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Radar systems operating aboard unmanned aerial systems (UASs) or drones are gaining lot of interest in the remote sensing field. UASs have recently been tested as platforms for synthetic aperture radar (SAR) systems. There are many expectations regarding the interferometric capabilities of UAS-based radar interferometers. Nevertheless, its implementation introduces new problems, which demand advanced processing techniques. To date, only few works presented interferometric data acquired aboard UAS. In this paper repeat-pass interferometric tests are presented, performed with an S-band UAS-based SAR. By using corner reflectors subject to known displacements, the interferometric capabilities of the sensor are demonstrated. Results from two measurement campaigns are presented: data acquired in controlled scenario and in a vegetated slope. A focusing algorithm that considers the UAS position and attitude information, able to cope with the UAS instabilities, is presented and discussed. At the knowledge of the authors, the experimental results obtained are the first systematic demonstration of repeat-pass UAS-based SAR interferometry, validated by ground truth.

Monitoring floods using optical satellite images in winter is challenging. Because of the thickness of clouds, the electromagnetic bands used by satellites are too narrow and the resulting signals are reflected into space. The purpose of this study is to assess the potential of implementing a high-resolution synthetic aperture radar system (SAR) for the purpose of mapping floods in Jordan while the weather is cloudy. TerraSAR-X satellite was utilized to acquire images of high resolution for flood monitoring. This research employed TerraSAR-X thresholding images to identify flood-prone areas and track floods over time using time-series images. It was possible to determine the places of the landscape that have been impacted by the flood and to establish the degree of damage by conducting an analysis of the changes that have occurred in the landscape. The accuracy of the study's findings was confirmed using backscatter texture analysis on the images, which revealed a small difference between thresholding and texture analysis approaches of less than 3% in the mapped test region's flood area. While monitoring floods, it is recommended to use TerraSAR-X images, particularly in conjunction with other data sources, such as weather predictions and hydrological models. [ABSTRACT FROM AUTHOR]

Different interferometric approaches have been developed over the past few decades to process SAR data and recover surface deformation, and each approach has advantages and limitations. Finding an accurate and reliable interval for preparing mean deformation rate maps (MDRMs) remains challenging. The primary purpose of this paper is to implement an application consisting of three unsupervised clustering algorithms (UCAs) for determining the best interval from SAR-derived deformation data, which can be used to interpret long-term deformation processes, such as subsidence, and identify displacement patterns. Considering Port Harcourt (in the Niger Delta) as the study area, it was essential to remove the sources of error in extracting deformation signals from SAR data, spatially ionospheric and tropospheric delays, before using UCAs to obtain its characteristics and real deformation data. Moreover, another purpose of this paper is to implement the advanced integration method (AIM) for atmospheric phase screen (APS) correction to enhance deformation signals obtained through different SAR processing approaches, including interferometric SARs (two-pass interferometry, InSAR) and multitemporal interferometry SARs (n-pass interferometry, DInSAR; permanent scatterer interferometry (PSI); and small baseline subset (SBAS)). Two methods were chosen to evaluate and find the best technique with which to create an MDRM: The first one was to compare the signals corrected by the AIM and the vertical component of the GPS station, which showed the AIM providing 58%, 42%, and 28% of the matching with GNSS station outputs for InSAR, PSI, and SBAS, respectively. Secondly, similarity measures and Davies–Bouldin index scores were implemented to find an accurate and reliable interval in which the SBAS technique with the unsupervised K-medians method has been chosen. Based on GNSS vertical deformation in a 500 m radius around the station, the SBAS K-medians technique expressed up to 5.5% better deformation patterns than the map of SAR processing techniques. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone. [ABSTRACT FROM AUTHOR]

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m). [ABSTRACT FROM AUTHOR]

Different interferometric approaches have been developed over the past few decades to process SAR data and recover surface deformation, and each approach has advantages and limitations. Finding an accurate and reliable interval for preparing mean deformation rate maps (MDRMs) remains challenging. The primary purpose of this paper is to implement an application consisting of three unsupervised clustering algorithms (UCAs) for determining the best interval from SAR-derived deformation data, which can be used to interpret long-term deformation processes, such as subsidence, and identify displacement patterns. Considering Port Harcourt (in the Niger Delta) as the study area, it was essential to remove the sources of error in extracting deformation signals from SAR data, spatially ionospheric and tropospheric delays, before using UCAs to obtain its characteristics and real deformation data. Moreover, another purpose of this paper is to implement the advanced integration method (AIM) for atmospheric phase screen (APS) correction to enhance deformation signals obtained through different SAR processing approaches, including interferometric SARs (two-pass interferometry, InSAR) and multitemporal interferometry SARs (n-pass interferometry, DInSAR; permanent scatterer interferometry (PSI); and small baseline subset (SBAS)). Two methods were chosen to evaluate and find the best technique with which to create an MDRM: The first one was to compare the signals corrected by the AIM and the vertical component of the GPS station, which showed the AIM providing 58%, 42%, and 28% of the matching with GNSS station outputs for InSAR, PSI, and SBAS, respectively. Secondly, similarity measures and Davies–Bouldin index scores were implemented to find an accurate and reliable interval in which the SBAS technique with the unsupervised K-medians method has been chosen. Based on GNSS vertical deformation in a 500 m radius around the station, the SBAS K-medians technique expressed up to 5.5% better deformation patterns than the map of SAR processing techniques.

Underground coal mining activities and ground movement are directly correlated, and coal mining-induced ground movement can cause damage to property and resources, thus its monitoring is essential for the safety and economics of a city. Fangezhuang coal mine is one of the largest coalfields in operation in Tangshan, China. The enormous amount of coal extraction has resulted in significant ground movement over the years. These phenomena have produced severe damages to the local infrastructure. This paper uses the finite difference method (FDM) 3D model and the stacking interferometric synthetic aperture radar (InSAR) method to monitor the ground movement in Fangezhuang coalfield during 2016. The FDM 3D model used calibrated Fangezhuang geological parameters and the satellite InSAR analysis involved the use of ascending C-band Sentinel-1A interferometric wide (IW) data for 2016. The results show that the most prominent subsidence signal occurs in mining panel 2553N and the area between panel 2553N and fault F0 with subsidence up to 57 cm. The subsidence observed for the FDM 3D model and stacking InSAR to monitor land deformation under the influence of fault are in close agreement and were verified using a two-sample t-test. It was observed that the maximum subsidence point shifted towards the fault location from the centre of the mining panel. The tectonic fault F0 was found to be reactivated by the coal mining and controls the spatial extent of the observed ground movement. The impact of dominant geological faults on local subsidence boundaries is investigated in details. It is concluded that ground movement in the study area was mainly induced by mining activities, with its spatial pattern being controlled by geological faults. These results highlight that the two methods are capable of measuring mining induced ground movement in fault dominated areas. The study will improve the understanding of subsidence control, and aid in developing preventive measures in Fangezhuang coalfield with fault reactivation.

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of −2 to −4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m).

Fast observations of rapid surface large-changes are demanded in disaster evaluations and scientific studies. Digital elevation model (DEM) differencing before and after the events is an effective way to retrieve the changes. Owing to a short repeat cycle, geosynchronous synthetic aperture radar (GEO SAR) systems can quickly obtain repeat-pass data and generate postevent DEMs by interferometry. However, interferometric baselines under its quick revisit cases are short, resulting in generating low-accuracy postevent DEMs. Moreover, surface large-changes can bring height ambiguity problems under the single-baseline interferometric processing. In this letter, we address the problem through a multibaseline (MB) processing. Since GEO SAR MB data can derive from the repeat-pass interferometric data of different subapertures and revisits, a subaperture-decomposition-based temporal and spatial MB method is proposed. The simulation results verify the effectiveness of the proposed method, where the quickly generated postevent DEM can help to realize the rapid large-elevation change observations. [ABSTRACT FROM AUTHOR]

Persistent Scatterer Interferometry (PSI) is a time series remote sensing technique to estimate displacements of geo-objects from the interferometric phases of selected Persistent Scatterers (PS). The relative position of a scatterer within a resolution cell causes an additional phase contribution in the observed phase, which needs to be accounted for in PSI processing. Here we analyze the influence of this sub-pixel position correction on point localization and displacement quality. Apart from a theoretical evaluation, we perform experiments with TerraSAR-X, Radarsat-2, and Sentinel-1, demonstrating various levels of improvement. We show that the influence of the sub-pixel correction is significant for the geolocation of the scatterer (meter-level improvement), modest for the elevation estimation (centimeter-level improvement), and limited for the displacement estimation (submillimeter-level). For displacement velocities, we find variations of a few tenths of a millimeter per year. The effect of sub-pixel correction is most dominant for large orbital baselines and short time series. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar(SAR) technology has unique advantages in forest resource monitoring due to its all-day, all-weather imaging capability and sensitivity to vertical forest structure information. Therefore, SAR has become a research focus of current forest resources remote sensing survey technology. Firstly, the development background, development trajectory and related knowledge of SAR forest resources monitoring technology are introduced. Then, the technological developments of polarimetric SAR, interferometric SAR, polarimetric SAR interferometry and tomographic SAR in research of forest land cover type classification, change detection and forest parameter quantification estimation are emphasized. Finally, the existing problems and development trends for forest resource monitoring research and application of SAR are summarized and analyzed. [ABSTRACT FROM AUTHOR]

Aeolian erosion occurring in sand deserts causes significant socio-economical threats over extensive areas through mineral dust storm generation and soil degradation. To monitor a sequence of aeolian erosion in a sand desert area, we developed an approach fusing a set of remote sensing data. Vegetation index and Interferometric Synthetic Aperture Radar (InSAR) phase coherence derived from space-borne optical/SAR remote sensing data were used. This scheme was applied to Kubuqi Desert in Inner Mongolia where the effects of activity to combat desertification could be used to verify the outcome of the approach. We first established time series phase coherence and conducted a functional operation based on principal component analysis (PCA) to remove uncorrelated noise. Then, through decomposition of vegetation effect, where a regression model together with the Enhanced Vegetation Index (EVI) was employed, we estimated surface migration caused by aeolian interaction, that is, the aeolian erosion rate (AER). AER metrics were normalized and validated by additional satellite and ground data. As a result, the spatiotemporal migration of the target environment, which certainly induced dust storm generation, was traced and analyzed based on the correlations among surface characteristics. It was revealed that the derived AER successfully monitored the surface changes that occurred before and after the activities to combat desertification in the target area. Employing the established observation scheme, we expect a better understanding of the aeolian process in sand deserts with enhanced spatio-temporal resolution. In addition, the scheme will be beneficial for the evaluation of combating desertification activities and early warning of dust storm generations.

The launch of Copernicus, the largest Earth Observation program to date, is significant due to the regular, reliable and freely accessible data to support space-based geodetic monitoring of physical phenomena that can result in natural hazards. In this study, wide area interferometric synthetic aperture radar (InSAR) capability is demonstrated by processing 435 Copernicus Sentinel-1 C-Band SAR images (May 2015–May 2017) using the Intermittent Small Baseline Subset (ISBAS) method to produce a wide-area-map (WAM) covering 53,000 km2 of the Netherlands, Belgium and Germany. The ISBAS-WAM contains over 19 million measurements, achieving a ground coverage of 94%. The retrieval of measurements over soft surfaces (i.e. agricultural fields, forests and wetlands) was crucial due the dominance of non-urban land cover. A statistical analysis of the velocities reveals that intermittently coherent measurements in rural areas can provide reliable, additional deformation information with a very high degree of confidence (5σ), which spatially correlates to known deformation features associated with compressible soils, infrastructure settlement, peat oxidation, gas production, salt mining and underground and opencast mining. The spatial distribution of deformations concurs with independent data sources, such as previous persistent scatterer interferometry (PSI) deformation maps, models of subsidence and settlement susceptibility, and quantitatively with GPS measurements over the Groningen gas field. Remotely derived deformation products, with near complete spatial coverage, provide a powerful screening tool for mitigation and remediation of geological and geotechnical issues to help in the protection of assets, property and life. The ISBAS-WAM demonstrates that routine generation of such products on a continental scale is now theoretically achievable, given the establishment of the Copernicus programme and the development of state-of-the-art InSAR methods, such as ISBAS. • ISBAS InSAR was used to produce a national deformation map with 94% coverage. • Pixels in rural areas provided reliable measurements at the 5σ level. • Motions were attributed to six main phenomena. • National deformation maps can provide critical information on potential geohazards. [ABSTRACT FROM AUTHOR]

The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empirical mode decomposition (EMD) and seasonal and trend decomposition using loess (STL) were combined to estimate the seasonal component of the total time-series displacements. Then, a temperature correlation map was generated by correlating the seasonal component with the temperature variation. Results show that the thermal expansion phenomenon is pronounced on the buildings of the Zhuhai–Macao Passenger Terminal as well as the bridge and road connecting to the Hong Kong International Airport (HKIA), while it is less obviously observed at the main Hong Kong-Zhuhai-Macao Bridge (HZMB). In addition, sudden changes between subsidence and uplift can be detected through the p-values derived by applying the augmented Dickey-Fuller (ADF) test to the residual signals after removing the linear and seasonal components from the original ones.

This article gives a brief description of the interferometric SAR mode. It gives a description and the main characteristics of some airborne interferometric SAR systems. It`s also shows possible ways of development of airborne interferometic radars in the near future. Compared to the interferometric systems of the Earth remote sensing space-based, airborne ISAR have higher precision of measurement of height and detail RI. At the same time, ISAR airborne have several disadvantages such as the need of accurate correction of the flight path of the aircraft, which entails the need to install onboard precision inertial systems and GPS receivers, and the application of complex algorithms for trajectory control; less coverage to the areas in the mapping, time limited and local character of the use, which leads to higher dimensions.

Circular Synthetic Aperture Radar (CSAR) can acquire targets’ scattering information in all directions by a 360° observation, but a single-track CSAR cannot efficiently obtain height scattering information for a strong directive scatter. In this study, we examine the typical target of the three-dimensional circular SAR interferometry theoryand validate the theory in a darkroom experiment. We present a 3D reconstruction of the actual tank metal model of interferometric CSAR for the first time, verify the validity of the method, and demonstrate the important potential applications of combining 3D reconstruction with omnidirectional observation.

Abstract The primary objective of the German TanDEM-X mission is the generation of a globally available, highly accurate and detailed digital elevation model (DEM), with the final product having 12 m posting, 2 m relative and 10 m absolute vertical accuracy. The first version of this global DEM has been finalized by the German Aerospace Center (DLR), in September 2016. Our experience with the experimental application of non-local means filters to TanDEM-X data suggests that TanDEM-X has the potential of producing DEMs of even higher resolution and accuracy. The goal of this investigation is to explore the possibility of employing non-local InSAR filters to achieve an effective resolution of 6 m, with an equivalent posting, and a relative height error below 0.8 m, i.e. an increase of quality by a factor of 2 × 2 in resolution and a factor of 2 m/0.8 m = 2.5 in height accuracy — all in all one order of magnitude. Highlights • NL-InSAR can potentially improve the quality of TanDEM-X DEMs by a factor of ten. • NL-InSAR filter gives us significantly less biased coherence estimates. • An unwanted terrace-like artifact produced by NL-InSAR is observed and explained. • A special defringing pre-processing may be the key to eliminate these artifacts. [ABSTRACT FROM AUTHOR]

The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR) Interferometry (InSAR) has been a widely used geodetic technique for observing the Earth's surface, especially for mapping the Earth's topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth's atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere) are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR). To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorología (AEMET) and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC) images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate height and deformation measurements. [ABSTRACT FROM AUTHOR]

The airborne interferometric SAR platform suffers from instability factors, such as air turbulence and mechanical vibrations during flight. Such factors cause the oscillation of the flexible baseline, which leads to significant degradation of the performance of the interferometric SAR system. This study is concerned with the baseline oscillation. First, the error of the slant range model under baseline oscillation conditions is formulated. Then, the SAR complex image signal and dual-channel correlation coefficient are modeled based on the first-order, second-order, and generic slant range error. Subsequently, the impact of the baseline oscillation on the imaging and interferometric performance of the SAR system is analyzed. Finally, simulations of the echo data are used to validate the theoretical analysis of the baseline oscillation in the airborne interferometric SAR.

The availability of high-resolution spaceborne synthetic aperture radar (SAR) data coupled with the ongoing refinement of tomographic SAR (TomoSAR) technology has made use of radar data feasible for preventive monitoring and assessment of built structures. In this study, we first applied extended differential TomoSAR (D-TomoSAR) to a set of 26 scenes of TerraSAR/TanDEM-X (TSX/TDX) (2013−2015) and 32 scenes of Cosmo-SkyMed (CSK) (2015−2017) images to estimate motions of skyscrapers, bridges and historical monuments in Nanjing City, China. The calculation and isolation of unknown parameters in the D-TomoSAR model, including linear velocity, thermal dynamics and structural heights, helped to estimate millimetric statistics of motion time series. Then, aforementioned two SAR datasets were tentatively tested using amplitude dispersion and phase stability indicators, highlighting the performance and sensitivity of X-band SAR in structural displacement monitoring. Experimental results demonstrated that motion indexes, e.g., heterogeneities of thermal amplitudes and spatiotemporal displacements, were useful to evaluate the conditions of built structures being monitored, in particular when their structural topology were visible owing to the enhanced density of persistent scatterer (PS) measurements. This study implies the value of high-resolution D-TomoSAR tools in the preventive monitoring and health diagnosis of built structures elsewhere over the world.

Distributed array antenna synthetic aperture radar (SAR), flexible baseline interferometric SAR (InSAR) or integrated multi-task imaging sensors are the most attractive development directions of aerial survey and remote sensing system, and urgently demand a distributed Position and Orientation System (DPOS) to accurately measure multi-nodes time-spatial reference information. However, the traditional transfer alignment (TA) method can't meet the requirements of some high precision interferometric imaging task. To solve the problem, a dual-filter TA method based on position-velocity-attitude matching (PVAM) was proposed. Firstly, the TA method based on attitude matching (AM) is conducted in filter-1 to estimate the flexible angles and the derivatives. Then the TA method based on position-velocity matching (PVM) is conducted in filter-2, and the position and velocity measurements are compensated by the flexible angles and the derivatives estimated by filter-1. Finally, the strapdown solutions of slave IMU are corrected by the corrections from both filter-1 and filter-2. A semi-physical simulation based on airborne DPOS flight experiment has been conducted, verifying that the baseline error has been reduced from 0.0240 m to 0.0083 m and the computation time has been decrease by 3.41%. [ABSTRACT FROM AUTHOR]

Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO2, which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes in vertical structure and AGB, with sub-hectare and monthly spatial and temporal resolution, respectively. The phase-height observation is described, with a focus on how it is related to vegetation-density, radar-power vertical profiles, and mean canopy heights, which are, in turn, related to AGB. The study site covers 18 x 60 km in the Tapajós National Forest in the Brazilian Amazon. Phase-heights over Tapajós were measured by DLR's TanDEM-X radar interferometer 32 times in a 3.2 year period from 2011-2014. Fieldwork was done on 78 secondary and primary forest plots. In the absence of disturbance, rates of change of phase-height for the 78 plots were estimated by fitting the phase-heights to time with a linear model. Phase-height time series for the disturbed plots were fit to the logistic function to track jumps in phase-height. The epochs of clearing for the disturbed plots were identified with ≈1-month accuracy. The size of the phase-height change due to disturbance was estimated with ≈2-m accuracy. The monthly time resolution will facilitate REDD+ monitoring. Phase-height rates of change were shown to correlate with LiDAR RH90 height rates taken over a subset of the TanDEM-X data's time span (2012-2013). The average rate of change of phase-height across all 78 plots was 0.5 m-yr-1 with a standard deviation of 0.6 m-yr-1. For 42 secondary forest plots, the average rate of change of phase-height was 0.8 m-yr-1 with a standard deviation of 0.6 m-yr-1. For 36 primary forest plots, the average phase-height rate was 0.1 m-yr-1 with a standard deviation of 0.5 m-yr-1. A method for converting phase-height rates to AGB-rates of change was developed using previously measured phase-heights and field-estimated AGB. For all 78 plots, the average AGB-rate was 1.7 Mg-ha-1-yr-1 with a standard deviation of 4.0 Mg-ha-1-yr-1. The secondary-plot average AGB-rate was 2.1 Mg-ha-1-yr-1, with a standard deviation of 2.4 Mg-ha-1-yr-1. For primary plots, the AGB average rate was 1.1 Mg-ha-1-yr-1 with a standard deviation of 5.2 Mg-ha-1-yr-1. Given the standard deviations and the number of plots in each category, rates in secondary forests and all forests were significantly different from zero; rates in primary forests were consistent with zero. AGB-rates were compared to change models for Tapajós and to LiDAR-based change measurements in other tropical forests. Strategies for improving AGB dynamical monitoring with X-band interferometry are discussed. [ABSTRACT FROM AUTHOR]

Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers’ Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992–2000, and Sentinel-1 C-band images, covering the period 2015–2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign.

This paper proposes a science and technology education program to teach junior high school students to measure terrain changes by using interferometric synthetic aperture radar (SAR). The objectives of the proposed program are to evaluate and use information technology by performing SAR data processing in order to measure ground deformation, and to incorporate an understanding of Earth sciences by analyzing interferometric SAR processing results. To draft the teaching guidance plan for the developed education program, this study considers both science and technology education. The education program was used in a Japanese junior high school. An educational SAR processor developed by the authors and the customized Delft object-oriented radar interferometric software package were employed. Earthquakes as diastrophism events were chosen as practical teaching materials. The selected events indicate clear ground deformation in differential interferograms with high coherence levels. The learners were able to investigate the ground deformations and disasters caused by the events. They interactively used computers and became skilled at recognizing the knowledge and techniques of information technology, and then they evaluated the technology. Based on the results of pre- and post-questionnaire surveys and self-evaluation by the learners, it was clarified that the proposed program was applicable for junior high school education, and the learners recognized the usefulness of Earth observation technology by using interferometric SAR. The usefulness of the teaching materials in the learning activities was also shown through the practical teaching experience. [ABSTRACT FROM AUTHOR]

A singular‐unit restoration filter based on complex‐valued neural networks that deal with spatial spectrum in interferometric synthetic aperture radar is proposed. This filter utilises more neural generalisation ability than conventional methods. In experiments, it shows a higher accuracy as well as shorter processing time than conventional real‐space filters and shorter learning time than previous spectral‐domain learning filters. [ABSTRACT FROM AUTHOR]

Highlights: [•] Use the 3D finite element method to model the InSAR deformation interferograms. [•] Determine the seismogenic fault through comparing the model results with InSAR observations. [•] Analayze the shallow slip deficit of the co-seismic deformation of the 2003 Delingha earthquake. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection. [ABSTRACT FROM AUTHOR]

This paper assesses the potential of using spaceborne X-band synthetic aperture radar (SAR) data for monitoring water-level changes over wetlands. Our analysis is based on three sets of TerraSAR-X (TSX) observations acquired over South Florida's Everglades wetlands during an eight-month period in 2008. The first set was acquired in single HH polarization stripmap mode over our northern study area, consisting of managed wetlands and urban environments. The second set was acquired in dual-polarization stripmap mode over the western half of the same area, consisting mostly of managed wetlands. The third set was also acquired with dual-polarization stripmap mode over our southern study area, consisting of natural flow fresh- and salt-water wetlands in the southern Everglades. The first data set was used for a proof-of-concept study to verify that X-band data can generate coherent interferograms in wetland areas. Interferometric processing of this data set shows a high level of coherence (> 0.35) over both wetland and urban regions, maintaining interferometric phase in all three interferograms spanning 11 days. Surprisingly, phase is maintained over some of the wetlands even for interferograms spanning 33 days. The other two data sets were used to evaluate interferometric coherence of all four polarization modes and to determine dominant scattering mechanism in each wetland environment. Our results show high coherence values (> 0.4) in all polarization modes, with highest values in HH, then VV, and lowest in HV or VH. Interferograms calculated from multipolarization data show very similar fringe patterns regardless of the polarization type, suggesting that the phase information in all polarization data reflects water-level changes in wetlands and that volume scattering may be less important than commonly believed. We also used the two multipolarization data sets to conduct the Pauli decomposition, finding a strong dependence of scattering mechanism on vegetation type. The high interferometric coherence level of all polarization data suggests that a significant part of the X-band scattered signal interacts with lower sections of the vegetation (trunks and branches), because scattering from wind-affected canopies cannot support such a high coherence level. The high spatial resolution of TSX, combined with its ll-day repeat orbit, makes this X-band sensor surprisingly suitable for wetland interferometric SAR applications. [ABSTRACT FROM AUTHOR]

This study presents the application of interferometric synthetic aperture radar (InSAR), small baseline subset (SBAS) algorithm and GIS for subsidence analysis in a mining area located at Gangwon-do, Korea. Several mining areas at the Samchuk coalfield had operating coalmines until 2002–2003. Ground deformation has occurred as a form of subsidence due to the continuous mining activity or the collapse of residual cavities. Twenty-three scenes from a JERS-1 satellite acquired from 1992 to 1998 are used to detect the ground movement by means of the differential InSAR process. Distinctive phase variations induced by subsidence were observed in several interferograms. The SBAS algorithm was applied for a detailed analysis. As a result, it was confirmed that deformation was occurring between September 21, 1992 and September 30, 1998 and attained 22 cm at the most significantly subsiding site. The mean and standard deviation of RMS error were 2.0 cm and 0.78 cm, respectively, showing the level of the error due to the used digital elevation model, unwrapping and atmospheric effect. The SBAS-derived cumulative subsidence map was validated with the underground workings and geological maps in order to find the spatial relations and subsiding mechanism. From the spatial comparison with these data, it was shown that the subsidence has taken place above excavations and fault lines. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) images of geophysical events such as preeruptive volcano deformation or interseismic strain accumulation are often limited by phase distortions from the superimposed atmospheric signature. Additionally, the approximate monthly repeat cycle of many radar satellites cannot accurately capture rapidly time-varying processes. The Scanning Synthetic Aperture Radar (ScanSAR) mode of the ENVISAT/ASAR instrument permits more frequent revisits of a given area, potentially overcoming both of these limitations. In particular, stripmap mode-to-ScanSAR images provide a denser time series of interferograms than is possible with conventional stripmap-to-stripmap mode InSAR. We present images of ENVISAT/ASAR data acquired over Hawaii in which data acquired roughly weekly in ScanSAR mode are combined with ENVISAT/ASAR conventional stripmap mode data to form interferograms at a much denser temporal spacing. The burst nature of ScanSAR data requires a new processing method to form the interferograms. We use traditional matched filtering for the range compression. For the azimuth processing, we compute the stripmap mode data on the ScanSAR sampling grid using a variation, consisting of different reference functions, of Lanari's modified SPECAN algorithm that is itself an adaptation of the chirp z-transform to readjust the azimuth pulse spacing. The resulting interferograms faithfully reflect the phase of conventional interferograms, but exhibit fewer looks and coarser resolution than those produced by fully stripmap mode data. For many problems, temporal density of the deformation observations is paramount, and the time series analysis and temporal averaging that were made possible using ScanSAR interferograms far out-weigh the loss in looks and resolution. [ABSTRACT FROM AUTHOR]