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We searched the Milky Way Plane along a 6-deg swath for pulses of monochromatic light as faint as 15th mag (V band) using a wide-field telescope equipped with a prism. Pulses with duration less than 1 s that occur more often than once every 10 min would be detected, and pulses arriving less frequently would be detected with proportionally lower probability. A 'difference-image' algorithm revealed 36 monochromatic sources. Subsequent assessment showed all were simply astrophysical objects emitting known atomic emission lines. No unexplainable monochromatic emission, pulsed or continuous, was detected. The detection threshold corresponds to an ∼70 GW laser having a diffraction-limited 10-m aperture 1 kpc away (depending on wavelength). Past all-sky optical and radio-wave surveys revealed thousands of unexpected objects exhibiting extraordinary spectral emission, but none were technological. Hypotheses of our Milky Way Galaxy teeming with advanced life must be demoted. [ABSTRACT FROM AUTHOR]

The processing of hundreds of Synthetic Aperture Radar (SAR) images acquired by two satellite systems: Sentinel‐1 and COSMO‐SkyMed reveals a decade of ground deformation for a ∼0.5 km diameter area around the summit crater of the only active carbonatitic volcano on Earth: Ol Doinyo Lengai in Tanzania. Further decomposing ascending and descending orbits when the appropriate SAR data sets overlap allow us to interpret the imaged deformation as ground subsidence with a significant rate of ∼3.6 cm/yr for the pixels located just north of the summit crater. Using geodetic modeling and inverting the highest spatial resolution COSMO‐SkyMed data set, we show that the mechanism explaining this subsidence is most likely a deflating very shallow (≤1 km depth below the summit crater at the 95% confidence level) magma reservoir, consistent with geochemical‐petrological and seismo‐acoustic studies. Plain Language Summary: We used data from two satellite systems, Sentinel‐1 and Cosmo‐SkyMed, to study Ol Doinyo Lengai, the only active carbonatitic volcano on Earth located in Tanzania. We analyzed hundreds of radar satellite images over a decade to understand changes in the ground deformation. By separating data from different orbits of the satellites and comparing them, we found that the ground at the volcano's summit was sinking at a rate of approximately 3.6 cm per year. Through simple geodetic modeling, we concluded that this sinking was likely caused by a shallow deflating magma reservoir, less than one km deep below the summit. This conclusion aligns with other scientific studies focusing on the volcano's petrology and geophysics. Key Points: InSAR time series show a ∼0.5 km diameter subsiding area around the summit crater at Ol Doinyo LengaiThe subsidence is likely due to a deflating shallow magma reservoir (point‐source)The modeled reservoir is less than 1 km deep below the summit crater, consistent with geophysical and geochemical studies [ABSTRACT FROM AUTHOR]

La Palma, Canary Islands, had its largest historical eruption in 2021. From January 2022 to May 2023 there were >2,100 seismic events, primarily at depths ≤20 km, prompting us to update the deformation and modeling study, using interferometric synthetic aperture radar observations and a last generation interpretation tool. We detect the evolution of the remaining magmatic body in the SW portion of the island, with arrival of new magma moving into the oceanic crust out to sea, and a pressurized zone in the central‐eastern area, at regions of structural weakness. The current source characteristics have some similarities to the early stage dynamics prior to the 2021 eruption. Operational and multidisciplinary studies must continue to monitor either their stabilization or growth and destabilization. The ability to identify magma ascent using only deformation data over short time periods allows us to characterize unrest patterns and provide new insights into volcanic processes. Plain Language Summary: Following the 2021 eruption at La Palma, Canary Islands, seismic activity has persisted that may be a precursor to future volcanic activity. Here we perform an updated deformation and modeling study that characterizes the evolution of the remaining magma in the SW area of the island. A fresh injection of magma occurs in the oceanic crust off the coast, along with a pressurized region in the central‐eastern area of the island where crustal weakness exists close to recent volcanic eruptions. The 2022–2023.4 process has similar characteristics to the evolution prior to the 2021 eruption. Ongoing studies should focus on monitoring the increase or decrease in this activity. Improved characterization of changing volcanic activity over short time periods can provide new insights into ongoing volcanic processes. Key Points: We determine the current unrest is similar to that prior to the 2021 eruption warranting attention by hazard response decision makersWe detect a shallow magmatic intrusion in the SW of the island migrating W into the submarine crust of the islandWe also detect a shallow pressurized zone located in the central eastern region of the island [ABSTRACT FROM AUTHOR]

Both unresolved physics in numerical models and limited theoretical understanding of the small-scale diffusion processes occurring near the ocean surface hamper predictability of tropical cyclone (TC) wind changes. An analytical model is here developed to diagnose the short-term evolution of the TC wind profile. An effective frictional parameter is introduced to control the unknown diffusion effects. When this frictional parameter is adjusted to match the TC intensity change, solutions are consistent with observed high-resolution ocean surface wind speeds from satellite synthetic aperture radar (SAR). The initial high-resolution estimate of the near-core wind structure is then found to strongly modulate the wind profile evolution. The frictional parameter can, unfortunately, not efficiently be calibrated using outer-core wind speed changes. Low-resolution observations or standard numerical weather predictions may thus not be directly used to reinterpret and anticipate short-term TC wind changes. The expected accumulation of orbiting SAR sensors as well as improved measurements of the ocean-atmosphere boundary layer characteristics shall then become essential to more precisely monitor TC dynamics. [ABSTRACT FROM AUTHOR]

Land subsidence, the gradual or sudden sinking of the land, poses a global threat to infrastructure and the environment. This paper introduced a hybrid method based on deep convolutional neural networks (CNN) and persistent scattered interferometric synthetic aperture radar (PSInSAR) to estimate land subsidence in areas where PSInSAR cannot provide reliable measurements. This approach involves training a deep CNN with subsidence driving forces and PSInSAR data to learn patterns and estimate subsidence values. Our evaluation of the model shows its efficiency in overcoming the discontinuities observed in the PSInSAR results, producing a continuous subsidence surface. The deep CNN was evaluated on training, validation, and testing data, resulting in mean squared errors of 5 mm, 9 mm, and 11 mm, respectively. In contrast, the kriging interpolation method showed a mean square error of 37.19 mm in the experimental data set. subsidence prediction using the deep CNN method showed a 70 % improvement compared to the Kriging interpolation method. [ABSTRACT FROM AUTHOR]

The longer the baseline length of an airborne array synthetic aperture radar (SAR) is, the richer the ground object information can be resolved. The airborne array flexible SAR can overcome the limitation that the baseline length is limited by the belly length by attaching the antenna to the wing. However, fusing the airborne array flexible SAR distributed position and orientation system (POS) data under the wing and the belly is a major difficulty. To address this issue, the paper analyzes the inconsistency of airborne array flexible SAR data with a distributed POS and proposes a three-dimensional imaging method of airborne array flexible SAR based on distributed POS by fusing the Doppler centre, yaw angle and time of the observation data. By using distributed POS data, flexible data can obtain higher resolution than single-POS processing. The experiment of airborne array flexible SAR data proves the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]

Drone-based synthetic aperture radar (SAR) analysis offers a simple and efficient technique for acquiring observational data from various research areas. However, fundamental research on observation techniques, including the investigation of drone flight parameters, remains insufficient. This study focuses on improving the resolution of intensity images of a single-look complex (SLC) via drone-based SAR observations. By conducting experiments at different flight speeds and analysing the obtained SLC images, we demonstrated that higher drone speeds resulted in enhanced image resolution under the conditions investigated at flight speeds of ≤ 5 m s−1. A comparison with satellite SAR images highlighted the superior performance of drone-based SAR in capturing topographic features. The analysis also suggested that factors including trajectory deviation and Doppler rate affected the resolution and azimuth position. The simulation results supported the experimental findings on the relationship between drone speed and resolution. Our results emphasize the potential of drone-based SAR observations for monitoring slope failures and suggest the importance of the drone flight parameter selection. In the future, drone-based SAR is expected to produce intensity images of slopes and locate slope deformations associated with blasting for mining and earthquakes. [ABSTRACT FROM AUTHOR]

The Haiyuan fault system plays a crucial role in accommodating the eastward expansion of the Tibetan Plateau (TP) and is currently slipping at a rate of several centimeters per year. However, limited seismic activities have been observed using geodetic techniques in this area, impeding the comprehensive investigation into regional tectonics. In this study, the geometric structure and source models of the 2022 Mw 6.7 and the 2016 Mw 5.9 Menyuan earthquakes were investigated using Sentinel-1A SAR images. By implementing an atmospheric error correction method, the signal-to-noise ratio of the 2016 interferometric synthetic aperture radar (InSAR) coseismic deformation field was significantly improved, enabling InSAR observations with higher accuracy. The results showed that the reliability of the source models for those events was improved following the reduction in observation errors. The Coulomb stress resulting from the 2016 event may have promoted the strike-slip movement of the western segment of the Lenglongling fault zone, potentially expediting the occurrence of the 2022 earthquake. The coseismic slip distribution and the spatial distribution of aftershocks of the 2022 event suggested that the seismogenic fault may connect the western segment of the Lenglongling fault (LLLF) and the eastern segment of the Tuolaishan fault (TLSF). Additionally, the western segment of the surface rupture zone of the northern branch may terminate in the secondary branch close to the Sunan-Qilian fault (SN-QL) strike direction, and the earthquake may have triggered deep aftershocks and accelerated stress release within the deep seismogenic fault. [ABSTRACT FROM AUTHOR]

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent. [ABSTRACT FROM AUTHOR]

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging. [ABSTRACT FROM AUTHOR]

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model's effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques. [ABSTRACT FROM AUTHOR]

Detecting surface deformation associated with low-magnitude ( M w ≤ 5 ) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator. [ABSTRACT FROM AUTHOR]

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. [ABSTRACT FROM AUTHOR]

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called "homogeneous" or "textureless" regions. The G I 0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and G I 0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) can generate diverse and high-resolution images for data augmentation. However, when GAN is applied to the synthetic aperture radar (SAR) dataset, the generated categories are not of the same quality. The unrealistic category will affect the performance of the subsequent automatic target recognition (ATR). To overcome the problem, we propose a reinforced constraint filtering with compensation afterwards GAN (RCFCA-GAN) algorithm to generate SAR images. The proposed algorithm includes two stages. We focus on improving the quality of easily generated categories in Stage 1. Then, we record the categories that are hard to generate and compensate by using traditional augmentation methods in Stage 2. Thus, the overall quality of the generated images is improved. We conduct experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) acquired by the proposed algorithm indicate its effectiveness. [ABSTRACT FROM AUTHOR]

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels' in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin. [ABSTRACT FROM AUTHOR]

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area. [ABSTRACT FROM AUTHOR]

SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition. [ABSTRACT FROM AUTHOR]

Urban planning has, in recent years, been significantly assisted by remote sensing data. The data and techniques that are used are very diverse and are available to government agencies as well as to private companies that are involved in planning urban and peri-urban areas. Synthetic aperture radar data are particularly important since they provide information on the geometric and electrical characteristics of ground objects and, at the same time, are unaffected by sunlight (day–night) and cloud cover. SAR data are usually combined with optical data (fusion) in order to increase the reliability of the terrain information. Most of the existing relative classification methods have been reviewed. New techniques that have been developed use decorrelation and interferometry to record changes on the Earth's surface. Texture-based features, such as Markov random fields and co-occurrence matrices, are employed, among others, for terrain classification. Furthermore, target geometrical features are used for the same purpose. Among the innovative works presented in this manuscript are those dealing with tomographic SAR imaging for creating digital elevation models in urban areas. Finally, tomographic techniques and digital elevation models can render three-dimensional representations for a much better understanding of the urban region. The above-mentioned sources of information are integrated into geographic information systems, making them more intelligent. In this work, most of the previous techniques and methods are reviewed, and selected papers are highlighted in order for the reader-researcher to have a complete picture of the use of SAR in urban planning. [ABSTRACT FROM AUTHOR]

Net-pen aquaculture, one of the primary forms of aquaculture in shallow lakes, has generated substantial economic benefits but has also triggered eutrophication and ecological degradation. Therefore, the accurate and scientific dynamic monitoring of aquaculture areas is of the utmost importance. However, existing studies have predominantly employed annual monitoring, failing to fully exploit the unique advantage of synthetic aperture radar (SAR) imagery for seasonal, monthly or more frequent time scale monitoring. This study aimed to develop an approach for monitoring the monthly dynamics of net-pen aquaculture using Sentinel-1 time series images. We integrated threshold segmentation and temporal consistency checking to extract spatial data on the net-pen aquaculture from eight typical shallow lakes in Jiangsu Province, one of the eastern coastal provinces of China, from 2016 to 2021. This study yielded the following results: (1) The VV polarization image offers a particularly noticeable contrast between net-pen aquaculture structures and their surroundings and checking for temporal consistency can help alleviate the problem of distinguishing between changes in error caused by misclassification and actual changes that occurred. The overall accuracy of the net-pen aquaculture extraction results was over 85%. (2) This approach not only enables the analysis of changes in the aquaculture area and fence length but can also identify key time points. For instance, the net-pen aquaculture area in Gehu Lake experienced a rapid decline from May to November 2018, the decrease in while Eastern Taihu Lake occurred from September 2018 to June 2019. (3) The removal of net-pen aquaculture in lakes within the Jiangsu Province is primarily attributed to policy responses. In response to the ecological river and lake action plan implemented in Jiangsu Province, a decrease was observed in both the aquaculture areas and fence lengths across most lakes from 2016 to 2021. These results can provide a reference for lake management and ensuring the effective implementation of related policies. [ABSTRACT FROM AUTHOR]

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies. [ABSTRACT FROM AUTHOR]

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake. [ABSTRACT FROM AUTHOR]

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R 2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring. [ABSTRACT FROM AUTHOR]

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China's Chang'e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions. [ABSTRACT FROM AUTHOR]

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations were designed, and the classification accuracy of various approaches was evaluated using the classic convolutional neural networks (CNNs) AlexNet and VGG16. The findings reveal that the commonly employed six-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete nine-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other seven schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition. [ABSTRACT FROM AUTHOR]

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Launched on 2022 December 16, the Surface Water and Ocean Topography (SWOT) satellite, using synthetic aperture radar interferometric techniques, measures sea surface heights (SSHs) across two 50-km-wide swaths, offering high-resolution and accurate 2-D SSH observations. This study explores the efficiency of SWOT in seamount detection employing the vertical gravity gradient (VGG) derived from simulated SWOT SSH data. Simulated circular and elliptical seamounts (height: 900–1500 m) are integrated within the South China Sea's 4000 m background depths. Geoid perturbations induced by these seamounts are extracted through the residual depth model principle, subsequently merged with the DTU21MSS model for simulating SWOT SSH observations. For comparative assessment, SSH data from Jason-2 and Cryosat-2 are included. An automatic algorithm (AIFS) is presented to identify seamount centres and base polygons using VGG derived from simulated altimeter SSH data. The analysis reveals SWOT-derived VGGs precisely locate all seamount centres, base polygons and elliptical seamount azimuths. The merged Jason-2 and Cryosat-2 data face challenges with identifying small circular and elliptical seamounts. Detecting long narrow elliptical seamounts remains arduous; however, SWOT-derived VGGs successfully elucidate the approximate shapes and major axis azimuths of the elliptical seamounts. Validated against 'true values' of VGG, the root-mean-squared deviation (RMSD) of SWOT-derived VGG stands at 1.33 Eötvös, whereas the merged Jason-2 and Cryosat-2 data exhibit an RMSD of 1.93 Eötvös. This study shows the enhanced capability of SWOT from its high-resolution 2-D SSH observations in advancing seamount detection via satellite-derived VGG. We identify challenges and recommend improved detections using data integration and machine learning. [ABSTRACT FROM AUTHOR]

To tackle the challenge of keeping 5G mobile devices compact while supporting millimeter-wave bands, we've created an ultra-compact 4-port dual-band MIMO antenna with a defected ground structure (DGS). This design minimizes mutual coupling and covers a broad frequency range. Built on a Rogers TMM4 substrate with dimensions of 17.76×17.76 mm2, the antenna includes four planar patch antennas positioned perpendicularly at the corners. Each antenna operates at 28/38 GHz and features a rectangular patch with four rectangular slots and a full ground plane. The patches are separated by 0.5 λo, with the DGS further reducing mutual coupling. Both simulations and measurements indicate a significant reduction in mutual coupling (−39 to −60 dB), improving ECC, TARC, MEG, and DG. Time-domain and SAR analyses confirm the antenna's efficiency and its suitability for 5G devices. [ABSTRACT FROM AUTHOR]

Flood detection from synthetic aperture radar (SAR) imagery plays an important role in crisis and disaster management. Based on pre- and post-flood SAR images, flooded areas can be extracted by detecting changes of water bodies. Existing state-of-the-art change detection methods primarily target optical image pairs. The nature of SAR images, such as scarce visual information, similar backscatter signals, and ubiquitous speckle noise, pose great challenges to identifying water bodies and mining change features, thus resulting in unsatisfactory performance. Besides, the lack of large-scale annotated datasets hinders the development of accurate flood detection methods. In this paper, we focus on the difference between SAR image pairs and present a differential attention metric-based network (DAM-Net), to achieve flood detection. By introducing feature interaction during temporal-wise feature representation, we guide the model to focus on changes of interest rather than fully understanding the scene of the image. On the other hand, we devise a class token to capture high-level semantic information about water body changes, increasing the ability to distinguish water body changes and pseudo changes caused by similar signals or speckle noise. To better train and evaluate DAM-Net, we create a large-scale flood detection dataset using Sentinel-1 SAR imagery, namely S1GFloods. This dataset consists of 5,360 image pairs, covering 46 flood events during 2015–2022, and spanning 6 continents of the world. The experimental results on this dataset demonstrate that our method outperforms several advanced change detection methods. DAM-Net achieves 97.8% overall accuracy, 96.5% F1, and 93.2% IoU on the test set. Our dataset and code are available at https://github.com/Tamer-Saleh/S1GFlood-Detection. [ABSTRACT FROM AUTHOR]

Accurate land cover information is pivotal in numerous planning and management activities. Synthetic Aperture Radar (SAR) data has emerged as a valuable resource for land cover assessment. Extracting scattering power components from Polarimetric SAR (PolSAR) data provides essential insights into the characteristics of land cover targets, aiding in their detailed characterization. Various techniques in radar polarimetry have been introduced to obtain scattering power components and explore their significance in land cover applications, typically leveraging full-polarimetric (full-pol) SAR data. However, applying these methods directly to dual-polarimetric (dual-pol) HH-HV or VV-VH data presents challenges due to the limited polarimetric information available compared to full-pol SAR data. Distinguishing between the scattering mechanisms of dihedral (or double-bounce) targets and trihedral (or surface) targets based solely on conventional polarimetric information is particularly challenging in dual-pol data compared to its full-pol counterpart. To address this limitation, our study introduces two indices (DpRBI and DpRSI) designed to characterize scattering from "dihedral-like" (e.g., buildings, bridges) and "surface-like" (e.g., water bodies, bare fields) targets in dual-pol SAR data. These indices, measuring the similarity of a given pixel to either a "dihedral-like" or a "surface-like" target, are then used in the Scattering Power Factorization Framework (SPFF) to derive the proposed scattering power components. The SPFF discerns the primary scattering power contribution within a resolution cell by factorizing the total power, employing the two indices. Our method applies to both Single Look Complex (SLC) and Ground Range Detected (GRD) dual-pol SAR data from operational platforms such as Sentinel-1 and the upcoming NISAR missions. We assess the proposed methodology using Sentinel-1 SLC and GRD SAR data for two scenarios. (1) Spatial variation in the scattering power components over various land covers near Delhi, India; Milan, Italy; and Barcelona, Spain. (2) Temporal variation in the scattering power components over rice-cultivated areas near Vijayawada, India. During these assessments, we observed significant variations in scattering power components over different land cover targets and rice-cultivated fields at various phenological stages. This highlights the capability of the proposed methodology to discern scattering information from dual-pol SAR data. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) systems mounted on Unmanned Aerial Vehicles (UAVs) are becoming a promising solution to inspect beneath the soil surface. In particular, resorting to Synthetic Aperture Radar (SAR) techniques, these systems are able to retrieve subsurface radar images, in which buried targets can be detected. However, one of the major bottlenecks of this technology is the significant amount of time required for computing the radar images (due to the usage of computationally expensive algorithms to handle non-regular acquisition domains). To face this challenge, this work proposes an efficient interpolation approach, combining a height shift operation with a 2D interpolation, that enables the adoption of a migration algorithm called Phase Shift Migration (PSM). As a result, the processing time is drastically improved, offering close to real-time operation. The proposed methodology has been validated in a realistic scenario for detecting buried explosive targets, comparing it with conventional backprojection techniques in terms of image quality and processing time. [Display omitted] [ABSTRACT FROM AUTHOR]

Millimeter wave operates within a wavelength range of 1mm to 10mm and can penetrate through various non-metallic materials, such as the paperboard and plastic films commonly used in cigarette packaging boxes. In comparison to X-ray devices, millimeter wave imagers have the added advantage of not emitting ionizing radiation and having lower electromagnetic radiation output than the standard limit for mobile phone. Given these characteristics, we believe that utilizing millimeter wave imaging technology for detecting missing carton in packaged cigarette boxes could be a viable solution. The paper presents the fundamental imaging theory and showcases some images of cigarette boxes. The results demonstrate that our millimeter wave imager can produce clear images of packaged cigarette boxes with missing carton. We anticipate that this system has significant potential for application in machine vision for tobacco and other similar industries. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Because of high-resolution imaging in day and night and any weather condition, Synthetic Aperture Radar (SAR) has various applications in remote sensing. Building detection is one of the important utilization of SAR images. With analysing SAR images information, a new framework based on fusion of statistical, texture, and semantic features is proposed. At first, in order to reduce the speckle noise effects on the classification results, the SAR image is segmented into superpixels which the classification process is performed for these segments. Then, the statistical Fisher and Haralick texture features are extracted and the best features are selected. By applying an adapted VGGNet, the third type of features are extracted. Using training samples, the features achieving the highest classification accuracy are selected and applied to the classifier. Then, the classification output is improved by decreasing the splitting of the large-size buildings and false alarms using morphological operations and contour fitting process. The performance of the proposed framework is evaluated with two different types of TerraSAR-X images of urban areas. Experiment results show that the proposed method has superior results than the similar works. Also, the proposed method has considerable small false alarm rate. [ABSTRACT FROM AUTHOR]

Environmental applications play a significant role in the ongoing research area of Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In this paper, a new model is proposed for classifying PolSAR images and applied to a part of the Greater Cairo area in the Nile basin, South of Delta, Egypt. First, the proposed model performs data pre-processing by extracting the coherency and covariance elements noted as [T] and [C] matrices, respectively. Second, temporal convolutional networks (TCN) deep learning is used to extract the features from coherency and covariance elements and then train the model. Third, the SoftMax classifier is used to classify the PolSAR image. Finally, the proposed model is tested with evaluation metrics. The obtained results show that the proposed model can achieve high classification performances. [ABSTRACT FROM AUTHOR]

The regional coverage satellite constellations are in demand for fulfilling earth observation needs of specific user regions of interest as opposed to global coverage thus enhancing the revisit period for the area. In this paper, we present a few key mission aspects of the regional coverage constellation designed and operated by the Indian Space Research Organization (ISRO) in low earth orbit. ISRO developed three X-Band Synthetic Aperture Radar (SAR) Earth Observation satellites and placed them in three inclined orbit planes to enhance the Indian region's coverage. This is India's first inclined orbit constellation designed for remote sensing applications in low earth orbit. The satellites were built around a standard bus carrying a Synthetic Aperture Radar payload with a deployable Radial Rib Antenna (RRA). They were designed to achieve a high agility capability. As SAR sensors are capable of all-weather, day, and night imaging; thus, most suitable in inclined orbits where the illumination conditions vary continuously for imaging. Though the inclined orbits provide more revisit opportunities to the region of interest, their characteristics pose challenges in mission design and operations. Though the mission design and planning involved a host of elements, this paper brings out the details of a few key mission analysis elements, planning aspects and operations that were critical in the course of various mission phases. The mission analysis included mission-specific requirements like launch windows selection for the constellation and reference attitude generation specific to inclined orbit geometry. Mission planning had to take care of Radial Rib Antenna (RRA) deployment requirements which were very critical for the mission, apart from taking care of individual subsystem requirements. The SAR payload data product quality and targeting accuracy are very sensitive to the satellite image point range (vis-a-vis SAR operation data window). Extensive post-launch calibration activity was carried out to meet all these mission requirements and data product specifications. The challenge was in fixing the payload beam alignment and fixing the uncertainties in onboard and ground computations. The paper also describes the methods of fixing all possible error sources affecting the high-resolution SAR payload performance. • The regional coverage satellite constellations are in demand for area targets coverage. • Inclined orbit constellations are more suitable for regional coverage using SAR sensors. • Mission operations plans need to consider all the specific requirements of spacecraft systems. • Pre-launch and post -launch calibrations of sensors is mandatory for achieving system performance. [ABSTRACT FROM AUTHOR]

The Pubugou dam is located on the Dadu River in China, with a height of 186 m. The fracturing body at 780 m upstream of its right bank poses a major threat to the downstream reservoir area's safety as well as the operation of the dam. In this study, a multi-source three-dimensional monitoring mode of space-ground-body is built using synthetic aperture radar, surface sensing, and rock mass stress monitoring. This makes it possible to keep an eye on the entire fracturing body in real time. This study proposes a GCPs selection method that combines the high coherence, small deformation reference points obtained by PS-InSAR inversion and stable deformation points, and realizes the high-precision bank slope deformation time series data acquisition method. It addresses the issue of strong subjectivity in the selection of ground control points (GCPs) in the SBAS-InSAR solution process in mountainous areas. The change in multiple scattering wave velocity and the evolution of the rock mass state inside the slope are both extracted using the seismic background noise cross-correlation technique. Through time series analysis and comparison of various monitoring data, the three-dimensional deformation characteristics, instability mechanism, and significant influence of rainfall on the deformation development of the fracturing body are summarized. Additionally, the adaptability, advantages, and disadvantages of various monitoring modes are evaluated. For the crucial slope of the project, a more trustworthy monitoring method will be available thanks to the mutual integration of the space-ground-body monitoring mode and data. [ABSTRACT FROM AUTHOR]

Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain. [ABSTRACT FROM AUTHOR]

Owakudani (Owakuzawa Valley), the largest fumarole area in Hakone volcano, Japan, is a place where volcanic disasters occur frequently, such as the mudslide disaster that killed six people in 1910 and the phreatic eruption that destroyed hot spring supply facilities in 2015. Therefore, monitoring surface displacements that may lead to slope failure is important for protecting life and property. In this study, we estimated three-dimensional displacements from interferometric synthetic aperture radar (InSAR) analysis from the ALOS-2/PALSAR-2 satellite data and detected landslide displacement around Owakudani. Moreover, the temporal variation of the landslide displacement was clarified from the InSAR time series analysis results. The landslide displacement detected in this study corresponds to a pre-existing collapsed landform whose underground structure was unknown. Additionally, the results of finite element analysis clarified the subsurface deformation concentration zone, suggesting the displacement on the pre-existing sliding surface. The landslide displacement started after the 2015 phreatic eruption. Therefore, fluid injection during the eruption might be one of the triggers of the displacement. Moreover, the annual displacement variation corresponds to changes in precipitation, suggesting that underground pore pressure changes have accelerated the displacement. These results demonstrate that the series of our approach can contribute to understanding the causes and mechanisms of landslides for which underground geological surveys are inadequate. [ABSTRACT FROM AUTHOR]

Reservoir filling in large hydropower stations triggers or accelerates deep-seated toppling deformations (DSTDs). In the absence of large failures, toppling can often last long. It is of great significance to study the mechanism of DSTDs for the prediction of affected areas before filling. Taking Yanwan (YW) DSTDs in Xiluodu Reservoir as a sample, we collected 17 scenes of ALOS/PALSAR-1 images and 45 scenes of ALOS/PARSAR-2 images and used the small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) and time-series offset tracking (TSOT) technologies to analyze the spatiotemporal evolution of YW DSTDs before and after reservoir filling. The correlation between deformation and reservoir filling was analyzed with the fluctuation in the reservoir water level. Before filling, there was no deformation in the area of YW DSTDs. After filling, the maximum deformation rate of YW DSTDs measured by TSOT during the study period was around 1500 mm/year in the line-of-sight (LOS) direction. YW DSTDs showed a fluctuating deformation trend, and the deformation rates of the four filling stages were successively from fast to slow: rapid drawdown of water level period, high water level period, low water level period, and rapid rise of water level period. The soft-hard-interbedded structure provides favorable lithology conditions for toppling. The incision of the Jinsha River and the development of gullies led to the topographic condition of exposure on three sides, shaping a convex slope similar to a triangular cone, which provides advantageous topographic conditions for toppling. [ABSTRACT FROM AUTHOR]

Tajikistan is an area characterized by several lakes created after rockfalls and moraine deposits collapsed due to its geographic position on the collisional area of the Euro-Asian and Indian tectonic plates. In 1911, a violent earthquake generated the Usoi dam, which led to the creation of Lake Sarez. The purpose of the article is to provide an overview of the ground deformation of the two landslides that affect and threaten the area of Lake Sarez exploiting the combination of two different techniques, the interferometric synthetic aperture radar (InSAR) method with the SqueeSAR approach and optical image analysis through the use of COSI-Corr software. The combination of these two techniques turned out to be useful and provide more information because InSAR method balances lack the optical analysis and vice versa giving the possibility to obtain a complete overview of the displacement of the area. Moreover, InSAR data have been used to reconstruct the geometry and depth of the sliding surface of a potential landslide that could affect the lake, generating a wave that could overtop the dam and affect the villages downstream. [ABSTRACT FROM AUTHOR]