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In this article, we present a method for combining image-based displacements to build time series. This method takes advantage of the redundancy of these displacements, which comes from multiple possible combinations between images. The proposed method combines common master series with a different master date to construct a single time series of relative displacements. We were able to test this method on displacements computed using images from a stereo time-lapse device recording images of the Argentière glacier during the summer and fall of 2019. Our method was compared to two other displacement aggregation strategies: a simple common master approach and the classical inversion method. In order to perform this comparison, displacement data from four permanent GPS are used. The results show that our method provides a more accurate time series of relative displacements than those obtained with the other methods. The higher accuracy of the proposed method makes the detection of seasonal variations in glacier velocity possible.

Antennas used in glacial environmental sensor networks and reported in the last two decades have been reviewed. A link budget framework for designing such antenna systems is presented and used to design an antenna system for deployment at the Thwaites glacier, Antarctica. Design details of two left hand circularly polarized cross dipole antennas, one for englacial sensor probes and the other for supraglacial surface receivers are presented. The probe antenna is a 3D bent cross dipole that fits within a borehole of 8 cm diameter while providing a 1 dBic gain at 433 MHz in ice. The surface receiver antenna is a planar printed antenna providing a gain of 6.1 dBic with a quarter wave reflector. Both antennas provide 3 dB beamwidths of at least 50° in the xz and yz vertical planes catering for transmitter-receiver antenna misalignments caused by extended deployments. The antennas displayed good circular polarization and polarization purity traits. The 3 dB axial ratio bandwidths of both the antennas remained 54.9 %. The total efficiencies of the bent cross dipole and the surface receiver antennas were noted as 69.7 % and 86.9 % respectively. Lastly, the 433 MHz band has been validated for achieving englacial communication ranges of up to 2300 metres.

The systematic monitoring of glaciers is essential to both evaluate water resource availability and better understand the effects of climate change. The increased speed of glacier changes observed in the past few years requires a more frequent update of the glacier inventories than in the past; however, the high human supervision required by the state-of-the-art techniques is discouraging their systematic application over large areas. This article proposes a novel approach to exploit the large volume of data provided by Copernicus Sentinel missions for detecting glacier outlines, including debris-covered glaciers. In detail, our method exploits the Sentinel-1 and Sentinel-2 multitemporal images to build a composite image representing the glacier conditions during the yearly maximum ablation period. The Sentinel-2 multispectral images are classified with a support vector machine and composed to a mosaic that represents the information of the maximum glacier ablation. At the same time, the Sentinel-1 time series are exploited to build a multitemporal coherence composite that represents all the snow-covered and glaciated areas together with all the moving surfaces. This information is used together with the Sentinal-2 composite to detect the debris-covered part of the glaciers. The proposed method was tested in the Central East Alps and presented an overall accuracy of 92% with respect to a reference inventory over South Tyrol and an agreement of 90% with respect to the latest glacier inventory of the Alps from Sentinel-2. The proposed approach enables to assist glacier experts in identifying glacier outlines over large areas and in short time

The TanDEM-X mission allows generation of digital elevation models (DEMs) with high potential for glacier monitoring, but the radar penetration into snow and ice remains a main source of uncertainty. In this study, we generate five new DEMs of the Mont-Blanc area from TanDEM-X interferometric pairs acquired in 2012/2013. We conducted a multitemporal analysis of the DEMs in comparison with two high-resolution DEMs obtained from Pléiades stereo satellite images in 2012 and 2013. A vertical precision of 1–3 m of the radar DEMs is estimated over ice and snow free areas and slopes less than 40°. DEM-derived elevation changes are compared with outputs of the snowpack model Crocus and snow accumulation measurements. The results show that at altitudes below $\sim$2500-m a.s.l., the radar penetration is negligible in our study area. The DEM-derived elevation changes agree, within uncertainty, with the modeled and field snow height. At higher altitudes, the comparison between the radar and optical DEMs acquired only a few weeks apart allows estimating the interferometric bias of the X-band DEM in the dry snowpack. At 4000-m a.s.l, it reaches 4 m on average in October and February. A geodetic glacier mass balance calculated using the October radar DEM would be biased. For the least favorable case, the highly elevated Bossons glacier, the bias would correspond to 1.66-m w.e. This error is too large to derive significant annual mass balances, but similar to elevation or seasonality uncertainties if integrated over a 10-years period. [ABSTRACT FROM PUBLISHER]

Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.

Accurate multitemporal glacier change investigations and analyses are lacking on the southeastern Tibetan Plateau (SETP). A combination of photogrammetry, optical remote sensing, and synthetic aperture radar datasets can accurately identify large-scale glaciers; In this article, glaciers in three periods on the SETP (1970s, 2000, 2020) were identified from multisource remote sensing data based on a deep learning method and manual visual interpretation, and multitemporal glacial inventory data from relatively high-frequency source imagery. Totals of 11648, 12993, and 11875 glaciers were identified in the 1970s, 2000, and 2020, with total areas of 13372.08 km2, 11692.31 km2, and 10612.94 km2, respectively. The general distribution of SETP glaciers was identified to be typical of alpine glaciers dominated by small-sized glaciers. The average elevation of glaciers was approximately 5000 m; the slopes were mostly lower than 40°, and the main aspect was southeast, followed by south and southwest. The glaciers retreated from the 1970s to 2020, and a total glacier area of approximately 2759.14 km2 was degraded during this time, with an average annual melting rate of 0.45% yr−1. Rising summer temperatures may be the driving force behind the continuous decline in the glacier area. Overall, the results obtained in this article showed relatively low uncertainty involved in the identification of glaciers compared to some previous studies. The results can provide accurate glacier information for glacier monitoring and modeling studies on the SETP.

The use of Kalman filtering techniques for landslide monitoring has proved effective as a tool for estimating and predicting land displacements. Ground-Based Synthetic Aperture Radars (GBSAR) are popular remote sensing instruments able to provide displacement maps of the investigated area, with submillimeter precision. These instruments outperform other sensors in several respects, such as all-weather and all-day monitoring. However, in some cases, for instance in vegetated scenarios, the displacement is affected by a significant uncertainty due to the decorrelation of the radar signal. In such a case, to retrieve any reliable information, noise must be filtered out using appropriate methods. Given the success of kinematic modeling of landslide movements through Kalman filtering, this technique seems to be the optimal candidate for processing the displacement measured by interferometric GBSAR data. This paper investigates this idea, by applying Kalman Filter to GBSAR measurements acquired in two different campaigns: a landslide monitoring in north Spain, and a sliding glacier monitoring in the Alpes, Italy. A proper initialization of the algorithm parameters is fundamental for a correct application of the Kalman filter. In this work, we present a strategy that exploits information from coherent pixels for tuning the filtering parameters and optimizing the filter performance on areas with low coherence.

In this study, we develop a novel method to automatically detect areas of snow avalanche debris using a color space segmentation technique applied to synthetic aperture radar (SAR) image time series through January 2018 in the Swiss Alps. Debris avalanche zones are detected assuming that these areas are characterized by a significant and localized increase in SAR signal relative to the surrounding environment. We undertake a sensitivity study by calculating debris products by varying the D-M reference images (a stable reference image taken several weeks before the event). We examine the results according to the direction of the orbit, the characteristics of the terrain (slope, altitude, orientation), and also by evaluating the relevance of the detection with the help of an independent SPOT database by Hafner and Buhler including 18 737 avalanche events. Small avalanches are not detected by SAR images, and depending on the orientation of the terrain some avalanches are not detected by either the ascending or the descending orbit. The detection results vary with the reference image; best detection results are obtained with some selected individual dates with almost 70% of verified avalanche events using the ascending orbit. [ABSTRACT FROM AUTHOR]

Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. [ABSTRACT FROM AUTHOR]

To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. [ABSTRACT FROM AUTHOR]

The article presents the design of a Ka-band synthetic aperture radar (SAR) instrument based on innovative phased-array antennas, with steering capability in azimuth and elevation. To limit the complexity and costs associated with conventional large active arrays with regular layouts, a sparse array concept is presented. The proposed approach results in a 40% reduction in the number of active controls, with a marginal reduction in performance compared to conventional phasedarray solutions previously developed for SAR instruments. Additionally, a preliminary thermomechanical design is presented to assess the challenges associated with the industrial production and to identify options for accommodation on the spacecraft. [ABSTRACT FROM AUTHOR]

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence. [ABSTRACT FROM AUTHOR]

For more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis ofdisplacement field time series (DFTS)can provide useful information. Efficient data mining techniques are, thus, required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach, which handles confidence measures, is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from synthetic aperture radar data over Alpine glaciers) show the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

Efficient and low power utilizing clock synchronization is a challenging task for a wireless-sensor network (WSN). Therefore, it is crucial to design a light weight clock synchronization protocols for these networks. An adaptive clock offset prediction model for WSN is proposed in this paper that exchanges fewer synchronization messages to improve the accuracy and efficiency. Timing information required is collected by setting a small WSN set up to investigate the soil condition to control the irrigation in agriculture. The networks investigate soils moisture, temperature, humidity, and pressure content along with the sensors clock offset. First, the prediction model perceives the existing sensor clock offset to observe the clock characteristics and delay. Then, a Gaussian function is applied for adjusting the parameters weight of the observed value in the prediction model. The system results demonstrate that the proposed adaptive non-linear Gaussian regression synchronization model utilizes 20% less energy as consumed by time sync protocol for sensor-network and reference broadcast synchronization Protocol. It also reduces the synchronization error with respect to root-mean-square error (RMSE) by 24.85% as compared to linear prediction synchronization with RMSE 28.72% in terms of accuracy. [ABSTRACT FROM AUTHOR]

The status of Gangotri, one of the largest Himalayan glaciers, has been a matter of scientific debate and close scrutiny of the global hydrologic community. To comprehend the current state of Gangotri Glacier, we estimated it's geodetic mass budget and ice velocity using optical satellite data. The spatial distribution of ice thickness, using a laminar flow equation, and possible locations of glacial lake formation are also analyzed. The geodetic mass budget of Gangotri Glacier, estimated for the period of 1999−2014, yielded a value of −0.55 ± 0.42 mw.e.a−1. Substantial decrease in ice velocity is also noted for a nearly contemporaneous period. The average decrease in velocity for the 14 year (1999–2000 to 2013–14) observation period is computed to be 6.98 ± 6.53 ma−1. While the deceleration is less pronounced in the accumulation areas and along the tributaries, palpable slowdown (11.07 ± 6.53 ma −1) is observed along the main trunk of the glacier. The ice velocity maps produced are further used for computing glacier ice thickness and bedrock topography. Depressions in the bedrock topography are identified as potential sites for the formation of supraglacial lakes, as and when the snout retreats. In total, eight bed overdeepenings, with a maximum volume of \text56.57\pm \text8.93\times \text10^6\,\textm^3, have been identified and demarcated as potential sites for lake formation. The results presented here highlight increasing mass loss and reducing velocity patterns of the Gangotri Glacier. [ABSTRACT FROM PUBLISHER]

The synthetic aperture radar (SAR) offset tracking method has been widely used for multitemporal analysis of fast glacier movements in the polar region. However, it can be severely distorted, particularly in the case of L-band SAR systems mainly due to a frequent occurrence of ionospheric effects in the polar region. In this study, we developed an efficient method to extract and correct the ionospheric contribution from SAR offset tracking measurements. The method exploits an iterative directional filtering approach, which is based on the pattern and directionality of ionospheric streaks. The measurement performance of the proposed method was evaluated by using three L-band advanced land observing satellite phased array type L-band synthetic aperture radar pairs. Our results showed that the proposed correction achieved the improved measurement accuracies from 4.68–23.88 to 1.03–1.51 m/yr. It means that the accuracies of corrected measurements were about 5–16 times better than those of the original measurements. From the results, we concluded that our correction technique is highly suitable for the precise measurement of the glacier displacements even in the presence of strong ionospheric effects. Using the proposed method, the variations of glacier velocities were measured in the Vylki, Shury, and Kropotnika glaciers on Novaya Zemlya, which is located in the Russian Arctic Ocean, and the grounding zones were detected from the measurements in the Shury and Kropotnika glaciers. It further confirmed that the proposed correction method is allowed for the precise monitoring of glacier movements. However, in cases of severe ionosphere-distorted measurements, the proposed method may be limitedly applied. [ABSTRACT FROM PUBLISHER]

Accurate and instant information about changes of snow and glaciers covered areas plays a vital role in hydrological and climatological research and implications. Among all the observation methods, spaceborne remote sensing has a great advantage in monitoring the glaciers located in cold high-altitude regions and inaccessible areas on a large scale. Unlike optical sensors, the synthetic aperture radar (SAR) sensor can obtain images with low limitations in terms of weather phenomena and illumination as some glaciers frequently located in cloudy regions. In this paper, we propose a multiclasses classification method for large area glacier using spaceborne single-polarimetric SAR intensity image. The proposed method takes advantage of the discrimination ability of sparse representations of features, based on which a linear classifier called supervised neighborhood embedding is constructed. Finally, we develop a gradient descent method to alternatively update the dictionary and projection matrix. Two study areas are chosen to represent the discriminative characteristics of glaciers. In the Taku glacier in Alaska, compared to the state-of-the-art methods, our proposed method achieved a suitable performance with the overall classification accuracy of \text90.34\%, and especially for bare ice of \text91.38\%. In the Baltoro glacier in Karakoram characterized by high-relief topography and thick debris cover, the overall accuracy of \text72.63\% and debris accuracy of \text90.14\% are obtained. [ABSTRACT FROM PUBLISHER]

Supraglacial debris is widely distributed in the ablation zones of the glaciers in mountain valleys, and it influences glacier melting considerably. Polarimetric Synthetic Aperture Radar (SAR) presents promising results in terms of glacier classification and monitoring, but the scattering mechanism of debris has been unclear until now. In this paper, we attempted to verify the main scattering components of debris in the L- and C-bands polarimetric SAR images. A newly developed target decomposition method that is specially designed for debris is used to quantitatively analyze the scattering component. The method combines the X-Bragg surface scattering, double bounce, and completely random volume scattering models. The results from the target decomposition agree well with the scattering property analysis from the phase difference and entropy-alpha methods. The Keqikaer glacier, which is in the southern Tianshan Mountains, is selected as the study area. Phased-Array L-band SAR (PALSAR) images from the Advanced Land Observing Satellite (ALOS), PALSAR-2 images from ALOS-2, and RADARSAT-2 polarimetric SAR images are employed. The results show that in the C-band, surface scattering is dominant in debris, and it accounts for approximately 70% of the total power; in the L-band, volume scattering increases to a larger portion (approximately 40%), but remains slightly weaker than surface scattering (approximately 56%). [ABSTRACT FROM PUBLISHER]

ArcSAR is a ground-based synthetic aperture radar (GBSAR) that has recently been receiving increasing interest in the scientific literature. While the conventional GBSAR exploits the movement of an antenna along a linear rail to synthesize a large aperture, an ArcSAR exploits the spatial diversity of the data acquired by an antenna fixed to a rotating arm. The great advantage of ArcSAR is its capability to synthesize images at 360° with a constant resolution in azimuth. In this paper, the authors propose and test a new focusing algorithm that does not require to operate in the far field and neither with narrow beam antennas; moreover, it is flexible enough to focus on any plane (not necessarily on the rotation plane) as well as in the whole 3-D space. Furthermore, the authors demonstrate theoretically and experimentally that ArcSAR images can be affected by a “defocusing effect” of the targets far from the rotation plane, which has to be taken into consideration when designing such radars. [ABSTRACT FROM PUBLISHER]

Most of the applications of SAR polarimetry such as classification are based on estimation of the polarimetric covariance matrix. This estimation is generally done through a boxcar spatial filtering. This estimation process can induce mixture if different scatterers are present in neighboring pixels. Since the polarimetric entropy H is a measure of variability, this mixture can result in a very uniform entropy map. A nonlocal algorithm can be used to improve the estimation of the covariance matrices. The entropy maps are smoothed and contrast is better preserved. We propose a third estimation of H by using a temporal stack. Pixels are averaged on the time axis instead of on a spatial basis. On the datasets we studied, the temporal estimation increases the contrast of H maps. This contrast allows us to better discriminate targets. Temporal entropy is very influenced by the degree of coherence. Nevertheless, H\text{temporal} provides additional information, combining information about the polarimetric stability of scattering mechanisms over time. [ABSTRACT FROM PUBLISHER]

In this study, we investigated the use of synthetic aperture radar (SAR) polarimetry (Pol-SAR) and a supervised classification technique, support vector machine (SVM), for the classification of bare soil, ice, and snow, over the Ortles–Cevedale massif, (Eastern Italian Alps). We analyzed the importance of topographic correction on the backscattering and polarimetric SAR signature and the advantage of quad-pol with respect to dual-pol data. When backscattering values only are employed, the incidence angle used as input feature of the SVM classifier assures the best classification accuracy, 9.9% higher than the accuracy obtained with cosine corrected \gamma ^0 backscattering. The introduction of polarimetric features and decomposition parameters (such as Cloude–Pottier or Touzi decomposition parameters) increases the classification accuracy by 5.2% with respect to the backscattering case. The simulation of RADARSAT-2 data as Sentinel-1 like for dual-pol data shows a decrease of accuracy equal to 7.8% with respect to the fully polarimetric case (93.5%). The first Sentinel-1 image acquired on our test area was also employed for classification. We then tested the capability of C-band SAR to detect accumulation and ablation zones of the glaciers under the winter dry snow by setting up a multi-incidence angle and fully polarimetric SVM classifier, exploiting ascending and descending RADARSAT-2 data. In this case, the accuracy increased by 14.7% combining different geometric acquisitions (88.9%) with respect to the single geometry case. Finally, from the resulting classification maps, we extracted the snowline altitude for a sample of three glaciers, using both optical and SAR data, comparing the different products. [ABSTRACT FROM PUBLISHER]

Accurate mapping of glaciers is important for the management of water resources, studying glacier mass-balance, and monitoring glacier health. However, many glaciers are partially covered by debris of varying thicknesses, and these debris-covered glacier areas are difficult to distinguish clearly from the adjacent valley rock using optical remote sensing data due to their similar reflectance in the visible-to-near-infrared (VNIR) wavelength region. For better detection of the debris-covered glacier margin, a new method was proposed here which considers glacier terrain features (concave and convex landscapes) as well as VNIR reflectance, shortwave-infrared reflectance (SWIR), and thermal infrared (TIR) information. The proposed method uses several geomorphometric parameters (slope, plan, and profile curvature) and a Landsat Thematic Mapper (TM) band ratio image TM TIR/(TM NIR/TM SWIR) for mapping the Yengisogat glacier in the Shaksgam valley of China. The final debris-covered glacier boundary is generated by thresholding the band ratio image and overlaying it onto the result derived from cluster analysis performed using plan, profile curvature, and combined with mean of slope information. Evaluation of the final debris-covered glacier outline was carried out through comparison with GIS data from several reference data sets, and the final Yengisogat glacier outline had a difference of less than 1% in area compared to the reference data sets. [ABSTRACT FROM PUBLISHER]

Urban subsidence and landslides are among the greatest hazards for people and infrastructure safety and they require an especial attention to reduce their associated risks. In this framework, ground-based synthetic aperture radar (SAR) interferometry (GB-InSAR) represents a cost-effective solution for the precise monitoring of displacements. This work presents the application of GB-InSAR techniques, particularly with the RiskSAR sensor and its processing chain developed by the Remote Sensing Laboratory (RSLab) of the Universitat Politècnica de Catalunya (UPC), for the monitoring of two different types of ground displacement. An example of urban subsidence monitoring over the village of Sallent, northeastern of Spain, and an example of landslide monitoring in El Forn de Canillo, located in the Andorran Pyrenees, are presented. In this framework, the key processing particularities for each case are deeply analyzed and discussed. The linear displacement maps and time series for both scenarios are showed and compared with in-field data. For the study, fully polarimetric data acquired at X-band with a zero-baseline configuration are employed in both scenarios. The displacement results obtained demonstrate the capabilities of GB-SAR sensors for the precise monitoring of ground displacement phenomena. [ABSTRACT FROM PUBLISHER]

We present a fast method for deriving a general explicit closed-form expression of second-order perturbative solutions of the problem of radiation and propagation electromagnetic (EM) fields in a hybrid layered structure with an arbitrary number of rough interfaces and planar stratified medium based on the classical small perturbation method (SPM). The second-order SPM expression of the electric and magnetic fields in each region of the rough-layered structure is provided by theoretical analysis and fully formula derivation instead of considering any other approximation and equivalent process. In addition, we give the complete understanding of physical radiation and propagation mechanism of double-bounce second-order SPM solutions. Finally, the second-order SPM solutions derived by employing the proposed method are verified with existing methods and numerical results. What is more, we investigate completely the influence of various parameters of layered structure and configuration of radar system on EM scattering fields. [ABSTRACT FROM PUBLISHER]

Provides a listing of current staff, committee members and society officers. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]