Configuration, Orbit Design of InSAR Formation Based on Mean Elements

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Coal mine subsidence hazard can be effectively evaluated by geographic information system (GIS) analysis if sufficient data is provided. It is, however, difficult to obtain ground-based data, especially in remote and less populated mining areas. In this study, we construct and validate a coal mine subsidence hazard map in Taebaek, Korea, by integration of space-borne L-band synthetic aperture radar (SAR) measurements and a fuzzy-based subsidence model. There is an approximately 15-year time interval between the radar measurements used for the subsidence hazard model and those used to validate the subsidence. A subsidence hazard map was constructed using Japanese Earth Resources Satellite (JERS-1) SAR data from the early 1990s and the subsidence model. For the coal mine subsidence hazard mapping, a certainty factor analysis was used to estimate the relative weights of four control factors influencing coal mine subsidence, and the relative weight of each factor was then integrated to produce a subsidence hazard index by a fuzzy combination operator. The predicted hazard areas were then investigated and validated by comparison with subsidence occurrences observed by Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) interferometry in 2007–2008. The results showed a good agreement between the predicted locations vulnerable to subsidence and the actual subsidence occurrences with a prediction accuracy of about 73% and a root mean square error of about 0.35. These results demonstrate that a map produced by integration of a subsidence model and SAR interferometry can be used to predict and monitor coal mine subsidence hazards, especially in remote regions.

Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

A comprehensive understanding of the spatial distribution of urban settlements is significant to a series of research topics related to environmental and biological changes caused by the urbanization process. Among various technologies, Synthetic Aperture Radar (SAR) has been successfully applied in urban settlement mapping in the past two decades. Although much effort with varying degrees of success has been made in previous studies, the research work is still ongoing, and three challenges should be highlighted. First, the effect of de-speckling is usually underestimated, to the extent that the improvement of the SAR image quality is totally ignored in some studies. Second, a method that combines full Interferometric SAR (InSAR) information is as yet missing. Third, training samples are generally required to process SAR images to extract urban settlements, which is time-consuming and labor-intensive, or even impractical when classifying satellite data at the regional/global scale. To address these issues, this paper presents an automatic method for urban settlement mapping trained by multi-temporal InSAR using social media. To improve the detection performance and reduce false alarm ratio, intensity and coherence are first accurately estimated without loss of image resolution by homogeneous pixel selection and robust estimators. The homogeneous pixels will be also applied to measure urban characteristics from the geometrical prospective. After that, training samples are automatically generated from social media based on the fact that cities and urban areas are nowadays full of individual geo-referenced data such as social network data Finally, these multiple information sources will be fused to extract urban areas based on an improved one class classifier. Experimental results show that the proposed method is effective in extracting urban areas with good accuracy. This study provides a new de-speckling means to process multi-temporal InSAR and sheds new light on the applications of social media in the field of remote sensing.

Synthetic aperture radar (SAR) has been an unparalleled tool in cloudy and rainy regions as it allows observations throughout the year because of its all-weather, all-day operation capability. In this paper, the influence of Wenchuan Earthquake on the Sichuan Giant Panda habitats was evaluated for the first time using SAR interferometry and combining data from C-band Envisat ASAR and L-band ALOS PALSAR data. Coherence analysis based on the zero-point shifting indicated that the deforestation process was significant, particularly in habitats along the Min River approaching the epicenter after the natural disaster, and as interpreted by the vegetation deterioration from landslides, avalanches and debris flows. Experiments demonstrated that C-band Envisat ASAR data were sensitive to vegetation, resulting in an underestimation of deforestation; in contrast, L-band PALSAR data were capable of evaluating the deforestation process owing to a better penetration and the significant coherence gain on damaged forest areas. The percentage of damaged forest estimated by PALSAR decreased from 20.66% to 17.34% during 2009–2010, implying an approximate 3% recovery rate of forests in the earthquake impacted areas. This study proves that long-wavelength SAR interferometry is promising for rapid assessment of disaster-induced deforestation, particularly in regions where the optical acquisition is constrained.

Online Material: Additional figures and tables. On 20 May 2012, an M L 5.9 earthquake (Table 1) occurred near the town of Finale Emilia, in the central Po plain, Northern Italy (Fig. 1a). The mainshock caused seven casualties and the collapse of several historical buildings and industrial sheds. The earthquake sequence continued with diminishing aftershock magnitudes until 29 May, when an M L 5.8 earthquake occurred near the town of Mirandola, ∼12 km west‐southwest of the mainshock (Scognamiglio et al. , 2012). This second mainshock started a new aftershock sequence in this area, and increased structural damage and collapses, causing 19 more casualties and increasing to 15,000 the number of evacuees. View this table: Table 1 Largest Earthquakes ( M L>5) of the 2012 Emilia Seismic Sequence (http://iside.rm.ingv.it) Figure 1. (a) Geostructural setting of the Emilia 2012 seismic sequence area showing the vertically projected top traces of the main thrust fault planes, and the anticlinal axes, modified after (Bigi et al. , 1983; Boccaletti et al. , 2010; Picotti and Pazzaglia, 2008); white dots, aftershock epicenters. Large instrumental and historical earthquakes are also shown (http://iside.rm.ingv.it, last accessed April 2013); Rovida et al. , 2011); yellow rectangles, footprint of the COSMO‐SkyMed (CSK) and Radarsat satellite image pairs. The focal mechanisms of the two main events (from Malagnini et al. , 2012) are reported in the map. (b) Geological profiles interpreted from seismic reflection data after (Boccaletti et al. , 2010) corresponding to traces A and B in (a). Shortly after the first mainshock, the Department of Civil Protection (DPC) activated the Italian Space Agency (ASI), which provided post‐seismic Synthetic Aperture Radar (SAR) Interferometry data coverage with all four Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO‐SkyMed) SAR satellites. Within the next two weeks, several SAR Interferometry (InSAR) image pairs were processed by the Istituto Nazionale di Geofisica e Vulcanologia–Space‐based Monitoring System …

Since the launch of the European Space Agency’s (ESA’s) Sentinel-1 mission, the synthetic aperture radar (SAR) user community has grown exponentially, adding a large number of users to the field who have only limited experience with the peculiarities of SAR and interferometric SAR (InSAR) data. This growth has been fueled by the increasing availability of globally acquired, free and open SAR data and by a growing number of freely accessible, open source processing tools, such as the Sentinel Application Platform, InSAR Scientific Computing Environment, GMTSAR, and the Alaska Satellite Facility’s cloud-based Hybrid Pluggable Processing Pipeline and OpenScienceLab platforms. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Anticipated to launch in early 2025, the NASA–Indian Space Research Organisation SAR (NISAR) mission will amplify this trend by bringing an unprecedented amount of new, free and open SAR data to the international SAR science community. As the first mission to provide L-band data freely, on a global scale, and in a regularly sampled manner, NISAR will further diversify the SAR application portfolio, likely accelerating community growth. Regional access to simultaneous L- and S-band data facilitated by NISAR will furthermore enable new SAR technology development that may lead to exciting new scientific discoveries. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Focusing on efforts made by NASA and the broader U.S. SAR community, this article summarizes innovative approaches to making the massive datasets of Sentinel-1 and NISAR more accessible to and usable by the SAR community. We summarize novel concepts for data discovery and distribution and highlight tools and services that enable working with SAR data directly at the archive. We also discuss recent education and training efforts developed across the U.S. SAR community to help new users of SAR build capacity to use this exciting dataset. Here, we introduce a set of independent, yet interrelated, projects aimed at developing educational materials and open source science workflows for SAR users with different experience levels.

Polarimetric synthetic aperture radar (SAR) Interferometry (PolInSAR) becomes a hot topic in SAR application areas, such as terrain classification. However, the system parameter design is mostly based on traditional Polarimetric SAR (PolSAR) or interferometric SAR (InSAR) methods. The accuracy of polarimetric calibration is the major factor for the PolInSAR system design, so it is necessary to analyze the affect of polarimatric calibration errors on terrain classification accuracy. In this paper, error transfer link of typical classification algorithm is investigated, then, the minimum requirements on polarimetric distortions including channel imbalance and crosstalk are investigated for terrain classification application for the first time. The result is verified by E-SAR datasets, which can provide the important theoretical basis and reference for quantitative calibration requirements of classification accuracy.

In Antarctica, SAR interferometry has largely been used in coastal glacial areas, while in rare cases this method has been used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for an area encompassing Talos Dome (TD) and the Frontier Mountain (FM) meteorite site in North Victoria Land/Antarctica. A digital elevation model (DEM) was calculated using a double SAR interferometry method. The DEM of the region was calculated by extracting approximately 100 control points from the Reference Elevation Model of Antarctica (REMA). The two DEMs differ slightly in some areas, probably due to the penetration of the SAR-C band signal into the cold firn. The largest differences are found in the western area of TD, where the radar penetration is more pronounced and fits well with the layer structures calculated by the georadar and the snow accumulation observations. By differentiating a 70-day interferogram with the calculated DEM, a displacement interferogram was calculated that represents the ice dynamics. The resulting ice flow pattern clearly shows the catchment areas of the Priestley and Rennick Glaciers as well as the ice flow from the west towards Wilkes Basin. The ice velocity field was analysed in the area of FM. This area has become well known due to the search for meteorites. The velocity field in combination with the calculated DEM confirms the generally accepted theories about the accumulation of meteorites over the Antarctic Plateau.

Synthetic aperture radar (SAR) imagery collected over the continental shelf near Cape Hatteras, N. C., is analyzed in conjunction with shipboard hydrographic and current measurements. The SAR measurements were made over a 2‐hour period on June 20, 1993, in both standard mapping mode and interferometric synthetic aperture radar (INSAR) mode from a NASA DC‐8 aircraft as part of the High‐Resolution Remote Sensing Experiment. In situ currents were measured using a surface‐towed acoustic Doppler current profiler (ADCP). The measurements were made near the end of a period of Gulf Stream incursion onto the shelf as detected using a shore‐based HF radar. Winds were southwesterly at 4–6 m s−1. Long, curvilinear SAR signatures, resembling earlier SEASAT observations made in the same area, are shown to correspond to narrow, shallow fronts separating water masses that increase in surface density with distance offshore. Across‐front changes in surface current inferred from the INSAR data are consistent with 2‐m‐depth currents measured by the ADCP over scales of tens of meters. Thus frontal current gradients measured by INSAR reflect real changes in surface current and are not due to biases induced by changes in the surface‐wave spectrum. This lends support to the detailed INSAR surface maps derived by Graber et al. [1996]. An east‐west salinity front having the largest observed surface density and current gradient is corrugated on length scales up to the local Rossby radius of deformation and translates southward between successive images. In data from the longer radar wavelengths the salinity front appears as a dark band downwind of a bright signature, and this is interpreted as a region where Bragg‐scale waves regenerate following their dissipation in the frontal region. In addition to the fronts the imagery shows closely spaced packets of southward propagating ocean internal waves occurring in the strongly stratified inshore water mass. This case study further serves to emphasize the potential of SAR imagery for study of a wide range of shelf processes.

ML based approach for inverting penetration depth of SAR signals over large desert areas

The SAR (synthetic aperture radar) system simulation is a useful tool for the design of an SAR system. It can predict the system performance before the SAR is realized. We present the design concept of a general purpose SAR simulation package, which can deal with both the airborne and spaceborne SARs, and also the multi-polarization, multi-frequency and interferometric SARs. This software package must be modular so that the different models can be selected for each subsystem. It consists of three parts. The first one is design entry for the model creation and representation. The second part is a simulation controller which selects the different models of the SAR subsystems, and generates the SAR raw data. The third part is the data processing and analysis tools which consists of a general SAR processor, waveform analysis tools and image analysis tools.

Over the past three decades, synthetic aperture radar (SAR) and SAR interferometry (InSAR) have revolutionized Earth observation, allowing for geophysical monitoring of Earth's surface deformation with centimeter‐to‐millimeter precision. InSAR time series are successive maps of ground motion used for natural hazards assessment with the monitoring of processes slowly deforming the Earth's surface, such as landslides, subsidence, or slow tectonic motion. In particular, the detection of transient displacements of millimeter amplitude is of utmost importance to better capture the dynamics of active tectonic faults, for a better understanding of the underlying physics governing the earthquake cycle. However, despite tremendous advances over the last two decades, noise affecting the data, primarily due to atmospheric delay, still challenges the detection of small deformation signals in InSAR time series. Consequently, an unknown amount of slow tectonic deformation signals remains concealed within the noise, raising the need for novel methodologies for signal extraction. We introduce InSARDenoiser, a spatiotemporal attentive convolutional U‐Net, designed to extract small‐scale deformation in noisy InSAR time series. The combination of a deep spatial U‐Net with a spatiotemporal Transformer tracks transient deformation in space and follows its temporal signature through time. We develop a mathematical formulation to generate realistic multi‐scale atmospheric noise and build a synthetic data set. When applied to a real InSAR time series along the North Anatolian Fault, the method effectively extracts millimeter‐scale slow deformation. The extracted deformation aligns with independent creepmeter measurements, thereby validating our method and opening the possibility of its application to diverse tectonic settings globally.

Abstract. In recent years, Synthetic Aperture Radar (SAR) data have been widely used for scientific applications and several SAR missions were realized. The active sensor principle and the signal wavelength in the order of centimeters provide all-day and all-weather capabilities, respectively. The modern German TerraSAR-X (TSX) satellite provides high spatial resolution down to one meter. Based on such data SAR Interferometry may yield high quality digital surface models (DSMs), which includes points located on 3d objects such as vegetation, forest, and elevated man-made structures. By removing these points, digital elevation model (DEM) representing the bare ground of Earth is obtained. The primary objective of this paper is the validation of DEMs obtained from TSX SAR data covering Barcelona area, Spain, in the framework of a scientific project conducted by ISPRS Working Group VII/2 "SAR Interferometry" that aims the evaluation of DEM derived from data of modern SAR satellite sensors. Towards this purpose, a DSM was generated with 10 m grid spacing using TSX StripMap mode SAR data and converted to a DEM by filtering. The accuracy results have been presented referring the comparison with a more accurate (10 cm–1 m) digital terrain model (DTM) derived from large scale photogrammetry. The results showed that the TSX DEM is quite coherent with the topography and the accuracy is in between ±8–10 m. As another application, the persistent scatterer interferometry (PSI) was conducted using TSX data and the outcomes were compared with a 3d city model available in Google Earth, which is known to be very precise because it is based on LIDAR data. The results showed that PSI outcomes are quite coherent with reference data and the RMSZ of differences is around 2.5 m.