Synthetic Aperture Radar

Trimble Terrasat as software and development engineer where he is working on high precision kinematic positioning using the future Galileo system. Dr. Ulrich Vollath received a Ph.D. in Computer Science from the Munich University of Technology (TUM) in 1993. At Trimble Terrasat -where he is working on GPS algorithms since more than eleven years -he is responsible for the Algorithm Development team. His professional interest is focused on high-precision real-time kinematic positioning and reference station network processing. Francisco Amarillo is the ESA/ESTEC technical officer responsible for the investigation on four carrier ambiguity resolution.

Agricultural Development combined with technology has made great progress in recent years, making it possible to improve the yield for farmers. This project combines deep learning algorithms with spraying technology to design a machine vision precision real-time targeting spraying system for field scenarios. Highly efficient mechanized nozzles are used to spray and apply nutrients and pesticides to crops so that farmers can increase production and mitigate the gap between supplies and demands. We employ high-speed visualization [8] to quantify the impact and evaporation of a droplet on a solid surface. This will also help us to identify the density/area covered by a single spray at a time and correct the delta part left to be covered at first work. This paper is focused on using image classification techniques with a computer vision algorithm to extract the parameters required from a single image at a time and convert it into structured data so that an unsupervised algorithm can cluster the regions based on density.

The article is devoted to the analysis of modern methods of monitoring main pipelines used to identify dangerous deformation processes that can disrupt the integrity of the pipeline. The paper discusses technologies for in-line inspection, fiber-optic sensors, and highly accurate geodetic observations using an electronic total station, satellite positioning systems, and radar interferometry (InSAR). Special attention is paid to integrated approaches, including a set of methods that allow automated data collection and processing using machine learning algorithms. A two-stage monitoring is proposed: regional, including the zoning of vast territories according to the degree of risk, taking into account natural and climatic conditions, man-made factors, and local, based on the analysis of deformations of the «ground surface-pipeline» system, performed through observations of points of geodetic deformation networks. The key advantages of the approach are adaptability to various operating conditions, optimization of resource intensity and early warning of defects.

The split-step solution to the parabolic wave equation describing beam propagation permits examination of the signal degradation in a Doppler weather radar caused by wind turbines blockage under general atmospheric conditions and at arbitrary transmitter and receiver configurations. At radar wavelengths, an understanding of turbine and terrain obscuration effects is essential for deciding the reliability of radar measurements affected by blockage.

Remote sensing studies of vegetation cover and hydrologic dynamics in Amazonian wetlands have been mostly limited temporally or spatially, and the distribution and spatial configuration of Amazonian va ´rzea habitats remains poorly known. This study uses multitemporal PALSAR L-band radar imagery combined with object-based image analysis, data mining techniques and field data to derive vegetation structure and inundation patterns and characterize major vegetation types in va ´rzea forests of the Mamiraua ´Sustainable Development Reserve. Our results show that the combination of vegetation cover and inundation extent information can be a good indicator of the complex gradient of habitats along the floodplain. The intersection between vegetation and flood duration classes showed a wider range of combinations than suggested from field based studies. Chavascal areas-chacaracterized as a dense and species-poor shrub/tree community developing in old depressions, abandoned channels, and shallow lakeshad shorter inundation periods than the usually recognized hydroperiod of 180-240 days of flooding, while low va ´rzea-a diverse community that have fewest and smallest species, and highest individual density and that tolerate 120-180 days of flooding every year-was distributed between flood duration ranges that were higher than reported by the literature. Forest communities growing at sites that were never mapped as flooded could indicate areas that only flood during extreme hydrological events, for short periods of time. Our results emphasize the potential contribution of SAR remote sensing to the monitoring and management of wetland environments, providing not only accurate information on spatial landscape configuration and vegetation distribution, but also

We introduce the S M Nazmuz Sakib Canonical Cumulant Ratio Constant (abbrev. Sakib Constant) for canonical ensembles: for total energy E at inverse temperature β, with energy cumulants κ n = κ n (E), Ξ := κ 2 3 κ 2 κ 4. We prove the S M Nazmuz Sakib Homogeneous Hamiltonian Theorem: for classical systems whose Hamiltonian is a sum of homogeneous kinetic and potential terms, the canonical law of E is Gamma, implying the exact universal value Ξ = 2 3 at all N and T. We further formulate the S M Nazmuz Sakib Tauberian Universality Hypothesis: whenever the density of states satisfies a local regularvariation ansatz g(E) ∼ A E k-1 L(E) with slowly varying L, the canonical energy distribution is asymptotically Gamma and Ξ → 2 3 as the effective shape k → ∞. We provide schematic computational evidence and a reduction to standard frameworks (partition-function scaling, regular variation, and large deviations). Connections to classical thermodynamics and rigorous statistical mechanics are discussed with references.

Images obtained with coherent illumination, as is the case of sonar, ultrasound-B, laser and Synthetic Aperture Radar -SAR, are affected by speckle noise which reduces the ability to extract information from the data. Specialized techniques are required to deal with such imagery, which has been modeled by the G 0 distribution and under which regions with different degrees of roughness and mean brightness can be characterized by two parameters; a third parameter, the number of looks, is related to the overall signal-to-noise ratio. Assessing distances between samples is an important step in image analysis; they provide grounds of the separability and, therefore, of the performance of classification procedures. This work derives and compares eight stochastic distances and assesses the performance of hypothesis tests that employ them and maximum likelihood estimation. We conclude that tests based on the triangular distance have the closest empirical size to the theoretical one, while those based on the arithmetic-geometric distances have the best power. Since the power of tests based on the triangular distance is close to optimum, we conclude that the safest choice is using this distance for hypothesis testing, even when compared with classical distances as Kullback-Leibler and Bhattacharyya.

Coastal areas have the potential to prosper the surrounding community. The coastline is the boundary line between land and sea level that is dynamic. Changes in the coastline can lead to abrasion and accretion. This study aims to determine changes in the coastline on the Situbondo coast for a period of 10 years by taking Landsat 8 satellite image data processed using ArcGIS 10.7. The method used in this study uses the calculation of the Haversin formula and Euclidean Distance. The results showed that the smallest shoreline change in Situbondo Regency over a period of 10 years occurred in transect 24, precisely in Agel Village, Jangkar District with a shoreline change rate of 1.11 meters. While the largest 10-year shoreline change in Situbondo Regency occurred in transect 25, precisely in Kumbangsari Village, Jangkar District with a shoreline change rate of 106.42 meters.

From a geological standpoint, northern Pakistan is one of the most active and unstable areas in the world. As a consequence, many massive landslides have occurred in the area in historical times that have destroyed infrastructure, blocked the Hunza River, and damaged the Karakoram Highway repeatedly. However, despite the high frequency of large magnitude landslide events, and the consequent damages, the entire area is largely understudied, mainly due to the difficult logistics and the large distances involved. This work is aimed at applying the potential use of Interferometric Synthetic Aperture Radar (InSAR) for landslide identification and investigation for the Hunza-Nagar Region. Sentinel-1 images covering a period of more than two years (February 2017–August 2019) were used and processed by adopting the small baseline subset (SBAS) method. The obtained deformation rate measured along the line of sight (VLOS) varies from −114 to 20 mm/year. The downslope velocity deformation rate...

The Imaging Wind and Rain Arborne Profilers OWRAP) is a dual-frequency, conically-scanning Doppler radar that measures high-resolution, dual-polarized, multi-beam C-and Ku-band reflectivity and Doppler velocity profiles of the atmospheric boundary layer (ABL) within the inner core of hurricanes. From the datasets acquired during the 2002 through 2UO5 hurricane seasons as part of the ONR Coupled Boundary Layer Air-Sea 'Itansfer (CBLAST) program and the NOAAfl'iESDIS Ocean Winds and Rain experiments, very high resolution radar observations of hurricanes have been acquired and made available to the CBLAST community. Of particular interest am the ABL wind fields and 3-D structures found within the inner core of hurricanes. As a result of these analysis, a limitation in the ability to retrieve the ABL wind field at very low altitudes was identified. This paper shows how thii limitation has been removed and presents initial results demonstrating its new capabilities to derive the ABL wind field within the inner a r e of hurricanes to much lower altitudes than the ones the original system was capable of.

The first three of a series of new generation satellites operating at L-band microwave frequencies have been launch in the last decade. L-band is particularly sensitive to the presence of water content in the scene under observation, being considered the optimal bandwidth for measuring the Earth's global surface soil moisture (SM) over land and sea surface salinity (SSS) over oceans. Monitoring these two essential climate variables is needed to further improve our understanding of the Earth's water and energy cycles. Additionally, remote sensing at L-band has been proved useful for monitoring the stability in ice sheets and measuring sea ice thickness. The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission specifically launched to monitor SM and SSS. It carries on-board a novel synthetic aperture radiometer with multi-angular and full-polarization capabilities. NASA's Aquarius (2011-2015) was the second mission, devoted to SSS monitoring wi...

The local polynomial Fourier transform (LPFT) based algorithm for autofocusing SAR images has recently been proposed by the authors. It produces a well focused image of moving targets, without defocusing stationary targets or inducing undesired cross-terms. The drawback of this algorithm is its high computational burden caused by the large number of elements in the set of used chirp-rates. We propose an algorithm with decreased number of elements used for the LPFTbased SAR imaging. The product high-order ambiguity function (PHAF) is applied to estimate parameters of a radar signal. The estimated chirp-rate is used as an initial value for forming the set of chirp-rates. The proposed algorithm has significantly smaller set of chirprate values (tens comparing to several hundreds or thousands used in the previous algorithm version). In this manner, the calculation complexity is significantly reduced. The proposed procedure is fully automated, meaning that it follows the change of motion parameters. In addition, our procedure considers the third-order phase compensation.

A cubic-phase function evaluation technique for multicomponent FM signals with non-overlapped components in the TF plane is proposed. The proposed technique is based on the STFT. Cross-terms are removed or reduced in the same manner as in the case of the TF representation called the S-method. The proposed technique is applied for visualization of signals in time-chirp-rate plane and parameter estimation of analytical and radar signals. In addition, a procedure for focusing SAR images by using estimated parameters is proposed in order to verify obtained results.

Recently, an L-statistics based method for the micro-Doppler effects removal has been proposed by the authors. Order statistics is performed on the spectrogram, while the rigid body signal synthesis is done by using the remaining STFT samples, after micro-Doppler removal. By the proposed method, the Fourier transform is recovered with a concentration close to the original one. However, during the procedure of the micro-Doppler removal, the STFT samples that correspond to the rigid body are retracted, as well. Consequently, in the reconstructed Fourier transform of the rigid body, we get one very highly concentrated pulse, as in the original Fourier transform, and a number of low-concentrated components, being spread around the peak. These low concentrated components are summed up by different random phases. In this paper, we propose a genetic algorithm for the estimation of the removed STFT samples corresponding to the rigid body. Each individual in the genetic algorithm contains possible estimation of the phases of the missing STFT samples, whereas fitness function forces individuals (combination of phases) for which a minimal energy of the side lobs is obtained. The individual with the highest fitness is considered as the final phases' estimation of the missing STFT values, and then used for the reconstruction of the original Fourier transform. The amplitude of a STFT sample is estimated as median of the amplitudes of the remaining samples at the same frequency. Performance of the proposed genetic algorithm is illustrated by examples.

We propose the adaptive S-method-based technique for imaging of SAR and ISAR targets. This approach can be applied in the 1D and 2D modes. It is a postprocessing technique, since the first stage is the standard radar imaging with the 2D Fourier transform. In addition, selection of the adaptive parameter in this technique is efficient and can be performed based on simple rules in real time. The proposed technique produces highly concentrated (focused) radar image without interferences commonly associated with time-frequency representations and without defocusing target images that are already focused in the 2D Fourier domain.

For target recognition applications, a blurred ISAR image has to be refocused quickly so that it can be used for real-time target identification. In this paper, we present the Smethod-based approach to real-time motion compensation, image formation and image enhancement of moving targets in ISAR. This approach performs better than the Fourier transform by drastically improving images of fast, maneuvering targets. The method is also computationally simple, requiring only slight modifications to the existing Fourier transformbased algorithm.

Recently, an L-statistics based method for a successful micro-Doppler effects removal and reconstruction of the rigid body in ISAR/SAR has been proposed. However, during the procedure of the micro-Doppler removal, a part of the shorttime Fourier transform samples that correspond to the rigid body is eliminated, as well. Consequently, in the Fourier transform of a rigid body, reconstructed based on the remaining short-time Fourier transform samples, one highly concentrated peak is obtained, as in the original Fourier transform, with a number of undesired low-concentrated components, being spread around this peak. In this paper, we propose a procedure for analytical reconstruction of the removed short-time Fourier transform samples that correspond to the rigid body. The proposed procedure leads to a highly concentrated Fourier transform (ISAR/SAR image) obtained by summing over time the resulting short-time Fourier transform filled up with the estimation of the missing rigid body related samples.

A wavelet based technique for multiple point targets detection in InfraRed image sequences in presence of clutter is proposed. Most existing approaches assume target of several pixels size or of Gaussian shape with variance of 1.5 pixel. We develop detection and tracking algorithm for single pixel targets. We also propose a modified pipeline algorithm for tracking multiple single pixel targets with large motion (### pixels) in an Infrared image with occlusion due to IR clouds and background noise.

Landslide is a hazard that threaten the people who lives in the mountain area, it comes active especially rainy seasons and causes a large number of casualties every year. The movement of the slope is an indicator of activity of the landslide, it is helpful to capture the precursor of the activity, the monitoring of the movement of the slope is very important. However it is a difficult problem due to complex topography, cloudy and rainy weather for optical sensors, In this paper we will show the capability of up-to-date Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) data in monitoring the movement of the landslide which located in south China, which can capture the fast and slow movement with different spatial and temporal baseline combination, the results shows that the L-band SAR data has its advantage in monitoring the movement of the landslides especially in the low latitude, cloudy and rainy area.

The successful application of the SAR Interferometry (InSAR) for interpretation of land subsidence, especially in coal mining areas is described in this paper. The set of interferograms was collected in Poland over Upper Silesian Coal Basin, where subsidence causes damages to buildings and changes of surface drainage patterns in a densely urbanized area. InSAR is a technique, which uses the phase difference between the radar echoes from repeated SAR (Synthetic Aperture Radar) images of the same area. InSAR is now a unique method, which gives us possibility to take a measurement of land subsidence on a strict determined huge area of tenths thousand square kilometers and a precision state in time range. Interferometric image of land subsidence over the SAR observation period of 35 and 70 days shows the subsidence increment on advancing slope of subsidence through. Elliptic-shape area of the presence of subsidence is high correlated with the extent of the front of underground mining. I...

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar...

This paper describes a new architecture for synthetic aperture radar (SAR) automatic target recognition (ATR) based on the premise that the pose of the target is estimated within a high degree of precision. The advantage of our classifier design is that the input space complexity is decreased with the pose information, which enables fewer features to classify targets with a higher degree of accuracy. Moreover, the training of the classifier can be done discriminantely, which also improves performance and decreases the complexity of the classifier. Three strategies of learning and representation to build the pattern space and discriminant functions are compared: Vapnik's support vector machine (SVM), a newly developed quadratic mutual information (QMI) cost function for neural networks, and a principal component analysis extended recently with multiresolution (PCA-M). Experimental results obtained in the MSTAR database show that the performance of our classifiers is better than that of standard template matching in the same dataset. We also rate the quality of the classifiers for detection using confusers, and show significant improvement in rejection.

On September 7, 1999 a magnitude M W =5.9 earthquake occurred in the Athens area (Greece) producing a subsidence of about 6 cm detected by radar interferometry. Using seventeen ERS SAR images acquired between December 1997 and January 2001, one hundred thirty six interferograms of the area have been calculated using the CNES DIAPASON software. Among them, eighty-one have been selected on the basis of coherence and used in the processing. These interferograms have been used to produce a collective coherence map and they have been unwrapped using the SNAPHU software. Orbital effects have been reduced by tilting and shifting the unwrapped interferograms, making the assumption that no deformation has occurred in zones located at the edges of the scene, which are far away from the area affected by the earthquake. This procedure was applied constraining the deformation value of the most coherent pixels in the selected zones to a zeroed value. Twenty-four co-seismic, five pre-seismic and eight post-seismic interferograms have been finally selected on the basis of the coherence of the phase signal. The co-seismic interferograms have been stacked to produce a smoothed image of the average deformation field. The resulted deformation field is used to recalculate the deformation source and to produce a new model, which is found to be close to that proposed by The synthetic interferogram issued from the model is then subtracted from each co-seismic interferogram. The corrected residuals of the co-seismic interferograms and the corrected pre-and postseismic interferograms are finally examined to detect any temporal evolution of the deformation before and after the main event.

Cubesats platforms expansion increases the need to simplify payloads and to optimize downlink data capabilities. A promising solution is to enhance on-board software, in order to take early decisions automatically. However, the most efficient methods for data analysis are generally large deep neural networks (DNN) oversized to be loaded and processed on limited hardware capacities of cubesats. To use them, we must reduce the size of DNN while accommodating efficiency in terms of both accuracy and inference cost. In this paper, we propose a distillation method which reduces image segmentation deep neural network's size to fit into on board processors. This method is presented through a ship detection example comparing accuracy and inference costs for several networks.

Glacier-surface displacements produced by geothermal and volcanic activity beneath Vatnajökull ice cap in Iceland are described by field surveys of the surface topography combined with interferograms acquired from repeat-pass synthetic aperture radar images. A simple ice-flow model serves well to confirm the basic interpretation of the observations. The observations cover the period October 1996–January 1999 and comprise: (a) the ice-flow field during the infilling of the depressions created by the subglacial Gjálp eruption of October 1996, (b) the extent and displacement of the floating ice cover of the subglacier lakes of Grímsvötn and the Skaftá cauldrons, (c) surface displacements above the subglacier pathways of the jökulhlaups from the Gjálp eruption site and the Grímsvötn lake, (d) detection of areas of increased basal sliding due to lubrication by water, and (e) detection of spots of temporal displacement that may be related to altering subglacial volcanic activity. At the d...

This research project report is dedicated to my family members, for believing in me and inspiring me, your love, friendship, has made a difference in my life. I would not be who I am without the seeds you have sown in my life. v Table of Contents DECLARATION .

Both spatial and non-spatial data are critical to a country's social and economic development. Land use and land cover (LULC) maps are data that can be used to retrieve land information and detect environmental changes. Several recent studies have used deep learning approaches to generate LULC maps from various remote-sensing image formats. They have correctly recognized several land cover elements such as built-up areas, forests, water, etc. On the other hand, spaceborne radar images must be used to test deep learning methods because radar sensors operate in all weather and lighting circumstances and have diverse image characteristics depending on the wavelength. Although many researchers have applied deep learning techniques to classify the land cover features, most of them need to do more by taking less or more several land cover features and taking proper evaluation metrics to justify the classification techniques. The current research shows segmentation on Sentinel-1 C-band...

The phenomenon of land subsidence has emerged as a critical issue in Semarang, Indonesia, particularly within its northern coastal regions. This issue is primarily attributed to the rapid urbanization and extensive infrastructure development in the area. The present study employs the Interferometric Synthetic Aperture Radar (InSAR) technique, a proven method for effectively monitoring subsidence patterns, to analyze changes occurring between 2020 and 2024. The findings indicate significant subsidence within the northern coastal zones, with rates reaching up to -0.23 meters per year. The primary drivers of this subsidence are identified as excessive groundwater extraction, the presence of soft alluvial soils, and accelerated urban development. The impacts of this land subsidence are multifaceted, including exacerbated flooding risks, disruption of local infrastructure, and increased challenges for urban planning and development. These findings underscore the urgency of implementing mitigation strategies, such as stricter regulation of groundwater extraction and the development of resilient infrastructure designed to adapt to these changing conditions. Furthermore, the study highlights the necessity for long-term, sustainable strategies to address ongoing subsidence and its associated risks, emphasizing the importance of safeguarding both coastal and inland areas of Semarang. These insights contribute to the broader understanding of urban subsidence and its implications, offering a foundation for developing effective management and adaptation measures in similar rapidly urbanizing regions.

In this work, an innovative methodology to generate the automatic ground motion areas mapping is presented. The methodology is based on the analysis of the Synthetic Aperture Radar (SAR)-based displacement time series. The procedure includes two modules developed using the ModelBuilder tool (ArcGis). These modules allow to identify the ground motion areas (GMA) using only one dataset and the persistent GMA (PGMA) considering the different monitored periods and datasets. These areas represent clusters of targets characterized by the same displacement time series trend. The procedure was tested using different sensors such as ERS-1/2, ENVISAT,

Accurate digital terrain models (DTMs) are essential for a wide range of geospatial and environmental applications, yet their derivation in forested regions remains a significant challenge. Existing global DTMs, typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar (InSAR), fail to accurately capture understory terrain due to limited penetration capabilities, resulting in elevation overestimation in densely vegetated areas. While airborne light detection and ranging (LiDAR) can provide highaccuracy DTMs, its limited spatial coverage and high acquisition cost hinder large-scale applications. Thus, there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models (DSMs). In this study, we propose a simple, interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to construct a tree height surface model, which is then subtracted from the stereo-derived DSM to generate the final DTM. By directly incorporating LiDAR constraints, the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs, ensuring ease of implementation and broad applicability. In contrast to machine learning-based terrain modeling methods, which are often prone to overfitting and data bias, the proposed approach is simple, interpretable, and robust across diverse forested landscapes. The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model (DEM) and the forest and buildings removed DEM (FABDEM), a global bare-earth elevation model corrected for vegetation bias. The results indicate that the proposed DTM consistently outperforms the Copernicus DEM (CopDEM) and achieves accuracy comparable to FABDEM. In addition, its finer spatial resolution of 1 m, compared to the 30 m resolution of FABDEM, allows for more detailed terrain representation and better capture of fine-scale variation. This advantage is most pronounced in gently to moderately sloped areas, where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM. The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints, offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable. To illustrate the potential utility of the proposed DTM, we applied it to a fire risk mapping application based on topographic parameters such as slope, aspect, and elevation. This case highlights how improved terrain representation can support geospatial hazard assessments.

Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two...

High mountain terrains, with steep slopes and deep valleys, are generally challenging areas to monitor using satellite earth observation techniques since the terrain creates perspective distortions and differences in illumination that can occlude or obfuscate a significant proportion of the land. This is particularly prominent in synthetic aperture radar (SAR) data, where the oblique geometry can result in large areas of layover and shadow, which must be excluded from any analysis. Interferometric SAR (InSAR) is an established technique for monitoring ground motion and this study assesses its potential for geohazard monitoring in mountainous areas using Lake Sarez in Tajikistan as a case study, applying SAR data from the Sentinel-1 mission. It is shown that, although the effect of layover and shadow is severe, a judicious combination of ascending and descending satellite passes is still capable of surveying 88% of the land surface. It is also demonstrated that, through the use of an...

Seismic monitoring of a geothermal reservoir is feasible, but it is necessary to design the seismic experiment carefully in order to be able to observe changes in the field. Travel time differences due to changes in the geothermal field with time are estimated to be small – on the order of 10-20 milliseconds. Changes in P velocity and Poisson’s ratio over time are large, but occur over small regions of less than a few hundred meters extent leading to the predicted small changes in travel times. Seismic experiments should therefore concentrate on the areas where these differences would be observed. Such changes are likely to be observable in cross-hole surveys, particularly near the regions of injection and the edge of the expanding two-phase zone. The largest effect is due to the presence of steam, which causes a sharp drop in P-velocity, an even sharper drop in Poisson’s ratio, strong P-wave attenuation, and seismic reflections from the water/steam boundary. It should, therefore, b...

Monitoring of ground surface deformation is important for determining and implementing effective measures against landslide disasters. Synthetic aperture radar interferometry(InSAR)is a promising monitoring technique that can detect surface deformation in wide areas at the centimeter scale. The present study aimed to interpret landslide deformation using InSAR images that cover the Shimekake landslide area in Yamagata Prefecture, Japan. The study area consists of gentle slopes(approx. １ ０°)facing southwest direction and predominantly lies on Neogene mudstone. Landslides in the area were triggered by snowmelt. InSAR images were produced from Advanced Land Observing Satellite(ALOS) ／Phased Array L-band SAR(PALSAR)data that were measured from ascending／descending orbit(viewed above from west／east) . For detecting landslide deformations, we selected the data measured between June ２ ０ ０ ６ and October ２ ０ ０ ８ because it displayed the best coherence among the available archived PAL-SAR data. Previous ２． ５-dimensional analysis revealed east-west and vertical displacement using InSAR images obtained from both ascending(northward)and descending(southward)orbits. We compared the displacement estimated by InSAR with that measured by GPS between December ２ ０ ０ ９ and March ２ ０ １ ０. The comparison revealed that westward displacement in the entire landslide area and subsidence at the landslide scar identified by InSAR was coincident with that of the GPS measurements. Although no GPS data were available between June ２ ０ ０ ６ and October ２ ０ ０ ８, it is thought that the same landslide deformation has continued since June ２ ０ ０ ６. Therefore, we concluded that the InSAR images successfully detected landslide deformation in the study area. Notably, however, uplifting displacement detected at the foot of the landslide was not coincident with that of the GPS measurements, and the discrepancy was possibly due to a partial phase-unwrapping error in the InSAR image obtained from the ascending orbit. Despite the detection of false displacement, it was found that InSAR can serve as an effective reconnaissance tool for the monitoring of subtle landslide deformations over wide areas and is expected to support the location planning of GPS survey equipment in the field for effective landslide deformation measurements.

The power of interferometric Synthetic Aperture Radar (SAR) when applied to studies of crustal deformation has been fully demonstrated by Massonnet et al. (1993Massonnet et al. ( , 1994) ) and Zebker et al. (1994) for the Landers, California earthquake of June 28, 1992. This method is unique in its capability of providing a two-dimensional representation of a deformation with a dense spatial coverage over most of the globe. Since 1994, the Geographical Survey Institute (GSI) has been conducting a study on applications of differential InSAR (Interferometric SAR) for the detection of crustal deformations associated with earthquakes and volcanic activities. Crustal deformations of many episodes were mapped by InSAR, such as those due to the 1994 Northridge earthquake, the 1995 Hyogo-ken Nanbu earthquake, the 1995 Neftegorsk earthquake, and volcanic deformations of Mt. Iwate and Mt. Usu. These interferograms played an important role in constructing geophysical models.

The 2007 Chuetsu-oki earthquake occurred just west off the coast of Kashiwazaki in Niigata Prefecture, Central Japan on July 16, 2007. The permanent GPS network (GEONET) clarifies the coseismic displacement as a thrust faulting whose compressional axis lies in the NW-SE direction. Interferometric analysis of synthetic aperture radar (SAR) images acquired by “Daichi” satellite (ALOS) maps a detailed spatial pattern of the displacement toward the satellite for both ascending and descending orbits. Peak-to-peak displacement reaches approximately 400 mm in the descending orbit interferometric (In)SAR data. Repeated precise leveling shows uplift near the northeast part of the aftershock area and subsidence near the southwestern part. We construct a preliminary fault model by inverting the observed deformation. The preferred model consists of two segments of rectangular faults whose moment magnitude is 6.7 in total. From only the used geodetic data on land, it is difficult to determine wh...

Monitoring water level changes is necessary to manage, conserve and restore natural, and anthropogenic lake systems. However, the in‐situ monitoring of lake systems is unfeasible due to limitations of costs and access. Furthermore, current remote sensing methods are restricted to large lakes and low spatial resolutions. We develop a novel approach using subsequential pixel‐wise observations of the Sentinel‐1B sensor based on interferometric synthetic aperture radar to detect water level changes in small lakes. We used 24 small ungauged lakes of the Cajas Massif lake system in Ecuador for development and validation. We found Differential Interferometric Synthetic Aperture Radar (DInSAR)‐derived water level changes across lakes to be consistent with precipitation, capturing the peak of the wet seasons. Furthermore, accumulated water level changes could be explained by differences in lake area among lakes. Although with limitations, this study shows the underutilized potential of DInSA...

Synthetic aperture radars (SAR) are a type of high-resolution radars that are used for terrestrial and airborne imaging. They use a moving source to simulate a large antenna aperture, thereby providing a high-quality map of the surroundings. They are popular due to their wide variety of applications in airborne naval and defense systems as well as topography and seismology. In defense applications, they are mainly used for reconnaissance and mapping of enemy targets such as armored vehicles, tanks, and runways. In recent times, there has been a heavy need for machine learning algorithms that are capable of automatically recognizing the targets detected by the SAR without human input. Such algorithms demand large-scale labeled datasets for training, validation and testing. Due to the nature of the SAR, acquisition and collection of these labeled datasets is quite a tedious and expensive task requiring a lot of man hours. Thus, alternate methods to produce new SAR image data are needed. In this paper, generative adversarial networks (GAN) are used to generate new spotlight SAR images from a limited pre-existing dataset. To quantitatively verify the effectiveness of the images produced by the GAN, a novel convolutional neural network (CNN) is devised and trained on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset images. Over the years, this problem of deficient datasets has been tackled in multiple ways. An approach described by Fawzi This article is part of the topical collection "Computational Statistics" guest edited by

Soil moisture is an essential factor that influences agricultural productivity and hydrological processes. Soil moisture estimation using field detection methods takes time and is challenging. However, using Remote Sensing (RS) and Geographic Information System (GIS) technology, soil moisture parameters become easier to detect. In microwave remote sensing, synthetic aperture radar (SAR) data helps to retrieve soil moisture from more considerable depths because of its high penetration capability and the illumination power of its light source. This study aims to process the SAR Sentinel-1A data and estimate soil moisture using the Water Cloud Model (WCM). Many physical and empirical models have been developed to determine soil moisture from microwave remote sensing platforms. However, the Water Cloud Model gives more accurate results. In this study, the WCM model is used for mixed crop types. The experimental soil moisture was determined from in-situ soil samples collected from various agricultural areas. The soil backscattering values corresponding to the different soil sampling locations were derived from Sentinel SAR data. Using linear regression analysis, the laboratory's soil moisture results and soil backscattering values were correlated to arrive at a model. The model was validated using a secondary set of in-situ moisture content values taken during the same period. The R2 and RMSE of the model were observed to be 0.825 and 0.0274, respectively, proving a strong correlation between the experimental soil moisture and satellite-derived soil moisture for mixed crop field types. This paper explains the methodology for arriving at a model for soil moisture estimation. This model helps to recommend suitable crop types in large, complex areas based on predicted moisture content.

Table of Contents 1 Chapter One modeling 7. Parameter estimation of Weibull distribution 8. Comparison among Normal, Lognormal and Weibull distribution in tree Diameter and Height distribution modeling Chapter Three Material and methods Site description Species description Sampling design Data collection and measurement Data analysis 1. Fitting normal and lognormal distribution 2. Estimation of Weibull parameters 2.1.Maximum likelihood method 2.2. Moments method 3. Model evaluation Chapter Four Results Descriptive statistics of tree diameters Diameter distribution models Model evaluation References

The main goal of this research is the activity state verification of existing landslide inventory maps using Persistent Scatterer Interferometry (PSI). The study was conducted in Małopolskie municipality, a rural setting with a sparse urbanization in Polish Flysch Carpathians. PSI have been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR, and Sentinel 1A/B from different acquisition geometry (ascending and descending orbit) to increase PS coverage and overcome geometric effects due to layover and shadowing. The Line-Of-Sight PSI measurements were projected to the steepest slope, which allows to homogenize the results from diverse acquisition modes and to compare displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damages maps were generated and verified during filed investigations. High correlation between PSI results and in-situ damage observations has been confirmed. Activity state and landslide-related expected damage map have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by Sentinel satellite 1A/B data allows increasing of the PS density significantly. The study substantiates usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas.

With the development of Synthetic Aperture Radar (SAR) in terms of multi-band, multi-polarization and high-resolution data, space radar remote sensing for archaeology has become a potential field for research. Nevertheless, the archaeological detection capability of this technology has so far not been fully assessed. This paper is a pioneering effort to assess the potential of satellite SAR X-band data in the detection of archaeological marks. We focus on the results obtained from a collaborative contribution jointly carried out by archaeologists and remote sensing experts in order to test the use of COSMO-SkyMed data in different contexts and environmental conditions. The methodological approaches we adopted are based on two different feature-enhancement procedures: (i) multi-temporal