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Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian "Valley of the Kings" (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves.

Spaceborne bistatic synthetic aperture radar (BiSAR) systems utilize an intersatellite link to achieve phase synchronization. However, radio frequency interference (RFI) from communication satellites and ground-based radars often contaminates the synchronization signal, leading to inaccuracies in the inverted digital elevation model (DEM). Therefore, this article puts forward an advanced phase-preserving synchronization RFI suppression method and validates it using data from an $L$-band BiSAR system, LuTan-1 (LT-1). The method involves detecting and locating RFI within a monopulse synchronization signal, and the signal at the estimated RFI position is removed to obtain a preprocessed signal. Then, based on the preprocessed signal and the RFI model, RFI is estimated using a gradient-based approach. Finally, the estimated RFI is subtracted from the monopulse signal to obtain the desired signal. In addition, synchronization RFI suppression and DEM generation experiments are performed on the LT-1 data to verify the proposed method. Experimental results demonstrate that the method effectively suppresses synchronization RFI and improves DEM accuracy, and it has extensive application prospects in future low-band distributed interferometric synthetic aperture radar missions.

Accurate estimation of forest aboveground biomass (AGB) is crucial for research on terrestrial carbon cycling and global climate change. In this study, we introduce an improved approach for estimating forest AGB combining P-band and X-band interferometric synthetic aperture radar (InSAR) data. Forest AGB was estimated by combining unbiased forest height and volume backscatter intensity. For forest height, a multilayer model and subaperture decomposition technology were used to remove the penetration bias of the X-band and reduce the effects of forest scatterers on the extraction of a pure understory terrain phase based on P-band, respectively. For volume backscatter intensity, a ground cancellation algorithm based on P-band InSAR was used to eliminate ground scattering contributions unrelated to forest AGB. The proposed method was validated using airborne P-band InSAR data and spaceborne X-band InSAR data gathered over the study area on the Saihanba Forest Farm in Hebei, China. The unbiased forest height and volume backscatter intensity had stronger correlations with forest AGB than estimates derived from unimproved features. The proposed method returned high-precision estimates of forest AGB with an accuracy of 83.73%, an improvement of 8.80% over an estimate derived from unoptimized features. Additionally, AGB estimates combined with forest height and backscatter intensity were greater than those based on a single feature, with the contribution of the former is greater than that of the latter.

Synthetic aperture radar (SAR) 3-D imaging technique can eliminate severe layover and geometric distortions in 2-D images, which greatly facilitates target recognition and 3-D modeling. However, due to the strong anistropic scattering properties of man-made targets, it's hard to obtain high-resolution 3-D images by conventional SAR imaging methods. Multi-aspect aperture synthesis is an advanced SAR imaging mode, which is used to obtain the multi-aspect anisotropic scattering information as well as the 3-D structures of the observed targets. We demonstrate that this technology greatly facilitates the 3-D SAR imaging of complex-structured targets. In this article, we introduce several recent works of our research group on 3-D radar imaging based on multi-aspect aperture synthesis techniques. Main topics include multi-aspect SAR 3-D imaging modes, spatial resolution theory, an object-level 3D reconstruction method with single-channel single-pass SAR data, theory of holographic SAR tomography imaging technology and a corresponding 3D inversion method, and an interferometric circular SAR 3D reconstruction method with its experimental results on the FAST radio telescope data. We analyze the advantages and existing scientific problems for each proposed technical path in detail, aiming to provide new solutions for SAR 3D imaging/inversion problems. [ABSTRACT FROM AUTHOR]

Permafrost-affected ecosystems of the Arctic-boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ~80 SAR flight lines and how they fit into the ABoVE experimental design. We provide hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including: boreal forest canopy structure from tomoSAR data over Delta Junction, AK and the BERMS site in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L55 and P-band SAR data. [ABSTRACT FROM AUTHOR]

Current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to improve the empirical Bayesian coherence magnitude estimation in terms of accuracy and computational cost. Precisely, this article proposes the interferometric coherence magnitude estimation by machine learning (ML). It results in a nonparametric and automated statistical inference. However, applying ML in this estimation context is not straightforward. The number and the domain of possible input processes is infinite and it is not possible to train all possible input signals. It is shown that the expected channel amplitudes and the expected interferometric phase cause redundancies in the input signals allowing to solve this issue. Similar to the empirical Bayesian methods, a single parameter for the maximum underlaying coherence is used to model the prior. However, no prior or any shape of prior probability is easy to implement within the ML framework. The article reports on the bias, standard deviation and RMSE of the developed estimators. It was found that ML estimators improve the coherence estimation RMSE from small samples ($2 \leq N < 30$) and for small underlaying coherence compared to the conventional and empirical Bayes estimators. The developed ML coherence magnitude estimators are suitable and recommended for operational InSAR systems. For the estimation, the ML model is extremely fast evaluated because no iteration, numeric integration or bootstrapping is needed.

An increasing interest in the use of low frequency Synthetic Aperture Radar (SAR) systems, e.g., L- and P-bands, makes the research of the ionospheric effects on SAR interferograms become urgent and significant. As the most pronounced signature in interferograms, the ionosphere-induced azimuth streak was thoroughly investigated in this study through processing of the 19 L-band Advanced Land-Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR)images over the Chongqing City, China. The investigations show that the visible ionosphere-induced stripe-shape azimuth shifts with the invariable direction of 26°E, 113°N are observed in some interferometric pairs. Relating these anomalous azimuth shifts to the International GNSS Service (IGS) final ionospheric products shows that the detected ionosphere-contaminated SAR images display the relatively large ionospheric variation with time during SAR satellite travelled through the study area, indicating a somewhat correlation between them. After detecting the ionosphere-contaminated interferograms, we estimated the Ionospheric Phase Streak (IPS) based on an approximate linear relationship between IPS and azimuth shift, and then removed them from the original interferograms. The corrected results show that ionospheric phase patterns are largely removed from the ionosphere-contaminated interferograms. The investigation indicates that the direction of the IPS keeps approximately constant in space and time, which provides the potential chance to develop methods to correct the ionospheric effect. Furthermore, this study once more proves that the ionospheric effect on SAR interferogram can be detected, estimated and corrected from azimuth shifts.

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multitemporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multitemporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (preflood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and a statistically homogeneous pixels selection based multitemporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.

Interferometric SAR (InSAR) is a practical technique to derive 3-D structure of the earth's surface. This article conducts a comparative study of digital elevation model (DEM) reconstruction using the single-baseline and multibaseline InSAR techniques. To improve the accuracy of the DEM generated from the single-baseline InSAR, we improve the weight definition approach according to the coherence magnitudes for the fusion of single-baseline InSAR DEMs. For multibaseline InSAR, we adopt a method based on the maximum-likelihood estimation (MLE) algorithm, which uses external DEM as a constraint. A TerraSAR-X dataset comprising four SAR images acquired over Barcelona is used for experimental investigation. The accuracies of four types of DEM are comparatively studied, including DEM by single-baseline InSAR, DEM by the fusion of single-baseline InSAR DEMs, DEM by unconstrained multibaseline InSAR, and DEM by constrained multibaseline InSAR. The results show that the proposed weighted fusion of single-baseline InSAR performed slightly better than the constrained multibaseline InSAR method, and both of them performed better than the single-baseline results, demonstrating that multiple observations can effectively improve the accuracy of the reconstructed DEM.

The Source Physics Experiment (SPE) Phase I conducted six underground chemical explosions at the same experimental pad with the goal of characterizing underground explosions to enhance the United States (U.S.) ability to detect and discriminate underground nuclear explosions (UNEs). A fully polarimetric synthetic aperture RADAR (PolSAR) collected imagery in VideoSAR mode during the fifth and sixth explosions in the series (SPE-5 and SPE-6). Previously, we reported the prompt PolSAR surface changes cause by SPE-5 and SPE-6 explosions within seconds or minutes of the underground chemical explosions, including a drop of spatial coherence and polarimetric scattering changes. Therein it was hypothesized that surface changes occurred when surface particles experienced upward acceleration greater than 1 g. Because the SPE site was instrumented with surface accelerometers, we explore that hypothesis and report our findings in this article. We equate explosion-caused prompt surface expressions measured by PolSAR to the prompt surface movement measured by accelerometers. We tie these findings to UNE detection by comparing the PolSAR and accelerometer results to empirical ground motion predictions derived from accelerometer recordings of UNEs collected prior to cessation of U.S. nuclear testing. We find the single threshold greater than 1 g hypothesis is not correct for it does not explain the PolSAR results. Our findings show PolSAR surface coherence spatial extent is highly correlated with surface velocity, both measured and predicted, and the resulting surface deformation extent is corroborated by accelerometer records and the predicted lateral spall extent. PolSAR scattering changes measured during SPE-6 are created by the prompt surface displacement being larger than the spall gap.

This article compares the performance of four different interferometric synthetic aperture radar (SAR) techniques for the estimation of rice crop height by means of bistatic TanDEM-X data. Methods based on the interferometric phase alone, on the coherence amplitude alone, on the complex coherence value, and on polarimetric SAR interferometry (PolInSAR) are analyzed. Validation is conducted with reference data acquired over rice fields in Spain during the Science Phase of the TanDEM-X mission. Single- and dual-polarized data are exploited to also provide further insights into the polarization influence on these approaches. Vegetation height estimates from methodologies based on the interferometric phase show a general underestimation for the HH channel (with a bias that reaches around 25 cm in mid-July for some fields), whereas the VV channel is strongly influenced by noisy phases, especially at large incidences [root-mean-square error (RMSE) = 31 cm]. Results show that these approaches perform better at shallower incidences than the methodologies based on coherence amplitude and on PolInSAR, which obtain the most suitable results at steep incidences, with RMSE values of 17 and 23 cm. On the contrary, at shallower incidences, they are highly affected by very low input coherence levels. Hence, they tend to overestimate vegetation height.

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realized by selecting the time series that is characterized by trend changes w.r.t. the historical behavior. In this work, we propose a deep learning approach for change point detection in the InSAR time series. The designed architecture combines long short-term memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and time-gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyze the InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain. [ABSTRACT FROM AUTHOR]

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas. [ABSTRACT FROM AUTHOR]

Thermokarst is a typical process that indicates widespread permafrost degradation in yedoma landscapes. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia is now facing extensive landscape changes with surface subsidence due to thermokarst development during the past few decades. To clarify the spatial extent and rate of subsidence, multiple spatial datasets, including GIS and remote sensing observations, were used to analyze the Churapcha rural locality, which has a typical yedoma landscape in Central Yakutia. Land cover classification maps for 1945 and 2009 provide basic information on anthropogenic disturbance to the natural landscape of boreal forest and dry grassland. Interferometric synthetic aperture radar (InSAR) with ALOS-2/PALSAR-2 data revealed activated surface subsidence of 2 cm/yr in the disturbed area, comprising mainly abandoned agricultural fields. Remote sensing with an unmanned aerial system also provided high-resolution information on polygonal relief formed by thermokarst development at a disused airfield where InSAR analysis exhibited extensive subsidence. It is worth noting that some historically deforested areas have likely recovered to the original landscape without further thermokarst development. Spatial information on historical land-use change is helpful because most areas with thermokarst development correspond to locations where land was used by humans in the past. Going forward, the integrated analysis of geospatial information will be essential for assessing permafrost degradation.

The distribution of free and open access radar satellite datasets, like those of Sentinel-1, has provided new opportunities for monitoring archaeological sites and monuments in a systematic way, and especially after earthquake events. While optical sensors are established in the scientific literature and radar sensors are lately introduced in the relevant literature, the role of satellite-driven ready products is still limited discussed. With the continuous improvement of remote sensing satellite data quality and accuracy, high-resolution data needs to be processed, while at the same time, this required high computational complexity. In respect to this, over the last years, various efforts have been made to support high-performance cloud-based processing, providing to the end-users ready products in a short time. This study presents the results from the exploitation of a relevant new cloud platform, namely the Hybrid Pluggable Processing Pipeline (HyP3) system that integrates GAMMA software, for detecting ground displacement within archaeological sites in Cyprus, after a 5.6 magnitude scale earthquake in 2015. Ascending and descending pairs of Sentinel-1 images, acquired before and after the event, were processed through the HyP3 platform, revealing small relative ground displacements in the area under study. The processing chain was performed in less than 1 h -per pair- on the HyP3 system, indicating that similar approaches could be beneficial in the future to support cultural heritage management of large areas.

The Source Physics Experiment (SPE) Phase I conducted six underground chemical explosions at the same experimental pad with the goal of characterizing underground explosions to enhance the United States (U.S.) ability to detect and discriminate underground nuclear explosions (UNEs). A fully polarimetric synthetic aperture RADAR (PolSAR) collected imagery in VideoSAR mode during the fifth and sixth explosions in the series (SPE-5 and SPE-6). Previously, we reported the prompt PolSAR surface changes cause by SPE-5 and SPE-6 explosions within seconds or minutes of the underground chemical explosions, including a drop of spatial coherence and polarimetric scattering changes. Therein it was hypothesized that surface changes occurred when surface particles experienced upward acceleration greater than 1 g. Because the SPE site was instrumented with surface accelerometers, we explore that hypothesis and report our findings in this article. We equate explosion-caused prompt surface expressions measured by PolSAR to the prompt surface movement measured by accelerometers. We tie these findings to UNE detection by comparing the PolSAR and accelerometer results to empirical ground motion predictions derived from accelerometer recordings of UNEs collected prior to cessation of U.S. nuclear testing. We find the single threshold greater than 1 g hypothesis is not correct for it does not explain the PolSAR results. Our findings show PolSAR surface coherence spatial extent is highly correlated with surface velocity, both measured and predicted, and the resulting surface deformation extent is corroborated by accelerometer records and the predicted lateral spall extent. PolSAR scattering changes measured during SPE-6 are created by the prompt surface displacement being larger than the spall gap. [ABSTRACT FROM AUTHOR]

Landslides are the most common natural hazard in British Columbia. The province has recorded the largest number of historical landslide fatalities in Canada, and damage to infrastructure comes at a great cost. In order to understand the potential impacts of landslides, radar remote sensing has become a cost-effective method for detecting downslope movements. This study investigates downslope movements in the Southern Interior of British Columbia, Canada, with Sentinel-1 and RADARSAT Constellation Mission (RCM) interferometric synthetic aperture radar (InSAR) data. The 2-dimensional time-series analysis with Sentinel-1 ascending and descending InSAR pairs from October 2017 to June 2021 observed distinct earthflow movements of up to ~15 cm/year in the east–west direction. The Grinder Creek, Red Mountain, Yalakom River, and Retaskit Creek earthflows previously documented are still active, with east–west movements of ~30 cm over the past four years. New RCM data acquired from June 2020 to September 2020 with a 4-day revisit capability were compared to 12-day Sentinel-1 InSAR pairs. The 4-day RCM InSAR pairs at higher spatial resolution showed better performance by detecting relatively small-sized slope movements within a few hundred meters, which were not clearly observed by Sentinel-1. The temporal variabilities observed from the RCM InSAR showed great potential for observing detailed slope movements within a narrower time window.

We investigated deep‐seated gravitational slope deformation (DSGSD) and slow mass movements in the southern Tien Shan Mountains front using synthetic aperture radar (SAR) time‐series data obtained by the ALOS/PALSAR satellite. DSGSD evolves with a variety of geomorphological changes (e.g. valley erosion, incision of slope drainage networks) over time that affect earth surfaces and, therefore, often remain unexplored. We analysed 118 interferograms generated from 20 SAR images that covered about 900 km2. To understand the spatial pattern of the slope movements and to identify triggering parameters, we correlated surface dynamics with the tectono‐geomorphic processes and lithologic conditions of the active front of the Alai Range. We observed spatially continuous, constant hillslope movements with a downslope speed of approximately 71 mm year−1 velocity. Our findings suggest that the lithological and structural framework defined by protracted deformation was the main controlling factor for sustained relief and, consequently, downslope mass movements. The analysed structures revealed integration of a geological/structural setting with the superposition of Cretaceous–Paleogene alternating carbonatic and clastic sedimentary structures as the substratum for younger, less consolidated sediments. This type of structural setting causes the development of large‐scale, gravity‐driven DSGSD and slow mass movement. Surface deformations with clear scarps and multiple crest lines triggered planes for large‐scale deep mass creeps, and these were related directly to active faults and folds in the geologic structures. Our study offers a new combination of InSAR techniques and structural field observations, along with morphometric and seismologic correlations, to identify and quantify slope instability phenomena along a tectonically active mountain front. These results contribute to an improved natural risk assessment in these structures. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

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Urban infrastructures have become imperative to human life. Any damage to these infrastructures as a result of detrimental activities would accrue huge economical costs and severe casualties. War in particular is a major anthropogenic calamity with immense collateral effects on the social and economic fabric of human nations. Therefore, damaged buildings assessment plays a prominent role in post-war resettlement and reconstruction of urban infrastructures. The data-analysis process of this assessment is essential to any post-disaster program and can be carried out via different formats. Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) techniques help us to establish a reliable and fast monitoring system for detecting post-war damages in urban areas. Along this thread, the present study aims to investigate the feasibility and mode of implementation of Sentinel-1 SAR data and InSAR techniques to estimate post-war damage in war-affected areas as opposed to using commercial high-resolution optical images. The study is presented in the form of a survey to identify urban areas damaged or destroyed by war (Islamic State of Iraq and the Levant, ISIL, or ISIS occupation) in the city of Mosul, Iraq, using Sentinel-1 (S1) data over the 2014–2017 period. Small BAseline Subset (SBAS), Persistent Scatterer Interferometry (PSI) and coherent-intensity-based analysis were also used to identify war-damaged buildings. Accuracy assessments for the proposed SAR-based mapping approach were conducted by comparing the destruction map to the available post-war destruction map of United Nations Institute for Training and Research (UNITAR); previously developed using optical very high-resolution images, drone imagery, and field visits. As the findings suggest, 40% of the entire city, the western sectors, especially the Old City, were affected most by ISIS war. The findings are also indicative of the efficiency of incorporating Sentinel-1 SAR data and InSAR technique to map post-war urban damages in Mosul. The proposed method could be widely used as a tool in damage assessment procedures in any post-war reconstruction programs.

Multitemporal Phased Array type L-band Synthetic Aperture Radar (PALSAR) horizontally transmitted and horizontally received (HH) coherence data was decomposed into temporal-coherence, spatial-coherence, and thermal noise components. The multitemporal data spanned between February and May of 2008, and consisted of two pairs of interferometric SAR (InSAR) images formed by consecutive repeat passes. With the analysis of ancillary data, a snow increase process and a snow decrease process were determined. Then, the multiple temporal-coherence components were used to study the variation of thawing and freezing statuses of snow because the components can mostly reflect the temporal change of the snow that occurred between two data acquisitions. Compared with snow mapping results derived from optical images, the outcomes from the snow increase process and the snow decrease process reached an overall accuracy of 71.3% and 79.5%, respectively. Being capable of delineating not only the areas with or without snow cover but also status changes among no-snow, wet snow, and dry snow, we have developed a critical means to assess the water resource in alpine areas.

This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km2 area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to ±17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to −63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.

Recent years have seen a surging interest in several novel tools pertaining to SAR, such as multichannel high-resolution and wide-swath (HRWS) SAR, multibaseline interferometric SAR (InSAR), multisubband SAR, polarimetric SAR (PolSAR), and polarimetric SAR interferometry (PolInSAR). We believe that these new approaches to SAR have valuable scientific applications. Here, we present a new experimental airborne SAR system named “N-SAR” (SAR of the Nanjing Research Institute of Electronic Technology) that can fulfill new requirements and is scalable to allow rapid development of modern SARs. As a dual-antenna airborne SAR system, the N-SAR system will be used to test new technologies and signal processing algorithms such as PolSAR and PolInSAR, multibaseline SAR interferometry, multichannel multichannel HRWS SAR/InSAR, and SAR ground moving target indicator. It will play a key role in evaluating the performance of current engineering-oriented SAR systems by using several new operational modes for scientific purposes. In this paper, we provide a conceptual description of the general system design features, instrument design, and capabilities of the N-SAR system. To meet the requirements of different experiments, a novel operational mode, named the alternating bistatic multipolarized mode based on the N-SAR system, is presented. A series of flight tests that started in April 2017 and will be carried out over the next few years are shown. Several preliminary experimental results pertaining to the calibration of PolSAR, multichannel SAR imaging, and interferometry are presented in this paper as an early validation of the capabilities of the N-SAR system. [ABSTRACT FROM AUTHOR]

Satellite Interferometric Synthetic Aperture Radar (InSAR), geological data and Small Unmanned Aerial Vehicle (SUAV) surveying was used to enhance our understanding of ground movement at five areas of interest in Northern Ireland. In total 68 ERS-1/2 images 1992-2000 were processed with the Small Baseline Subset (SBAS) InSAR technique to derive the baseline ground instability scenario of key areas of interest for five stakeholders: TransportNI, Northern Ireland Railways, Department for the Economy, Arup, and Belfast City Council. These stakeholders require monitoring of ground deformation across either their geotechnical infrastructure (i.e., embankments, cuttings, engineered fills and earth retaining structures) or assessment of subsidence risk as a result of abandoned mine workings, using the most efficient, cost-effective methods, with a view to minimising and managing risk to their businesses. The InSAR results provided an overview of the extent and magnitude of ground deformation for a 3000 km² region, including the key sites of the disused salt mines in Carrickfergus, the Belfast-Bangor railway line, Throne Bend and Ligoniel Park in Belfast, Straidkilly and Garron Point along the Antrim Coast Road, plus other urbanised areas in and around Belfast. Tailored SUAV campaigns with a X8 airframe and generation of very high resolution ortho-photographs and a 3D surface model via the Structure from Motion (SfM) approach at Maiden Mount salt mine collapse in Carrickfergus in 2016 and 2017 also demonstrate the benefits of very high resolution surveying technologies to detect localised deformation and indicators of ground instability. [ABSTRACT FROM AUTHOR]

Differential synthetic aperture radar interferometry (DInSAR) has shown its capability in monitoring ground displacement caused by the freeze-thaw cycle in the active layer of permafrost regions. However, the unique landscape in the discontinuous permafrost zone increases the difficulty of applying DInSAR to detect ground displacements. In this study, datasets from two radar systems, X-band TerraSAR-X and L-band ALOS PALSAR, were used to evaluate the influencing factors and application conditions for DInSAR in the discontinuous permafrost environment based on a large number of analyzed interferograms. Furthermore, the impact of different DEMs on the application of DInSAR was illustrated by comparing the high-resolution LiDAR-DEM, TanDEM-X DEM, and SRTM DEM. The results demonstrate that temporal decorrelation and strong volume decorrelation in areas with developed vegetation highly constrains the application of X-band data. In terrain with more developed vegetation (such as shrubs and spruce), the X-band differential phase becomes linked to the canopy rather than the topography, whereas L-band data show promising results in retrieving topography-related displacement. By comparing the displacement velocity maps of the two sensors and referencing in situ measurements, we demonstrated that the ALOS PALSAR results capture the permafrost-induced terrain movement characteristics and values in the correct range. Moreover, the influence of soil moisture and vegetation phenology on the accuracy of displacement retrievals using the L-band data are illustrated and discussed. The analyses confirm that the L-band has strong advantages over the X-band in monitoring displacements in discontinuous permafrost environments. [ABSTRACT FROM PUBLISHER]

Advanced interferometric synthetic aperture radar (InSAR) postprocessing, like persistent scatterer InSAR (PS-InSAR), offers the possibility to investigate slow moving landslides, where standard interferometry is problematic. These advanced algorithms involve the analysis of a series of SAR acquisitions in both time and space. One input that requires particular attention for landslide applications is the external digital elevation model (DEM) that is used to correct the interferograms for the topographic phase term. When multiple elevation data sets are available for a given study area, it is difficult to decide which one should be used. In this paper, we test the sensitivity of PS-InSAR/Small Baseline Subset (SBAS) results to different DEMs. The study area is located in the Northern Apennines of Italy, where chaotic clay shales and fine-grained flysch host slow-moving earth flows and ancient rock slides. $C$ -band (Envisat) and $X$ -band (Cosmo-SkyMed) data are processed with different DEMs. We describe a simple framework to statistically analyze the influence of these models on the final PS-InSAR/SBAS results. We find that individual interferograms do not vary much depending on the DEM, while the results from PS-InSAR and SBAS analysis do vary. This is likely caused by the way the DEM error is estimated. We find also that the quality of the DEM is more important than the resolution and that $X$ -band InSAR data are more sensitive to the choice of the DEM than $C$ -band. The significance of the results is discussed with reference to two landslide areas. [ABSTRACT FROM PUBLISHER]

A real-time monitoring system based on ground-based synthetic aperture radar interferometry (GB-InSAR) were developed and deployed with the objective to gain understanding of landslide dynamics in an area in Xerém (district of Duque de Caxias - RJ, Brazil). The area is subjected to landslide risk and the deployed system aims to provide subsidies to the authorities to prepare actions related to prevention and evacuation, and serve as the basis for a future early-warning system in the framework of the Civil Defense of Duque de Caxias. The system consists of a compact and rugged X-band GB-InSAR newly developed by Bradar, Brazil. The processing and monitoring methodology are proposed. Two campaigns, totaling six days, were carried out. The acquisitions of the first campaign occurred without rain. In the second campaign, rain events, varying from light to torrential rainfall, occurred for a period of time, in subsequent days, during the radar acquisitions. New insights on landslide dynamics are revealed. The monitoring data show that, approximately 10 h after heavy rainfall, the terrain movement on a slope reached 4 mm/h during ca. 5 to 8 h, resulting in velocities of 20 to 32 mm/day. [ABSTRACT FROM PUBLISHER]

The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.

Phase unwrapping is a key issue in InSAR research. As a critical step of InSAR processing, it affects the accuracy of interferometry measurement directly. The efficiency of the traditional quality-guided phase unwrapping method is low due to a great deal of sorting, espceically for large interferogram. This paper proposes a highly efficient quality-guided phase unwrapping method based on heap sort in order to solve the problem. First, the quality map is caculated according to the interferometric complex data or interferogram. Next, with the max-heap acting as the data structure of sorting, its property is maintained while deleting root node and inserting new node, and thus the sorting of quality map is accomplished and the phase can be unwrapped from high quality areas to low quality areas. The improved algorithm reduces the computational complexity greatly compared with traditional methods, which is significant in large area mapping of InSAR. At the end of the paper, the simulated and experimental results show the accuracy and the efficiency of the algorithm.

This paper presents a new filtering approach for Synthetic Aperture Radar (SAR) interferometric phase noise reduction in the shearlet domain, depending on the coherent statistical characteristics. Shearlets provide a multidirectional and multiscale decomposition that have advantages over wavelet filtering methods when dealing with noisy phase fringes. Phase noise in SAR interferograms is directly related to the interferometric coherence and the look number of the interferogram. Therefore, an optimal interferogram filter should incorporate information from both of them. The proposed method combines the phase noise standard deviation with the shearlet transform. Experimental results show that the proposed method can reduce the interferogram noise while maintaining the spatial resolution, especially in areas with low coherence. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR) has demonstrated its potential for high-density spatial mapping of ground displacement associated with earthquakes, volcanoes, and other geologic processes. However, this technique may be affected by the ionosphere, which can result in the distortions of Synthetic Aperture Radar (SAR) images, phases, and polarization. Moreover, ionospheric effect has become and is becoming further significant with the increasing interest in low-frequency SAR systems, limiting the further development of InSAR technique. Although some research has been carried out, thorough analysis of ionospheric influence on true SAR imagery is still limited. Based on this background, this study performs a thorough investigation of ionospheric effect on InSAR through processing L-band ALOS-1/PALSAR-1 images and dual-frequency Global Positioning System (GPS) data over Hong Kong, where the phenomenon of ionospheric irregularities often occurs. The result shows that the small-scale ionospheric irregularities can cause the azimuth pixel shifts and phase advance errors on interferograms. Meanwhile, it is found that these two effects result in the stripe-shaped features in InSAR images. The direction of the stripe-shaped effects keep approximately constant in space for our InSAR dataset. Moreover, the GPS-derived rate of total electron content change index (ROTI), an index to reflect the level of ionospheric disturbances, may be a useful indicator for predicting the ionospheric effect for SAR images. This finding can help us evaluate the quality of SAR images when considering the ionospheric effect. [ABSTRACT FROM AUTHOR]

Sentinel-1 (S-1) has an unparalleled mapping capacity. In interferometric wide swath (IW) mode, three subswaths imaged in the novel Terrain Observation by Progressive Scans (TOPS) SAR mode result in a total swath width of 250 km. S-1 has become the European workhorse for large area mapping and interferometric monitoring at medium resolution. The interferometric processing of TOPS data however requires special consideration of the signal properties, resulting from the ScanSAR-type burst imaging and the antenna beam steering in azimuth. The high Doppler rate in azimuth sets very stringent coregistration requirements, making the use of enhanced spectral diversity (ESD) necessary to obtain the required fine azimuth coregistration accuracy. Other unique aspects of processing IW data, such as azimuth spectral filtering, image resampling, and data deramping and reramping, are reviewed, giving a recipe-like description that enables the user community to use S-1 IW mode repeat-pass SAR data. Interferometric results from S-1A are provided, demonstrating the mapping capacity of the S-1 system and its interferometric suitability for geophysical applications. An interferometric evaluation of a coherent interferometric pair over Salar de Uyuni, Bolivia, is provided, where several aspects related to coregistration, deramping, and synchronization are analyzed. Additionally, a spatiotemporal evaluation of the along-track shifts, which are directly related to the orbital/instrument timing error, measured from the SAR data is shown, which justifies the necessity to refine the azimuth shifts with ESD. The spatial evaluation indicates high stability of the azimuth shifts for several slices of a datatake. [ABSTRACT FROM PUBLISHER]

Several current and expected future SAR satellites missions (e.g., TanDEM-X (TDX)/PAZ, COSMO-SkyMed (CSK), and Sentinel-1A/B) are designed as constellations of SAR sensors. Relative to single satellite systems, such constellations can provide greater spatial coverage and temporal sampling, thereby enabling better control on interferometric decorrelation and lower latency data access. These improvements lead to more effective near real-time disaster monitoring, assessment and response, and a greater ability to constrain dynamically changing physical processes. Using observations from the CSK system, we highlight examples of the potential for such imaging capabilities to enable advances in Earth science and natural hazards response. [ABSTRACT FROM PUBLISHER]