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Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis. [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]

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals. [ABSTRACT FROM AUTHOR]

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model. We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (R2), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT's continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. [ABSTRACT FROM AUTHOR]

This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale. [ABSTRACT FROM AUTHOR]

It is challenging to precisely measure the slow interseismic crustal-deformation rate from Synthetic Aperture Radar (SAR) data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the Interferometric SAR (InSAR) products and become the main barrier to extracting interseismic tectonic deformation. In this study, we propose a novel temporal-network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio–temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5–6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents a significantly different performance from the traditional quadratic approximate method in the far field. Through the implementation of the proposed method, the root mean square error (RMSE) between the LOSGPS and our derived descending InSAR LOS (line of sight) measurements is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal-deformation measurement. [ABSTRACT FROM AUTHOR]

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas. [ABSTRACT FROM AUTHOR]

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models. [ABSTRACT FROM AUTHOR]

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season's backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former's lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. [ABSTRACT FROM AUTHOR]

Satellite data have been widely utilized for flood detection and mapping tasks, and in recent years, there has been a growing interest in using Synthetic Aperture Radar (SAR) data due to the increased availability of recent missions with enhanced temporal resolution. This capability, when combined with the inherent advantages of SAR technology over optical sensors, such as spatial resolution and independence from weather conditions, allows for timely and accurate information on flood event dynamics. In this study, we present an innovative automated approach, SAR-RST-FLOOD, for mapping flooded areas using SAR data. Based on a multi-temporal analysis of Sentinel 1 data, such an approach would allow for robust and automatic identification of flooded areas. To assess its reliability and accuracy, we analyzed five case studies in areas where floods caused significant damage. Performance metrics, such as overall (OA), user (UA), and producer (PA) accuracy, as well as the Kappa index (K), were used to evaluate the methodology by considering several reference flood maps. The results demonstrate a user accuracy exceeding 0.78 for each test map when compared to the observed flood data. Additionally, the overall accuracy values surpassed 0.96, and the kappa index values exceeded 0.78 when compared to the mapping processes from observed data or other reference datasets from the Copernicus Emergency Management System. Considering these results and the fact that the proposed approach has been implemented within the Google Earth Engine framework, its potential for global-scale applications is evident. [ABSTRACT FROM AUTHOR]

The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using a Convolutional Neural Network (CNN) based on a Bilateral Aggregation Guidance (BAG) strategy, termed BGA-Net. A comprehensive landslide hazard analysis, integrating static landslide susceptibility zonation with dynamic surface deformation monitoring, was therefore conducted. The study area selected was the upper reaches of the Jinsha River, particularly the site of the Baige landslide. The BGA-Net model was first proposed for LSP generation, achieving an accuracy exceeding 85%, while the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was jointly applied to comprehensively analyze the dynamic geological hazard risk at a regional scale. The final results were presented in a lookup table format and mapped to delineate and grade each risk level. The results show the method is practical, with high feasibility. Compared with traditional machine learning methods, the BGA-strategy-oriented CNN model more effectively differentiated the extremely low- and extremely high-susceptibility areas, enhancing decision-making processes. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices. [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]

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]

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SARs) and Ground-Penetrating Radars (GPRs) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency-Modulated Continuous Wave (FMCW) operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device. In one scenario, it was able to detect and locate a 15 cm-diameter fire-extinguisher located at a distance of 3.5 m from the scanning system and 1.6 m behind a 3 cm-thick MDF (medium-density fiberboard) wall with cm-level accuracy. In a second test, the proposed system was used to perform interferometric displacement measurements, and it was capable of determining the displacement of a metal case with sub-millimeter accuracy. In a third experiment, the system was used to construct a 3D image of the inside of a wood table with cm-level resolution. [ABSTRACT FROM AUTHOR]

Quasi-continuous wave radar is an attempt to give consideration to the performance of pulse and continuous wave radar signals. However, it also has the shortcomings of both. This paper aims to add a new quasi-continuous-wave coding method to the spaceborne synthetic aperture radar (SAR) system. The technology of improving spaceborne SAR imaging performance by coding quasi-continuous-wave pulses is studied, and some shortcomings of this algorithm are improved. Firstly, the application of quasi-continuous-wave radar in the SAR system is studied, and the coding and reconstruction scheme is provided so that this technology can be successfully applied in spaceborne SAR. Secondly, the effects of different quasi-continuous-wave coding methods on SAR imaging performance are evaluated, including signal-to-noise ratio, resolution, and integration time. Then, several coding schemes are given, and the characteristic changes of the signal after quasi-continuous-wave coding are analyzed. The transmit–receive conversion loss function and azimuth Doppler ambiguity function of the design scheme are analyzed, which proves the advantages of the scheme. Finally, we design the hardware implementation scheme and carry out the practical test. [ABSTRACT FROM AUTHOR]

Planetary radar observations have provided invaluable information on the solar system through both ground-based and space-based observations. In this overview article, we summarize how radar observations have contributed in planetary science, how the radar technology as a remote-sensing method for planetary exploration and the methods to interpret the radar data have advanced in the eight decades of increasing use, where the field stands in the early 2020s, and what are the future prospects of the ground-based facilities conducting planetary radar observations and the planned spacecraft missions equipped with radar instruments. The focus of the paper is on radar as a remote-sensing technique using radar instruments in spacecraft orbiting planetary objects and in Earth-based radio telescopes, whereas ground-penetrating radar systems on landers are mentioned only briefly. The key scientific developments are focused on the search for water ice in the subsurface of the Moon, which could be an invaluable in situ resource for crewed missions, dynamical and physical characterization of near-Earth asteroids, which is also crucial for effective planetary defense, and a better understanding of planetary geology. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations' data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city's gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China. [ABSTRACT FROM AUTHOR]

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system's ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas. [ABSTRACT FROM AUTHOR]

Traditional synthetic-aperture radar (SAR) imaging, while adept at capturing stationary scenes with clarity, often results in the blurring of moving targets due to Doppler spectrum disparities. To overcome this, this study introduces an innovative optical approach for imaging moving targets. By employing a spatial light modulator (SLM), the phase information of SAR data can be modulated onto a light beam. Then, the light propagation path is meticulously designed to take advantage of the free propagation of light to compensate for the phase differences in SAR data, thereby achieving focal clarity. Simulations and experimental validations have demonstrated that this method surpasses traditional digital imaging techniques in terms of focusing precision. Additionally, the processing delay is only as long as the time taken for light propagation. The optical path is simple, avoiding complex assembly and alignment. This novel approach opens up new possibilities for the SAR imaging of moving targets, offering potential applications in moving target extraction, separation, and velocity estimation. [ABSTRACT FROM AUTHOR]

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly valuable in the field of high-precision mapping. However, the motion geometry of CSAR is more intricate compared to traditional strip SAR, demanding high precision from navigation systems. The accumulation of errors over the long synthetic aperture time cannot be overlooked. CSAR exhibits significant coupling between the range and azimuth directions, making traditional motion compensation methods based on linear SAR unsuitable for direct application in CSAR. The dynamic nature of flight, with its continuous changes in attitude, introduces a significant deformation error between the non-rigidly connected Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). This deformation error makes it difficult to accurately obtain radar position information, resulting in imaging defocus. The research in this article uncovers a correlation between the deformation error and radial acceleration. Leveraging this insight, we propose utilizing radial acceleration to estimate residual motion errors. This paper delves into the analysis of Position and Orientation System (POS) errors, presenting a novel high-resolution CSAR motion compensation method based on airborne platform acceleration information. Once the system deformation parameters are calibrated using point targets, the deformation error can be directly calculated and compensated based on the acceleration information, ultimately resulting in the generation of a high-resolution image. In this paper, the effectiveness of the method is verified with airborne flight test data. This method can compensate for the deformation error and effectively improve the peak sidelobe ratio and integral sidelobe ratio of the target, thus improving image quality. The introduction of acceleration information provides new means and methods for high-resolution CSAR imaging. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions. [ABSTRACT FROM AUTHOR]

The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m. [ABSTRACT FROM AUTHOR]

Exploring the deformation mechanism of the 2021 Mw 7.4 Maduo Earthquake is crucial for better understanding the seismic hazard of the faults with low strain rates inside the Bayan Har block. This study leverages deformation information derived from Sentient-1 A/B images and GPS data to investigate in detail the co- and postseismic deformation mechanisms using multiple methods. The main results are as follows. First, the postseismic InSAR time series robustly identified the reactivation of the Changmahe fault, indicating the impact of the Maduo event on surrounding active faults. Second, the joint inversion of Interferometric Synthetic Aperture Radar and GPS revealed that (1) there was a complementary and partially overlapping relationship between the coseismic slip and postseismic afterslip of the main rupture; and (2) the Changmahe fault exhibited thrust compression dislocation in the early stage and experienced a sustained compressive effect from afterslip in the one year after the mainshock. Third, modeling the processes of viscoelastic relaxation and poroelastic rebound revealed that the postseismic deformation was probably caused by a combination of afterslip (near-field) and viscoelastic relaxation (near and far field). Fourth, the stress changes driven by the Maduo event revealed that the seismic gaps inside the Maqin-Maqu segment and the Kunlun Pass-Jiangcuo fault will be potential seismic risks in the future. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. In response to this challenge, this paper innovatively proposes an object-enhanced network, OE-YOLO, designed specifically for SAR ship detection. Firstly, we input the original image into an improved CFAR detector, which enhances the network's ability to localize and perform object extraction by providing more information through an additional channel. Additionally, the Coordinate Attention mechanism (CA) is introduced into the backbone of YOLOv7-tiny to improve the model's ability to capture spatial and positional information in the image, thereby alleviating the problem of losing the position of small objects. Furthermore, to enhance the model's detection capability for multi-scale objects, we optimize the neck part of the original model to integrate the Asymptotic Feature Fusion (AFF) network. Finally, the proposed network model is thoroughly tested and evaluated using publicly available SAR image datasets, including the SAR-Ship-Dataset and HRSID dataset. In comparison to the baseline method YOLOv7-tiny, OE-YOLO exhibits superior performance with a lower parameter count. When compared with other commonly used deep learning-based detection methods, OE-YOLO demonstrates optimal performance and more accurate detection results. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) imaging, intelligent object detection methods are facing significant challenges in terms of model robustness and application security, which are posed by adversarial examples. The existing adversarial example generation methods for SAR object detection can be divided into two main types: global perturbation attacks and local perturbation attacks. Due to the dynamic changes and irregular spatial distribution of SAR coherent speckle backgrounds, the attack effectiveness of global perturbation attacks is significantly reduced by coherent speckle. In contrast, by focusing on the image objects, local perturbation attacks achieve targeted and effective advantages over global perturbations by minimizing interference from the SAR coherent speckle background. However, the adaptability of conventional local perturbations is limited because they employ a fixed size without considering the diverse sizes and shapes of SAR objects under various conditions. This paper presents a framework for region-adaptive local perturbations (RaLP) specifically designed for SAR object detection tasks. The framework consists of two modules. To address the issue of coherent speckle noise interference in SAR imagery, we develop a local perturbation generator (LPG) module. By filtering the original image, this module reduces the speckle features introduced during perturbation generation. It then superimposes adversarial perturbations in the form of local perturbations on areas of the object with weaker speckles, thereby reducing the mutual interference between coherent speckles and adversarial perturbation. To address the issue of insufficient adaptability in terms of the size variation in local adversarial perturbations, we propose an adaptive perturbation optimizer (APO) module. This optimizer adapts the size of the adversarial perturbations based on the size and shape of the object, effectively solving the problem of adaptive perturbation size and enhancing the universality of the attack. The experimental results show that RaLP reduces the detection accuracy of the YOLOv3 detector by 29.0%, 29.9%, and 32.3% on the SSDD, SAR-Ship, and AIR-SARShip datasets, respectively, and the model-to-model and dataset-to-dataset transferability of RaLP attacks are verified. [ABSTRACT FROM AUTHOR]

Generating accurate monthly estimations of water level fluctuations in reservoirs and lakes is crucial for supporting effective water resource management and protection. The dual-satellite configuration of Sentinel-3 makes it possible to monitor water level changes with great coverage and short time intervals. However, the potential of Sentinel-3's Synthetic Aperture Radar Altimetry (SRAL) data to enable operational monitoring of water levels across Jiangsu Province on a monthly basis has not yet been fully explored. This study demonstrated and validated the use of Sentinel-3's SRAL to generate accurate monthly water level estimations needed to inform water management strategies. The monthly water levels of lakes and reservoirs from 2017 to 2021 were produced using Sentinel-3 level-2 land products. Results showed that, compared with in situ data across eight studied lakes, all lakes presented R (Pearson correlation coefficient) values greater than 0.5 and Root Mean Square Error (RMSE) values less than 1 m. Notably, water level estimates for Tai Lake, Gaoyou Lake, and Luoma Lake were particularly accurate, with R above 0.9 and RMSE below 0.5 m. Furthermore, the monthly water level estimates derived from the Sentinel-3 data showed consistent seasonal trends over the multi-year study period. The annual water level of all lakes did not change significantly, except for Shijiu Lake, of which the difference between the highest and lowest water level was up to about 5 m. Our findings confirmed the water level observation ability of Sentinel-3. The accuracy of water level monitoring could be influenced by internal water level differences, terrain features, as well as the area and shape of the lake. Larger lakes with more altimetry sampling points tended to yield higher accuracy estimates of water level fluctuations. These results demonstrate that the frequent, wide-area coverage offered by this satellite platform provides valuable hydrological information, especially across remote regions lacking in situ data. Sentinel-3 has immense potential to support improved water security in data-scarce regions. [ABSTRACT FROM AUTHOR]

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic InSAR, having relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne bistatic InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne bistatic InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision bistatic InSAR imaging, and interferometric processing. Height inversion results based on the proposed method are also provided, which demonstrate the effectiveness of the proposed method in improving the accuracy of interferometric measurement at calibration points from 0.66 m to 0.42 m. [ABSTRACT FROM AUTHOR]

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2 ≥ 0.75 ) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs. [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform's position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS). [ABSTRACT FROM AUTHOR]

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms. [ABSTRACT FROM AUTHOR]

The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of the exact location and time of the initial internal wave for which the generation mechanism of internal waves in the northern South China Sea must be well understood. By analyzing the internal wave field in the northern South China Sea (SCS) simulated using the MIT General Circulation Model (MITgcm) and observations from satellite synthetic aperture radar (SAR) and mooring temperature–salinity–depth (TSD) chains, the source regions and propagation initiation times of internal waves are identified for three typical tidal phases, i.e., the diurnal-tide-dominated phase (DTP), transition tide phase (TTP), and semidiurnal-tide-dominated phase (STP). The generation procedures of Type A and Type B internal waves are discussed in detail with those data. The present study reveals that Type A and Type B waves are generated at the eastern and western ridges, respectively, and both commence their westward propagation at the peak of the eastward tidal flow. The dynamics of lee waves and the resonance effect with double ridges constitute the generation mechanisms of internal waves in the northern SCS. Combined with varying configurations of tidal conditions, topography, and stratification, the generation procedures of Type A and Type B waves in the DTP, TTP, and STP are elucidated with the generation mechanism in a unified and self-consistent way. In short, during DTP, weaker A waves alternate with weaker B waves each day; during TTP, strong A waves and strong B waves appear alternately every day; and there are two weak A waves per day during the STP. The generation mechanism can help in developing future empirical models for generating internal waves using tidal currents, topography, and stratification without requiring complex fluid dynamics calculations. [ABSTRACT FROM AUTHOR]

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail. [ABSTRACT FROM AUTHOR]

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely used to improve land cover classification. However, fully integrating optical and SAR image data is not straightforward due to the distinct distributions of their features. To this end, we propose a land cover classification network based on multimodal feature fusion, i.e., MFFnet. We adopt a dual-stream network to extract features from SAR and optical images, where a ResNet network is utilized to extract deep features from optical images and PidiNet is employed to extract edge features from SAR. Simultaneously, the iAFF feature fusion module is used to facilitate data interactions between multimodal data for both low- and high-level features. Additionally, to enhance global feature dependency, the ASPP module is employed to handle the interactions between high-level features. The processed high-level features extracted from the dual-stream encoder are fused with low-level features and inputted into the decoder to restore the dimensional feature maps, generating predicted images. Comprehensive evaluations demonstrate that MFFnet achieves excellent performance in both qualitative and quantitative assessments on the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the OA and Kappa metrics by 7.7% and 11.26% on the WHU-OPT-SAR dataset, respectively. [ABSTRACT FROM AUTHOR]

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one. [ABSTRACT FROM AUTHOR]

Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction in the past 70 years. In this study, we used MT-InSAR technology and ascending C-band Sentinel-1 SAR data from 2017 to 2023 to study land deformation in the junction area of Shijiazhuang–Baoding–Cangzhou–Hengshui. We identified multiple subsidence funnels with a maximum rate exceeding −150 mm/year and a total deformation surpassing 600 mm. Seasonal decomposition methods accurately separated seasonal signals in the time-series deformation and groundwater level data. An exponential function model applied to long-term deformation showed no significant decrease in subsidence in severely affected areas. By modeling seasonal deformation and seasonal groundwater levels, we determined the elastic skeletal storage coefficients (Ske) to be in the range of 1.02 × 10−3~6.53 × 10−3 in subsidence areas. We obtained the spatiotemporal evolution of the total groundwater storage (TGWS), irreversible ground storage (IGWS), and recoverable ground storage (RGWS). The TGWS and IGWS decreased annually while the RGWS increased, which is attributable to the implementation of the South-to-North Water Diversion Project (SNWDP) and the issuance of groundwater withdrawal policies in the NCP. [ABSTRACT FROM AUTHOR]

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model's high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts' by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems. [ABSTRACT FROM AUTHOR]

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m. [ABSTRACT FROM AUTHOR]

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo- L 0 -norm fast iterative shrinkage algorithm network (pseudo- L 0 -norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo- L 0 -norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo- L 0 -norm regularization model. The network's parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo- L 0 -norm FISTA-net for AgileSAR imaging. [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data. [ABSTRACT FROM AUTHOR]

Due to the complex range migration characteristics of large squint angle synthetic aperture radar (SAR), it is difficult for traditional SAR deceptive jamming algorithms to balance focusing ability and computational efficiency. There is an urgent demand for proposing a deceptive jamming algorithm against large squint angle SAR in the field of SAR jamming. This article proposes a deceptive jamming algorithm against SAR with large squint angles based on non-linear chirp scaling and low azimuth sampling reconstruction (NLCSR). The NLCSR algorithm uses a high-order approximation of a high-precision model to accurately construct the jammer's frequency response (JFR) function. In line with the notion of low azimuth sampling processing of the transformation domain, the construction of the space-variant azimuth modulation phase item is completed using the non-linear chirp scaling method. Compared with the traditional deceptive jamming algorithms against the large squint angle SAR, the NLCSR algorithm only needs Fourier transform and complex multiplication while ensuring the focusing ability, which is easier to implement on an efficient parallel digital signal processor based on fast Fourier transform (FFT). Simulation results prove the superior property of the NLCSR algorithm in focusing ability and computational efficiency. Compared to the existing large squint angle SAR deceptive jamming algorithm, the focusing ability of the NLCSR algorithm is almost the same, and the calculation efficiency is improved by at least 52.1%. [ABSTRACT FROM AUTHOR]