift along its eastern coast. To find a mechanical explanation for the present-day strain field, we investigate short- and large-scale surface-to-crustal deformation processes. Geological and geophysical data suggest that the southward migration of the Calabrian subduction could be the causative geodynamic process. We evaluate this hypothesis using flexural modeling and show that the combined downward pull force, induced by the Ionian slab roll-back and the overloading of the Calabrian accretionary prism, is strong enough to flex the adjacent Hyblean continental domain, explaining the measured large-scale subsidence and eastward bending of the Hyblean Plateau. To explain the short-scale relative uplift evidenced along the eastern coast, we perform elastic modeling on identified or inferred onshore and offshore normal faults. We also investigate the potential effects of other deformation processes, including upwelling mantle flow, volcanic deflation, and hydrologic loading. Our results enable us to propose an original seismic cycle model for southeastern Sicily, linking the current interseismic strain field with available long-term deformation data. This model is mainly driven by the southward migration of the Ionian slab roll-back which induces a downward force capable of flexuring the Hyblean crust. [ABSTRACT FROM AUTHOR]

Continental-scale, open-access datasets of ground surface displacement in all countries of the European Union, plus Norway, United Kingdom, and Iceland, are now available from the European Ground Motion Service (EGMS). Under the European Union's Copernicus program, the EGMS has been available since the end of 2022 and will continue for the foreseeable future. The EGMS data are presently derived from Interferometric Synthetic Aperture Radar (InSAR) processing of the Sentinel-1 SAR satellite imagery, which has been collected from 2015 to date. While EGMS data can be visualised and obtained through an online platform (EGMS Explorer), the data access arrangements are inefficient for large-scale analysis of ground surface displacements due to the volume of data, the tile-formatting of datasets and some server limitations. Here we present a Python-based toolkit, named EGMS-toolkit, to provide a unified and more efficient workflow for accessing EGMS datasets. The toolkit can automatically detect and download EGMS datasets based on a Region of Interest provided by users, then it can merge, clip, and crop the results to that region regardless of its scale. The toolkit then produces files of EGMS ground surface motions in GIS-ready formats for further analysis. [ABSTRACT FROM AUTHOR]

Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given ongoing climate change impacts. Synthetic aperture radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow, and SAR interferometry (InSAR) can be used to estimate changes in SWE (Δ SWE) between SAR acquisitions. We calculated Δ SWE retrievals from 10 NASA L-band (1–2 GHz, ∼25 cm wavelength) uninhabited aerial vehicle SAR (UAVSAR) acquisitions covering a ∼640 km 2 swath in northern Colorado during the winters of 2020 and 2021. UAVSAR acquisitions coincided with ∼117 mm of accumulation in 2020 and ∼282 mm of accumulation in 2021. Δ SWE retrievals were evaluated against measurements of SWE from repeat ground-penetrating radar (GPR) and terrestrial lidar scans (TLSs) collected during the NASA SnowEx time series campaigns at two field sites (total area =∼0.2 km 2) as well as SWE measurements from seven automated stations distributed throughout the UAVSAR swath. For single InSAR pairs, UAVSAR Δ SWE retrievals yielded an overall r of 0.72–0.79 and an RMSE of 19–22 mm when compared with TLS and GPR Δ SWE retrievals. UAVSAR Δ SWE showed some scatter with Δ SWE measured at automated stations for both study years, but cumulative UAVSAR SWE yielded a r of 0.92 and an RMSE of 42 mm when compared to total SWE measured by the stations. Further, UAVSAR Δ SWE RMSEs differed by <10 mm for coherences (i.e., the complex interferometric coherence) of 0.10 to 0.90, suggesting that coherence has only a small influence on the Δ SWE retrieval accuracy. Given the evaluations presented here and in other recent studies, the upcoming NASA-ISRO SAR (NISAR) satellite mission, with a 12 d revisit period, offers an exciting opportunity to apply this methodology globally. [ABSTRACT FROM AUTHOR]

Active-layer thickness (ALT) is estimated for a study area in northern Alaska's continuous-permafrost zone using satellite data from Sentinel-1 (radar) and ICESat-2 (lidar) for the period 2017 to 2022. Synthetic aperture radar (SAR) interferograms were generated using the Short Baseline Subset (SBAS) approach. Displacement time series over the thaw season (June–September) are fit well with a linear model (root mean square error (RMSE) scatter is less than 7 mm) and show maximum seasonal subsidence of 20–60 mm. ICESat-2 products were used to validate the interferometric synthetic aperture radar (InSAR) displacement time series. ALT was estimated from measured subsidence using a widely used model exploiting the volume difference between ice and water, reaching a maximum depth in our study area of 1.5 m. Estimated ALT is in good agreement with in situ and other remotely sensed data but is sensitive to assumed thaw season onset, indicating the need for reliable surface temperature data. Our results suggest the feasibility of long-term permafrost monitoring with satellite InSAR. However, the C-band (∼55 mm center wavelength) Sentinel radar is sensitive to vegetation cover and, in our studies, was not successful for similar monitoring in the heavily treed discontinuous-permafrost zone of central Alaska. [ABSTRACT FROM AUTHOR]

Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants' submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results. [ABSTRACT FROM AUTHOR]

Deep learning techniques have made significant advancements in computer vision. The YOLO algorithm, a representative single-stage detection approach, has demonstrated remarkable results in detecting ship targets in SAR images. We introduce an enhanced ship target detection model for SAR images, utilizing the improved YOLOv7 object detection network. We incorporate the coordinate attention mechanism into the network to enable automatic detection and diagnosis of ship targets within SAR images. To enhance the robustness and positioning accuracy of the detection network, we replace the CIoU regression loss in YOLOv7 with the SIoU loss, reducing the complexity of the loss function. Additionally, we integrate the rotating target detection technology into the network to mitigate the impact of target overlap on detection results. Comprehensive experiments conducted on the Capella Space synthetic aperture radar datasets validate that the proposed methodology achieves superior performance in multiple evaluation metrics, including precision, recall, and mean average precision. [ABSTRACT FROM AUTHOR]

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone. [ABSTRACT FROM AUTHOR]

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. [ABSTRACT FROM AUTHOR]

There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye. [ABSTRACT FROM AUTHOR]

Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency's (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality. In this case, InSAR data enabled prompt identification of the phenomenon, supporting the authorities in charge of environmental management to thoroughly investigate the situation. First, an on-site validation was performed via field surveys confirming the presence of cracks and fissures on some edifices. Further analysis, including water pumping rates, settlement gauge and topographic levelling, corroborated the InSAR data's findings regarding vertical deformation. Integration of collected data allowed for spatial identification and assessment of the subsidence bowl and its source depth recognized by the remote sensing data. The Montemurlo case offers a procedural guideline for managing abrupt accelerations, identified by InSAR data in subsidence-prone areas due to fluid overexploitation. In fact, these data proved useful in helping local authorities responsible for hydrogeomorphological risk management. With the exacerbation of deformation issues in subsidence-prone regions due to climate change, early detection and monitoring of such phenomena are increasingly crucial, with InSAR data playing a central role in achieving this goal. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979. [ABSTRACT FROM AUTHOR]

Beijing is a city on the North China Plain with severe land subsidence. In recent years, Beijing has implemented effective measures to control land subsidence. Since this implementation, the development of time-series land subsidence in Beijing has slowed and has shown nonlinearity. Most previous studies have focused on the linear evolution of land subsidence; the nonlinear evolutionary patterns of land subsidence require further discussion. Therefore, we aimed to identify the evolution of land subsidence in Beijing, based on Envisat ASAR and Radarsat-2 images from 2003 to 2020, using permanent scatterer interferometric synthetic aperture radar (PS-InSAR) and cubic curve polynomial fitting methods. The dates of the extreme and inflection points were identified from the polynomial coefficients. From 2003 to 2020, the subsidence rate reached 138.55 mm/year, and the area with a subsidence rate > 15 mm/year reached 1688.81 km2. The cubic polynomials fit the time-series deformation well, with R2 ranging from 0.86 to 0.99 and the RMSE ranging from 1.97 to 60.28 mm. Furthermore, the subsidence rate at 96.64% of permanent scatterer (PS) points first increased and then decreased. The subsidence rate at 86.58% of the PS points began to decrease from 2010 to 2015; whereas the subsidence rate at 30.51% of the PS point reached a maximum between 2015 and 2019 and then decreased. The cumulative settlement continued to increase at 69.49% of the PS points. These findings imply that groundwater levels are highly correlated with the temporal evolution of subsidence in areas with pattern D (Vs+-, S+), with increasing and then decelerating rates and increasing amounts. In regions with a thickness of compressible clay layer over 210 m, subsidence follows pattern E (Vs+, S+), with increasing rates and amounts. Fractures such as the Gaoliying and Sunhe fractures significantly influence the spatial distribution of subsidence patterns, showing distinct differences on either side. Near the Global Resort Station, pattern E (Vs+, S+) intensifies in subsidence, potentially due to factors like land use changes and construction activities. [ABSTRACT FROM AUTHOR]

The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral imaging (MSI) and synthetic aperture radar imaging (SAR) to overcome the limitations of single-modal images. We propose a cross-modal multidimensional frequency perception network (CMFPNet) to enhance classification and extraction accuracy. CMFPNet includes a local–global perception block (LGPB) for combining local and global semantic information and a multidimensional adaptive frequency filtering attention block (MAFFAB) that dynamically filters frequency-domain information that is beneficial for aquaculture area recognition. We constructed six typical offshore aquaculture datasets and compared CMFPNet with other models. The quantitative results showed that CMFPNet outperformed the existing methods in terms of classifying and extracting floating raft aquaculture (FRA) and cage aquaculture (CA), achieving mean intersection over union (mIoU), mean F1 score (mF1), and mean Kappa coefficient (mKappa) values of 87.66%, 93.41%, and 92.59%, respectively. Moreover, CMFPNet has low model complexity and successfully achieves a good balance between performance and the number of required parameters. Qualitative results indicate significant reductions in missed detections, false detections, and adhesion phenomena. Overall, CMFPNet demonstrates great potential for accurately extracting large-scale offshore aquaculture areas, providing effective data support for marine planning and environmental protection. Our code is available at Data Availability Statement section. [ABSTRACT FROM AUTHOR]

This study aimed to map and analyze the spatial pattern of the surface deformation associated with the 2024 Noto Peninsula earthquake (Mw7.5) using structure-from-motion/multi-view-stereo (SfM–MVS), an advanced photogrammetric technique. The analysis was conducted using digital aerial photographs with a ground pixel dimension of 0.2 m (captured the day after the earthquake). Horizontal locations of GCPs were determined using pre-earthquake data to remove the wide-area horizontal crustal deformation component. The elevations of the GCPs were corrected by incorporating quasi-vertical values derived from a 2.5-dimensional analysis of synthetic aperture radar (SAR) results. In the synclinorium structure area, where no active fault had previously been identified, we observed a 5 km long uplift zone (0.1 to 0.2 km in width), along with multiple scarps that reached a maximum height of 2.2 m. The area and shape of the surface deformation suggested that the induced uplift and surrounding landslides were related to fold structures and their growth. Thus, our study shows the efficacy of SfM–MVS with respect to accurately mapping earthquake-induced deformations, providing crucial data for understanding seismic activity and informing disaster-response strategies. [ABSTRACT FROM AUTHOR]

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images. [ABSTRACT FROM AUTHOR]

Land cover classification of Synthetic Aperture Radar (SAR) imagery is a significant research direction in SAR image interpretation. However, due to the unique imaging methodology of SAR, interpreting SAR images presents numerous challenges, and land cover classification using SAR imagery often lacks innovative features. Distributed scatterers interferometric synthetic aperture radar (DS-InSAR), a common technique for deformation extraction, generates several intermediate parameters during its processing, which have a close relationship with land features. Therefore, this paper utilizes the coherence matrix, the number of statistically homogeneous pixels (SHPs), and ensemble coherence, which are involved in DS-InSAR as classification features, combined with the backscatter intensity of multi-temporal SAR imagery, to explore the impact of these features on the discernibility of land objects in SAR images. The results indicate that the adopted features improve the accuracy of land cover classification. SHPs and ensemble coherence demonstrate significant importance in distinguishing land features, proving that these proposed features can serve as new attributes for land cover classification in SAR imagery. [ABSTRACT FROM AUTHOR]

The goal of this study was to develop a model describing the relationship between the ground-displacement-caused tremors induced by underground mining, and mining and geological factors using the Random Forest Regression machine learning method. The Rudna mine (Poland) was selected as the research area, which is one of the largest deep copper ore mines in the world. The SAR Interferometry methods, Differential Interferometric Synthetic Aperture Radar (DInSAR) and Small Baseline Subset (SBAS), were used in the first case to detect line-of-sight (LOS) displacements, and in the second case to detect cumulative LOS displacements caused by mining tremors. The best-prediction LOS displacement model was characterized by R2 = 0.93 and RMSE = 5 mm, which proved the high effectiveness and a high degree of explanation of the variation of the dependent variable. The identified statistically significant driving variables included duration of exploitation, the area of the exploitation field, energy, goaf area, and the average depth of field exploitation. The results of the research indicate the great potential of the proposed solutions due to the availability of data (found in the resources of each mine), and the effectiveness of the methods used. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) has attracted a lot of interest, recently, for its excellent performance in civilian and military applications. However, in CSAR imaging, the result is to be defocused when the height of an object deviates from a reference height. Existing approaches for this problem rely on digital elevation models (DEMs) for error compensation. It is difficult and costly to collect DEM using specific equipment, while the inversion of DEM based on echo is computationally intensive, and the accuracy of results is unsatisfactory. Inspired by multi-focus image fusion in optical images, a spatial-domain fusion method is proposed based on the sum of modified Laplacian (SML) and guided filter. After obtaining CSAR images in a stack of different reference heights, an all-in-focus image can be computed by the proposed method. First, the SMLs of all source images are calculated. Second, take the rule of selecting the maximum value of SML pixel by pixel to acquire initial decision maps. Secondly, a guided filter is utilized to correct the initial decision maps. Finally, fuse the source images and decision maps to obtain the result. A comparative experiment has been processed to verify the exceptional performance of the proposed method. The final processing result of real-measured CSAR data demonstrated that the proposed method is effective and practical. [ABSTRACT FROM AUTHOR]

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the "salt and pepper" effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the "salt and pepper" spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. [ABSTRACT FROM AUTHOR]

Soil freeze-thaw (FT) cycles over agricultural lands are of great importance due to their vital role in controlling soil moisture distribution, nutrient availability, health of microbial communities, and water partitioning during flood events. Active microwave sensors such as C-band Sentinel-1 synthetic aperture radar (SAR) can serve as powerful tools to detect field-scale soil FT state. Using Sentinel-1 SAR observations, this study compares the performance of two FT detection approaches, a commonly used seasonal threshold approach (STA) and a computationally inexpensive general threshold approach (GTA) at an agricultural field in New Hampshire, US. It also explores the applicability of an interferometric coherence approach (ICA) for FT detection. STA and GTA achieved 85% and 78% accuracy, respectively, using VH polarization. We find a marginal degradation in the performance of STA (82%) and GTA (76%) when employing VV-polarized data. While there was approximately a 6 percentage point difference between STA's and GTA's overall accuracy, we recommend GTA for FT detection using SAR images at sub-field-scale over extended regions because of its higher computational efficiency. Our analysis shows that interferometric coherence is not suitable for detecting FT transitions under mild and highly dynamic winter conditions. We hypothesize that the relatively mild winter conditions and therefore the subtle FT transitions are not able to significantly reduce the correlation between the phase values. Also, the ephemeral nature of snowpack in our study area, further compounded by frequent rainfall, could cause decorrelation of SAR images even in the absence of a FT transition. We conclude that despite Sentinel-1's ~80% mapping accuracy at a mid-latitude site, understanding the cause of misclassification remains challenging, even when detailed ground data are readily available and employed in error attribution efforts. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which requires a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in deep learning-based SAR ship detectors struggling due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed a Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher–student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and a comprehensive diversity of learning information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Affected by geological factors, the scale of surface deformation in a hilly semi-desertification mining area varies. Meanwhile, there is certain dense vegetation on the ground, so it is difficult to construct a high-precision and complete image of a subsidence basin by using a single monitoring method, and hence the laws of the deformation and inversion of mining parameters cannot be known. Therefore, we firstly propose conducting collaborative monitoring by using InSAR (Interferometric Synthetic Aperture Radar), UAV (unmanned aerial vehicle), and 3DTLS (three-dimensional terrestrial laser scanning). The time-series complete surface subsidence basin is constructed by fusing heterogeneous data. In this paper, SBAS-InSAR (Small Baseline Subset) technology, which has the characteristics of reducing the time and space discorrelation, is used to obtain the small-scale deformation of the subsidence basin, oblique photogrammetry and 3D-TLS with strong penetrating power are used to obtain the anomaly and large-scale deformation, and the local polynomial interpolation based on the weight of heterogeneous data is used to construct a complete and high-precision subsidence basin. Compared with GNSS (Global Navigation Satellite System) monitoring data, the mean square errors of 1.442 m, 0.090 m, 0.072 m are obtained. The root mean square error of the high-precision image of the subsidence basin data is 0.040 m, accounting for 1.4% of the maximum subsidence value. The high-precision image of complete subsidence basin data can provide reliable support for the study of surface subsidence law and mining parameter inversion. [ABSTRACT FROM AUTHOR]

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally. [ABSTRACT FROM AUTHOR]

Holographic synthetic aperture radar (HoloSAR) introduces a cutting-edge three-dimensional (3-D) imaging mode to the field of synthetic aperture radar (SAR), enriching the scattering information of targets by observing them across multiple spatial dimensions. However, independent phase errors among baselines, such as those caused by platform jitter and measurement inaccuracies, pose significant challenges to imaging quality. The phase gradient autofocus (PGA) method effectively estimates phase errors, but struggles to accurately estimate the linear component, causing vertical shift in HoloSAR subaperture imaging result. Therefore, this paper proposes a PGA-based phase error compensation method for HoloSAR to address the vertical shift issue caused by linear phase errors. This method can achieve phase error correction in both the echo domain and image domain with enhanced efficiency. Experimental results of simulated targets and real data from the GOTCHA system demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. [ABSTRACT FROM AUTHOR]

Estimating landslide dynamics is vital for the prevention of landslide disasters. Differential interferometric synthetic aperture radar (DInSAR) based on L-band SAR satellites is an effective tool for estimating the dynamics of forested landslides that occur in Japan. High-temporal L-band SAR observations have been planned for the future. Thus, it is necessary to further investigate the specific advantages of high-temporal L-band SAR observations for estimating landslide dynamics. In this study, we used DInSAR data with different time windows to identify active landslides in Hokkaido, Japan. This study is the first attempt to demonstrate the advantages of high-temporal L-band SAR observations for estimating active landslide dynamics. We successfully observed the dynamics of two active landslides, Kounai-1 and Kounai-2, using DInSAR over a time window of 14 days. We present the first spatial observation of the dynamics of Kounai-1 and Kounai-2. In addition, we discuss the dynamics of Kounai-1 and Kounai-2 based on interferograms, and our results suggest that both landslides are subunits of the same landslide, called the Kounai landslide. These results indicate that high-temporal L-band SAR observations can mitigate cycle slips and enable the estimation of active landslide dynamics. [ABSTRACT FROM AUTHOR]

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS's blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. [ABSTRACT FROM AUTHOR]

The advanced interferometric synthetic aperture radar (InSAR) provides an effective tool to detect landslides over a large area. However, it is greatly affected by atmospheric delays and phase unwrapping errors in a complex environment and requires massive calculations and analysis. These factors hinder InSAR from reliably and rapidly identifying landslides. In this study, we propose an improved phase gradient stacking (IPGS) method, which effectively suppresses atmospheric delay disturbance, topographic residuals, and noise while enhancing local deformation signals. The temporally stacked phase gradients with a preset step along four directions are merged to form a phase gradient map. It avoids complicated unwrapping and massive time series analysis. The simulation experiment demonstrates the improvement to traditional methods by combining four directions and a specific step. The IPGS method achieves a comparative landslide detection as the classical SBAS method in terms of Sentinel-1 datasets covering Danba County. Even for some small-scale landslides that are difficult for SBAS to detect, the phase gradients are distinct. A field investigation validates the reliability of IPGS-detected landslides. It provides an effective tool for large-scale, rapid, and reliable detection of geological disasters. [ABSTRACT FROM AUTHOR]

Objectives: Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent. Methods: In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed. Results: The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach −134 mm/a and −697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach −135 mm/a and −734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy. Conclusions: The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent. [ABSTRACT FROM AUTHOR]

Objectives: Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics. Methods: Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods. Results: It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point. Conclusions: Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar [ABSTRACT FROM AUTHOR]

Objectives: On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local resident's lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control engineering design, monitoring and early warning. Methods: First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characteristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR). Results: Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall. Conclusions: Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the land‐ slide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing onsite real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized [ABSTRACT FROM AUTHOR]

Objectives: The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage. Methods: This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends. Results: The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide. Conclusions: A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity. [ABSTRACT FROM AUTHOR]

Objectives: The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region. Methods: The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.Results: The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be − 62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results. Conclusions: The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBASInSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region. [ABSTRACT FROM AUTHOR]

Detecting crustal deformation during transient deformation events at offshore subduction zones remains challenging. The spatiotemporal evolution of slow slip events (SSEs) on the offshore Hikurangi subduction zone, New Zealand, during February–July 2019, is revealed through a time‐dependent inversion of onshore and offshore geodetic data that also accounts for spatially varying elastic crustal properties. Our model is constrained by seafloor pressure time series (as a proxy for vertical seafloor deformation), onshore continuous Global Navigation Satellite System (GNSS) data, and Interferometric Synthetic Aperture Radar displacements. Large GNSS displacements onshore and uplift of the seafloor (10–33 mm) require peak slip during the event of 150 to >200 mm at 6–12 km depth offshore Hawkes Bay and Gisborne, comparable to maximum slip observed during previous seafloor pressure deployments at north Hikurangi. The onshore and offshore data reveal a complex evolution of the SSE, over a period of months. Seafloor pressure data indicates the slow slip may have persisted longer near the trench than suggested by onshore GNSS stations in both the Gisborne and Hawkes Bay regions. Seafloor pressure data also reveal up‐dip migration of SSE slip beneath Hawke Bay occurred over a period of a few weeks. The SSE source region appears to coincide with locations of the March 1947 Mw 7.0–7.1 tsunami earthquake offshore Gisborne and estimated great earthquake rupture sources from paleoseismic investigations offshore Hawkes Bay, suggesting that the shallow megathrust at north and central Hikurangi is capable of both seismic and aseismic rupture. Plain Language Summary: Subduction zones, where one tectonic plate dives beneath another, generate the planet's largest earthquakes. They also host an important mode of fault slip called "slow slip events (SSEs)," which are essentially earthquakes in slow motion. The Hikurangi subduction zone, where the Pacific Plate subducts beneath New Zealand hosts large and frequent SSEs near the trench, where the plate boundary emerges at the seabed, requiring seafloor instrumentation to investigate them. Seafloor pressure measurements can track centimeter‐level up or down movement of the seafloor during slow slip, and reveal offshore displacement during a large 2019 SSE at the Hikurangi subduction zone. The 2019 event involved substantial migration, beginning at ∼15 km depth, and expanding to the trench over a period of several weeks. We also show that the same areas which have ruptured in previous seismic earthquakes (that involved faster slip) can also rupture slowly, in SSEs. This raises the possibility that regions where we currently observe SSEs could also produce seismic events. This result also demands that more work must be done to understand the physical processes that enable the same part of a fault to rupture both fast and slow at different times. Key Points: Central Hikurangi slow slip events (SSEs) propagate up‐dip over a period of weeks to monthsSeafloor geodetic data reveal that shallow SSEs may last longer than onshore Global Navigation Satellite System data suggestThe same portions of a shallow megathrust can host both large seismic and aseismic rupture [ABSTRACT FROM AUTHOR]

SUMMARY: The prominent Pamir plateau holds considerable significance in comprehending the processes of Asian continental collisional orogeny. However, due to harsh natural conditions and low seismic activity within the Pamir hinterland, our understanding of this region remains deficient. Recent major events and the accumulation of geodetic observations present a rare opportunity for us to get insights into the tectonic activities and orogenic processes occurring in this region. First, employing Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Synthetic Aperture Radar (SAR) images, we acquire coseismic displacements associated with the most recent earthquakes in 2015 and 2023. Subsequently, we conduct the source models inversion with the constraints of surface displacements based on a finite-fault model. Our results reveal displacements ranging from −0.8 to 0.8 m for the 2015 Mw 7.2 Tajik earthquake and −0.25 to 0.25 m for the 2023 Mw 6.9 Murghob event, respectively. The optimal three-segment model for the 2015 event ruptured a fault length of 89 km with a surface rupture extending 59 km along the Sarez–Karakul fault (SKF), characterized predominantly by left-lateral strike-slip motion, with a maximum slip of 3.5 m. Meanwhile, our preferred uniform slip model suggests that the 2023 event ruptured an unmapped fault in the southern Pamir region with a strike angle of 31° and a dip angle of 76.8°. The distributed slip model indicates that the 2023 event ruptured a fault length of 32 km, resulting in an 8 km surface rupture. This event is characterized by left-lateral strike slip, with a peak slip of 2.2 m. Secondly, the Coulomb stress calculations demonstrate that the 2023 event was impeded by the 2015 event. Finally, interseismic Global Positioning System data revel a relative motion of 3.4–5.7 mm yr−1 in the N-S component and 3.2–3.8 mm yr−1 in the E-W component along the SKF in the Pamir hinterland, respectively. These N-S direction strike-slip activities and slip behaviours support an ongoing strong shear and extension in the Pamir regime, which is a response to the oblique convergence between the Indian and Eurasian plates. [ABSTRACT FROM AUTHOR]

SUMMARY: The 2022 Har Lake earthquake sequence, which began in 2022 January and lasted for ∼70 d, jolted the Har Lake area, which is located in the western Qilian Shan, northeastern Tibetan Plateau. Two Mw > 5.5 earthquakes occurred during the earthquake sequence, among which the March 25 Mw 5.8 event is considered the largest event recorded in the area. However, determining the seismogenic faults of the earthquake sequence, as well as the detailed rupture features, is difficult due to the lack of geological data and near-field seismological observations. In this study, we use Sentinel-1 synthetic aperture radar (SAR) data to obtain the coseismic deformation field, identify possible ruptured faults and associated fault geometries, and further estimate detailed coseismic slip models of the two Mw > 5.5 earthquakes. The results show that the January 23 Mw 5.6 earthquake (Earthquake A) occurred on a N15° W-trending dextral-slip fault with a dip angle of ∼61°. For the March 25 Mw 5.8 earthquake (Earthquake B), the interferometric synthetic aperture radar (InSAR) data can be described by either an ∼N–S-trending dextral-slip fault or an ∼E–W-trending sinistral-slip fault. The ∼N–S-trending fault better describes the aftershock distribution, while the ∼E–W-trending model is more consistent with the regional geological setting. We suggest that the complex coseismic ruptures in the multiple-fault system are driven by widespread NE–SW-trending compression in the western Qilian Shan. This study demonstrates the importance of integrating geodetic and seismological observations to capture the full complexity of moderate earthquakes and further suggests potential seismic hazards in the Har Lake area. [ABSTRACT FROM AUTHOR]

Traditional methods for monitoring the foundation settlement of airport runways predominantly employ equipment such as leveling instruments, total stations, layered settlement instruments, magnetic ring settlement instruments, ground-penetrating radar (GPR), and synthetic aperture radar. These methods suffer from low automation levels, are time-consuming, labor-intensive, and can significantly disrupt airport operations. An alternative electromagnetic detection technique, Controlled Source Audio-Frequency Magnetotellurics (CSAMT), offers deep-depth detection capabilities. However, CSAMT faces significant challenges, particularly in generating high signal-to-noise ratio (SNR) signals in the far-field region (FfR). Traditional CSAMT utilizes grounded horizontal dipoles (GHDs), which radiate symmetric beams. Due to the low directivity of GHDs, only a small fraction of the radiated energy is effectively utilized in FfR observations. Enhancing the SNR in FfR typically requires either reducing the transceiving distance or increasing the transmitting power, both of which introduce substantial complications. This paper proposes an airborne-dangled monopole-antenna symmetric remote-sensing radiation source for airport runway monitoring, which replaces the conventional GHD. The analytical, simulation, and experimental verification results indicate that the energy required by the airborne-dangled symmetric source to generate the same electric field amplitude in the FfR is only one-third of that needed by traditional CSAMT. This results in significant energy savings and reduced emissions, underscoring the advantages of the airborne-dangled monopole-antenna symmetric source in enhancing energy efficiency for CSAMT. The theoretical analysis, simulations, and experimental results consistently verify the validity and efficacy of the proposed airborne-dangled monopole-antenna symmetric remote-sensing radiation source in CSAMT. This innovative approach holds substantial promise for airport runway monitoring, offering a more efficient and less intrusive solution compared to traditional methods. [ABSTRACT FROM AUTHOR]

Image restoration is an inevitable pre-processing step in most satellite imaging applications. The satellite imaging modality such as Synthetic Aperture Radar (SAR) is prone to speckle distortions due to constructive and destructive interference of the probing signals. Speckles being data correlated and multiplicative, their reduction is not so trivial. Since speckles are not purely noise interventions, a blind reduction process leads to spurious analysis at the later stages. Moreover, the image details are liable to get compromised during such a noise reduction process. An attention-based deep image prior (DIP) model with U-Net architecture has been proposed in this work to carefully address these setbacks. The attention block is used to scale the features extracted from the encoder, and they are concatenated with the features from the decoder to obtain both low- and high-level features. The attention module incorporated in the model helps to extract significant complex structures in SAR images. Further, the DIP model duly respects the noise distribution of speckles while performing the despeckling task. Various synthetic, natural, aerial, and satellite images are subjected to the testing and verification process, and the results obtained are in favor of the proposed model. The quantitative analysis carried out using various statistical metrics in this study also reveals the restoration ability of the proposed method in terms of both despeckling and structure preservation. [ABSTRACT FROM AUTHOR]