Search Results (367)

The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m²), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from ~80 to ~2800 m²), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m² area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm. Full article

Urban areas face an imminent risk of collapse due to structural deficiencies and gradual ground subsidence. Therefore, monitoring surface movements is crucial for detecting abnormal behavior, implementing timely preventive measures, and minimizing the detrimental effects of this phenomenon in residential regions. In this context, interferometric synthetic aperture radar (InSAR) has emerged as a highly effective technique for monitoring slow and long-term ground hazards and surface motions. The first goal of this study is to explore the potential applications of persistent scatterer interferometry (PSI) and small baseline subset (SBAS) algorithms in collapse hotspot detection, utilizing a dataset consisting of 144 Sentinel-1 images. The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. In the second phase, this research incorporated an additional dataset of 36 TerraSAR-X images alongside the Sentinel-1 data to compare results based on radar images with different spatial resolutions in the C and X bands. The findings reveal a strong correlation between the TerraSAR-X and Sentinel-1 time series. Notably, the analysis of the TerraSAR-X time series for one study area identified additional collapse-prone points near the accident site, attributed to the higher spatial resolution of these data. By leveraging the capabilities of InSAR and advanced algorithms, like SBAS, this study highlights the potential to identify areas at risk of collapse, enabling the implementation of preventive measures and reducing potential harm to residential communities. Full article

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite. Full article

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions. Full article

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings. Full article

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images. Full article

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a “W” shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines. Full article

The effective monitoring of railway facilities is crucial for safety and operational efficiency. This study proposes an enhanced remote monitoring technique for railway facilities, specifically bridges, using satellite radar InSAR (Interferometric Synthetic Aperture Radar) technology. Previous studies faced limitations such as insufficient data points and challenges with topographical and structural variations. Our approach addresses these issues by analysing displacements from 30 images captured by the X-band SAR satellite, TerraSAR-X, over two years. We tested each InSAR parameter to develop an optimal set of parameters, applying the technique to a post-tensioned PSC (pre-stressed concrete) box bridge. Our findings revealed a recurring arch-shaped elevation along the bridge, attributed to temporal changes and long-term deformation. Further analysis showed a strong correlation between this deformation pattern and average surrounding temperature. This indicates that our technique can effectively identify micro-displacements due to temperature changes and structural deformation. Thus, the technique provides a theoretical foundation for improved SAR monitoring of large-scale social overhead capital (SOC) facilities, ensuring efficient maintenance and management. Full article

In this study, we investigate the usefulness of the spectral whitening procedure, devised by one of the authors as a preprocessing stage of envelope-detected single-look synthetic aperture radar (SAR) images, in application contexts where phase information is relevant. In the first experiment, each of the raw datasets of an interferometric pair of COSMO-SkyMed images, representing industrial buildings amidst vegetated areas, was individually (1) synthesized by the SAR processor without Fourier-domain Hamming windowing; (2) synthesized with Hamming windowing, used to improve the focalization of targets, with the drawback of spatially correlating speckle; and (3) processed for the whitening of complex speckle, using the data obtained in (2). The interferograms were produced in the three cases, and interferometric coherence and phase maps were calculated through 3 × 3 boxcar filtering. In (1), coherence is low on vegetation; the presence of high sidelobes in the system’s point-spread function (PSF) causes the spread of areas featuring high backscattering. In (2), point targets and buildings are better defined, thanks to the sidelobe suppression achieved by the frequency windowing, but the background coherence is abnormally increased because of the spatial correlation introduced by the Hamming window. Case (3) is the most favorable because the whitening operation results in low coherence in vegetation and high coherence in buildings, where the effects of windowing are preserved. An analysis of the phase map reveals that (3) is likely to be facilitated also in terms of unwrapping. Results are presented on a TerraSAR-X/TanDEM-X (TSX-TDX) image pair by processing the interferograms of original and whitened data using a non-local filter. The main results are as follows: (1) with autocorrelated speckle, the estimation error of coherence may attain 16% and inversely depends on the heterogeneity of the scene; and (2) the cleanness and accuracy of the phase are increased by the preliminary whitening stage, as witnessed by the number of residues, reduced by 24%. Benefits are also expected not only for differential InSAR (DInSAR) but also for any coherent analysis and processing carried out performed on SLC data. Full article

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. Full article

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. Full article

Dam stability is one of the most essential geotechnical engineering challenges. Studying the structural behavior of dams during their useful life is an essential component of their safety. Terrestrial surveying network approaches are typically expensive and time-consuming. Over the last decade, the interferometric synthetic aperture radar (InSAR) method has been widely used to monitor millimeter displacements in dam crests. This research investigates the structural monitoring of the Lar Dam in Iran, using InSAR and the terrestrial surveying network technique to identify the possible failure risk of the dam. Sentinel-1A images taken from 5 February 2015 to 30 September 2019 and TerraSAR-X (09.05.2018 to 16.08.2018) images were analyzed to investigate the dam’s behavior. The InSAR results were compared with those of the terrestrial surveying network for the period of 1992 to 2019. The Sentinel-1 results implied that the dam on the left side moved over 8 mm/yr. However, the pillars to the left abutment indicated an uplift, which is consistent with the TerraSAR-X results. Also, the TerraSAR-X data indicated an 8 mm displacement over a three-month period. The terrestrial surveying showed that the largest uplift was 19.68 mm at the TB4 point on the left side and upstream of the body, while this amount was 10 mm in the interferometry analysis for the period of 2015–2020. The subsidence rate increased from the middle part toward the left abutment. The geological observations made during the ninth stage of the terrestrial surveying network indicate that there was horizontal and vertical movement over time, from 1992 to 2019. However, the results of the InSAR processing in the crown were similar to those of the terrestrial surveying network. Although different comparisons were used for the measurements, the difference in the displacement rates was reasonable, but all three methods showed the same trend in terms of uplift and displacement. Full article

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ₀-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ₀-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm. Full article

Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment. Full article

A new analytical formula for H₀, one of the three parameters (H₀, g, and r) on which the NeQuick model is based to describe the altitude profile of the electron density above the F2-layer peak height hmF2, has recently been proposed. This new analytical representation of H₀, called H_0,corr, relies on numerical grids based on two different types of datasets. On one side, electron density observations by the Swarm satellites over Europe from December 2013 to September 2018, and on the other side, IRI UP (International Reference Ionosphere UPdate) maps over Europe of the critical frequency of the ordinary mode of propagation associated with the F2 layer, foF2, and hmF2, at 15 min cadence for the same period. The new NeQuick topside representation based on H_0,corr, hereafter referred to as NeQuick-corr, improved the original NeQuick topside representation. This work updates the numerical grids of H_0,corr by extending the underlying Swarm and IRI UP datasets until December 2021, thus allowing coverage of low solar activity levels, as well. Moreover, concerning Swarm, besides the original dataset, the calibrated one is considered, and corresponding grids of H_0,corr calculated. At the same time, the role of g is investigated, by considering values different from the reference one, equal to 0.125, currently adopted. To understand what are the best H_0,corr grids to be considered for the NeQuick-corr topside representation, vertical total electron content data for low, middle, and high latitudes, recorded from five low-Earth-orbit satellite missions (COSMIC/FORMOSAT-3, GRACE, METOP, TerraSAR-X, and Swarm) have been analyzed. The updated H_0,corr grids based on the original Swarm dataset with a value for g = 0.15, and the updated H_0,corr grids based on the calibrated Swarm dataset with a value for g = 0.14, are those for which the best results are obtained. The results show that the performance of the different NeQuick-corr models is reliable also for low latitudes, even though these are outside the spatial domain for which the H_0,corr grids were obtained, and are dependent on solar activity. Full article