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We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

Differential Synthetic Aperture Radar Interferometry (DInSAR) represents by now an established tool for the remote monitoring of the ground displacements occurring on our planet. In addition, this technique is benefiting from the recent advancements in radar, navigation and aeronautical technologies. These indeed allow us on one side to avail of wide-coverage SAR satellites to observe larger and larger areas of the Earth’s surface. On the other side, they make it possible to profit of cost-efficient aerial SAR systems, whose operative flexibility can play a crucial role to observe phenomena possibly hidden to satellite sensors and for the prompt monitoring in emergency scenarios. In this work, we provide the results of a ground deformation analysis performed over the municipality of Vazante, Brazil, based on the DInSAR processing of the data acquired by both the space-borne C-band Sentinel-1A and the drone-borne multi-frequency (P/L/C-bands) DBSS SAR systems.

This work presents the first results obtained by applying in situ and remote-sensing methodologies to monitor the Ponte della Musica-Armando Trovajoli located in Rome, within the activities of the WP6 “Structural Health Monitoring and Satellite Data” 2019-21 Reluis Project. In particular, the use of remote-sensing Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) measurements provided a spatial map of the displacement of the investigated infrastructure and the corresponding time-series, with the aim of monitoring deformation phenomena, focusing on the local scale analysis, which produces suitable results for urban monitoring and damage assessment. The DInSAR results have been integrated with the identification of the dynamic characteristics of the bridge, performed through an experimental campaign of ambient vibration measurements carried out in October 2020 and with the local-scale definition of the engineering geological setting of the foundation soil. The subsoil of the bridge is constituted by more than 50 m of recent alluvial deposits resting on Pliocene stiff clay acting as a geological bedrock. A substantially stable behavior of the bridge structural elements has been observed based on the analysis of both satellite and velocimetric data. This case represents a good example about how the integration of in situ sensors with remotely sensed data and the exploitation of a detailed knowledge regarding the on-site conditions represent a key factor for a sustainable structural and infrastructural monitoring and can support the planning both of maintenance and safety management.

We present the implementation of a global archive of Differential Synthetic Aperture Radar Interferometry (DInSAR) co-seismic deformation maps. The archive has been generated by automatically processing all the Copernicus Sentinel-1 data spanning about 300 significant (at least Mw > 5.5) earthquakes all over the Earth. An empirical relation between magnitude and epicenter depth was considered to limit the study to those earthquakes that can likely induce ground deformation. DInSAR processing has been carried out within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, to benefit of high processing capacity. The generated DInSAR results are then made freely and openly available to the Solid Earth scientific community through the European Planet Observing System (EPOS) Research Infrastructure.

In this work, we present the ground deformation analysis of the Italian Peninsula carried out via the Sentinel-1 (S-1) Parallel Small BAseline Subset (P-SBAS) approach. In particular, we generate surface deformation time-series and the corresponding mean deformation velocity maps by considering the overall archive of S-1 data acquired from both ascending and descending orbits during the March 2015 - December 2018 period. Moreover, we perform a large-scale comparison of the retrieved results with the deformation measurements of the available GPS stations deployed on the Italian territory and provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL). Finally, we properly combine the ascending and descending deformation time-series in order to compute the vertical and east-west components of the retrieved surface displacements. The achieved results demonstrate the capability of the P-SBAS processing chain to efficiently process large S-1 datasets to investigate ground deformation dynamics of large portions of the Earth surface.

Airborne Synthetic Aperture Radar (SAR) systems are gaining increasing interest within the remote sensing community due to their operational flexibility and observation capabilities. Among these systems, those exploiting the Frequency-Modulated Continuous-Wave (FMCW) technology are compact, lightweight, and comparatively low cost. For these reasons, they are becoming very attractive, since they can be easily mounted onboard ever-smaller and highly flexible aerial platforms, like helicopters or unmanned aerial vehicles (UAVs). In this work, we present the imaging and topographic capabilities of a novel Italian airborne SAR system developed in the frame of cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The system, which is named AXIS (standing for Airborne X-band Interferometric SAR), is based on FMCW technology and is equipped with a single-pass interferometric layout. In the work we first provide a description of the AXIS system. Then, we describe the acquisition campaign carried out in April 2018, just after the system completion. Finally, we perform an analysis of the radar data acquired during the campaign, by presenting a quantitative assessment of the quality of the SLC (Single Look Complex) SAR images and the interferometric products achievable through the system. The overall analysis aims at providing first reference values for future research and operational activities that will be conducted with this sensor.

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

We present in this work a methodology for computing surface deformation time series and mean velocity maps of large areas. Our approach relies on the availability of a multi-temporal set of synthetic aperture radar (SAR) data collected from ascending and descending orbits over an area of interest, and also permits us to estimate the vertical and horizontal (East-West) components of the Earth's surface deformation. The adopted methodology is based on an advanced cloud computing implementation of the differential SAR interferometry (DInSAR) Parallel Small Baseline Subset (P-SBAS) processing chain which allows the unsupervised processing of large SAR data volumes, from the raw data (level-0) imagery up to the generation of the corresponding DInSAR time series and maps. The solution presented, which is highly scalable, has been tested on ascending and descending ENVISAT SAR archives comprising approximately 400 GB of data, which have been acquired over a large area of southern California (US) that extends over about 90,000 km 2 . Such an input dataset has been processed in parallel by exploiting 280 computing nodes of the Amazon Web Services Cloud environment. The overall processing lasted about 8 h and cost approximately $1900 USD. Moreover, to produce the final mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area, we also took advantage of the information available from external GPS measurements that permit us to account for possible regional trends not easily detectable by DInSAR and to refer the P-SBAS measurements to an external geodetic datum. The results presented clearly demonstrate the effectiveness of the proposed approach that paves the way to the extensive use of the available ERS-1/2 and ENVISAT SAR data archives. Furthermore, the proposed methodology can be particularly suitable to deal with the very large data flow provided by the Sentinel-1 constellation, thus permitting the extension of the DInSAR analyses at a nearly global scale.

FUNDAMENTALS Introduction Historical Background Synthetic Aperture Radar Systems Modes Geometric Resolution Goemetric Distortions Synthetic Aperture Radar Signal Statistics InterferometricSynthetic Aperture Radar Phase Statistics Radiometric Resolution Ambiguity Considerations Power and Noise Considerations STRIP MODE TRANSFER FUNCTION Signal Analysis in Time Domain Synthetic Aperture Radar Transfer Function Squinted Geometry Earth's Rotation and Sensor Orbit Effects Reflectivity Pattern STRIP MODE DATA PROCESSING Point Target Response Synthetic Aperture Radar Transfer Function and its Approximations Narrow Focus Synthetic Aperture Radar Processing Wide Focus Syntheric Aperture Radar Processing Efficient Wide Focus Synthetic Aperture Radar Processing Range-Doppler Synthetic Aperture Radar Processing Motion Compensation Multiple Look Synthetic Aperture Radar Image Generation Estimation Procedures for Synthetic Aperture Radar Parameters SYNTHETIC APERTURE RADAR INTERFEROMETRY Introduction Interferometric Synthetic Aperture Radar Processing Interferometric Phase Noise Image Registration Techniques Interferometric Phase Statistics Decorrelation Effects Digital Elevation Model Accuracy Phase Unwrapping Weighted Phase Unwrapping Via Finite Element Method Geocoding Differential Interfermetric Synthetic Aperture Radar SCAN MODE SIGNAL ANALYSIS AND DATA PROCESSING Time Domain Analysis Frequency Domain Analysis Point Target Image Generation Scan Mode Data Processing SPOT MODE SIGNAL ANALYSIS AND DATA PROCESSING Time Domain Analysis Frequency Domain Analysis Bandwidths Considerations Residual Video Phase Compensation Spot Mode Image Generation PROCESSING CODE EXAMPLE Code Presentation Processing Code INDEX

In last years the Frequency Modulated Continuous Wave (FMCW) technology has been playing an ever greater role in the realization of compact, lightweight and cheap Synthetic Aperture Radar (SAR) systems to be mounted onboard small, low altitude platforms such as airplanes, helicopters and drones. In this work, we present the imaging capabilities of the AXIS (Airborne X-band Interferometric SAR) system, which is an Italian airborne interferometric SAR system operating at X- Band and based on the FMCW technology. The system has been developed in the frame of a cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The first flight tests of the system have been carried out at the end of April 2018 over the Salerno coastline, in the South of Italy, onboard a Cessna 172 aircraft. In this paper, we show a preview of the obtained preliminary results, comprising a description on the mission, an analysis of both the SAR images and the interferometric products.

We present a new solution for the phase-preserving focusing of synthetic aperture radar (SAR) raw data acquired through the Terrain Observation with Progressive Scan (TOPS) mode. The proposed algorithm consists of a first interpolation stage of the TOPS raw data, which takes into account the Doppler Centroid frequency variations due to the azimuth antenna steering function, and allows us to unfold the azimuth spectra of the TOPS raw data. Subsequently, the interpolated signals are processed by using conventional phase-preserving SAR focusing methods that exploit frequency domain and spectral analyses algorithms, which are extensively used to efficiently process Stripmap and ScanSAR data. Accordingly, the developed focusing approach is easy to implement. In particular, the presented focusing approach exploits one of the available frequency domain Stripmap processing techniques. The only modification is represented by the inclusion, within the 2D frequency domain focusing step, of a spurious azimuth chirp signal with a properly selected azimuthal rate. This allows us to efficiently carry out the TOPS azimuth focusing through the SPECAN method. Furthermore, an important aspect of this algorithm is the possibility to easily achieve a constant and tunable output azimuth pixel size without any additional computing time, this is a remarkable feature with respect to the full-aperture TOPS-mode algorithms available in the existing literature. Moreover, although tailored on Sentinel-1 (S1) raw data, the proposed algorithm can be easily extended to process data collected through the TOPS mode by different radar sensors. The presented experimental results have been obtained by processing real Sentinel-1 raw data and confirm the effectiveness of the proposed algorithm.

The Differential Synthetic Aperture Radar Interferometry (DInSAR) remote sensing technique permits to investigate the temporal behaviour of the detected displacements through the generation of the deformation time-series. In this scenario the Phase Unwrapping (PhU) is a crucial point where several errors could occur since the problem is intrinsically ill-posed. In this paper we present a technique to correct unavoidable PhU errors and limit their impact in the final deformation time-series. The proposed approach works pixel-by-pixel and is based on the combination of an L1-norm inversion for the identification of the possible PhU errors and a genetic algorithm (GA) for the search of the best fitting solution. We include the technique in the Small Baseline Subset (SBAS) DInSAR processing chain to correct the results of the Extended Minimum Cost Flow algorithm, but in principle it can be used as a correction step after a generic PhU procedure used in SBAS. Results from MonteCarlo simulations are shown in this paper while real data cases will be presented at the conference.

We investigate the deformation of Mt. Etna volcano (Italy) by exploiting the DInSAR technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we take advantage of the multi-sensor data processing capability of the SBAS algorithm which allows us to generate Mt. Etna mean deformation velocity maps and the corresponding time series in the last twenty-five years. To achieve this task, we exploit different set of SAR data collected by ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 radar sensors in the 1992-2018 time interval. We, also, show the deformation pattern induced by the recent seismo-volcanic crisis occurring from 24 to 27 December 2018. The main results show that the East-West displacement map highlights the most significant entities, whose maximum values exceed 30 cm towards the West and 40 towards the East on the summit of the volcano. In this scenario, the measurements permit to model the geometry and characteristics of the causative deformation sources; preliminary results suggest the presence of two different sources: a shallow dyke feeding the eruptive activity at summit craters and a major strike-slip fault/s on the southeastern flank nucleating the 26 th mainshock.

The retrieval of soil moisture and surface roughness from P-Band Synthetic Aperture Radar (SAR) data is here addressed. In particular, the presented methodology is based on the Polarimetric Two Scale Model (PTSM) recently proposed in the literature, and exploits the combination of SAR data collected along two antiparallel flight-tracks flown on the same area with a very short revisit time. Some considerations about the effects of the SAR acquisition geometry on the retrieval results are also provided. The used dataset is relevant to a desert campaign carried out in 2018 with the helicopter-borne P-band SAR system owned by the Italian Space Agency.

In this work we present a cloud-computing based strategy for generating surface displacement time series and mean deformation velocity maps of very large areas by exploiting Sentinel-1 (S1) data. Our approach relies on the simultaneous exploitation of a fast access to the S1 data archives, High Performance Computing resources and external geodetic data. The presented pipeline is based on an advanced cloud- computing implementation of the differential SAR interferometry (DInSAR) Parallel Small BAseline Subset (P-SBAS) processing chain, which allows the fully unsupervised processing of huge Interferometric Wide Swath (IWS) Sentinel-1 data volumes. In particular, for what concerns the S1 data archives, we benefited from the NASA’s Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), which stores and distributes S1 SAR data from Amazon Web Service (AWS) for advancing Earth science research. The ASF- DAAC allowed us to reach a very high performance level in terms of downloading time and system reliability. Moreover, we exploited the geodetic measurements provided from the Magnet + Global GPS Network Map of the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL), which supply GPS measurements daily updated and continuosly available. The GPS measurements were used to account for regional trends and to better discriminate the low (tectonic) and high (local) frequency deformation patterns. The presented solution is highly scalable and has been migrated to the Amazon Web Service (AWS) environment. It runs out along an automatic routine to generate the mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area. The developed pipeline has been tested on ascending and descending Sentinel-1 archives acquired over a large area of Southern California (US), which extends over about 150,000 square kilometers.

In this work, we present some results of the flight-test campaign carried out with the AXIS system, which is an Italian single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR) based on the frequency modulated continuous wave technology. The system has recently been developed in the frame of a cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave). The campaign was performed in 2018 over the Salerno area, South of Italy, and some corner reflectors (CRs) were deployed over the illuminated area with the aim of verifying the system performances in terms of geometric resolution and target location.

This work is aimed at showing the present capabilities and future potentialities of an imaging radar system that can be mounted onboard flexible aerial platforms, such as helicopters or small airplanes, and may operate in the UHF and VHF frequency bands as Sounder and as Synthetic Aperture Radar (SAR). More specifically, the Sounder operates at 165 MHz, whereas the SAR may operate either at 450 MHz or at 860 MHz. In the work, we present the first results relevant to a set of Sounder and SAR data collected by the radar during a helicopter-borne campaign conducted in 2018 over a desert area in Erfoud, Morocco, just after the conclusion of a system upgrading procedure. In particular, a first analysis of the focusing capabilities of the Sounder mode and of the polarimetric and interferometric capabilities of the SAR mode is conducted. The overall system, originally developed by CO.RI.S.T.A. according to a ASI funding set up in 2010, has been upgraded in the frame of a contract signed in 2015 between ASI and different private and public Italian Research Institutes and Universities, namely CO.RI.S.T.A., IREA-CNR, Politecnico di Milano and University of Trento.

In this work, a time-domain procedure is presented to focus airborne Synthetic Aperture Radar (SAR) data collected in the presence of strong variations of the squint angle during the radar acquisition. The approach is tested on two datasets acquired by two different X-band airborne SAR systems.

We present an unsupervised tool for the automatic generation of co-seismic displacement maps by using satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. The tool relies on the use of Sentinel-1 SLC Interferometric Wide Swath (IWS) data and main earthquake information, the latter retrieved from on-line global catalogs. In particular, the tool collects location, depth and magnitude of a seismic event and immediately triggers the data download and the interferometric process over the area affected by the earthquake. The generated DInSAR results (mainly interferograms and displacement maps) are then made available to the Solid Earth scientific community through the EPOS Research Infrastructure. Moreover, the implemented system is also used to serve the Italian Department of Civil Protection in case of main seismic events in Italy. Finally, due to the Sentinel-1 characteristics, the obtained results can contribute to the generation of a global database of DInSAR co-seismic displacement maps.

In this work we implement a completely automatic interferometric processing chain, based on the well-known advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-l (S-1) Interferometric Wide Swath (IWS) deformation mean velocity maps and time-series of very wide areas, implemented within the Amazon Web Services (AWS) Elastic Cloud Compute environment. Our processing chain consists of the initial data query to the S-1 archive that we created on the AWS S3 storage, then the data transfer to the AWS computing nodes, the data processing and, finally, the transfer of the obtained interferometric results back to the original S3 storage. In order to demonstrate the capability of the implemented Cloud-based processing chain to deal with massive amount of data, we focus our analysis on the whole Italian territory by processing all the available data acquired both from ascending and descending orbits within the October 2014 - March 2017 time interval. As final result we combine the retrieved LOS displacements in order to compute the mean velocity maps and time-series of the vertical and East-West surface deformation components.

In this work we present a fully parallel SBAS-DInSAR processing chain for the effective and massive generation of Sentinel-1 (S-1) Interferometric Wide Swath (IWS) deformation time-series. The pursued strategy to develop such a parallel processing chain fully benefits from the data acquisition characteristics of the TOPS mode, consisting of a series of bursts that can be considered as separate images to be independently processed. Such a data structure fosters the intrinsic parallelization of the S-1 IWS interferometric data processing, which can automatically be carried out through the exploitation of high-performance distributed computing infrastructures, and of efficient algorithms for tackling the huge data flow provided by the S-1 constellation. In order to demonstrate the capability of the presented S-1 P-SBAS processing chain to deal with the huge amount of data acquired by the S-1 constellation, we process a very extended dataset acquired over Italy from which we compute the mean velocity maps and corresponding deformation time-series.

In this paper we derive a simple model to describe the distortions on focused airborne SAR data rising from the usage of inaccurate external information of the topography of the illuminated area. The model is based on the assumption that an accurate time-domain strategy is applied to focus the data. Moreover, perfect knowledge of the aircraft trajectory is also assumed, in order to better highlight the relation between true topography and airborne SAR focused data. The presented model takes into account the coupling between the trajectory deviations affecting the aircraft flight track and the topography errors. A closed form expression for both the amplitude and phase distortions corrupting the focused signal is provided. To validate the presented analysis some simulation results relevant to a realistic SAR acquisition scenario are finally shown.

This letter aims to investigate the applicability of the hybrid stripmap–ScanSAR differential synthetic aperture radar interferometry to data collected by the X-band RADAR sensors of the Italian COSMO-SkyMed (CSK) constellation. The presented analysis relies on the experiments carried out on two sets of stripmap and ScanSAR CSK images collected over the Chuquicamata copper mine in Chile and the Mojave Desert in Southern California, respectively. As a result of our investigation, a spurious phase modulation has been recognized in the generated hybrid stripmap–ScanSAR interferograms along the azimuth direction. This effect is due to the specific staggered topology of the used active phased array antennas that introduces half-wavelength residuals during the electronic antenna steering in the elevation plane. A straightforward solution to compensate for such spurious phase modulation effects has been provided, and the achieved results demonstrate its effectiveness. The outcomes of our analysis may be relevant in the perspective of the future CSK Second Generation Program.

We present an automatic pipeline implemented within the Amazon Web Services (AWS) Cloud Computing platform for the interferometric processing of large Sentinel-1 (S1) multi-temporal SAR datasets, aimed at analyzing Earth surface deformation phenomena at wide spatial scale. The developed processing chain is based on the advanced DInSAR approach referred to as Small BAseline Subset (SBAS) technique, which allows producing, with centimeter to millimeter accuracy, surface deformation time series and the corresponding mean velocity maps from a temporal sequence of SAR images. The implemented solution addresses the aspects relevant to i) S1 input data archiving; ii) interferometric processing of S1 data sequences, performed in parallel on the AWS computing nodes through both multi-node and multi-core programming techniques; iii) storage of the generated interferometric products. The experimental results are focused on a national scale DInSAR analysis performed over the whole Italian territory by processing 18 S1 slices acquired from descending orbits between March 2015 and April 2017, corresponding to 2612 S1 acquisitions. Our analysis clearly shows that an effective integration of advanced remote sensing methods and new ICT technologies can successfully contribute to deeply investigate the Earth System processes and to address new challenges within the Big Data EO scenario.

This paper aims at investigating the use of microwave and optical images for the detection and characterization of fire scars in vegetated areas. To cope with this issue, Sentinel-1 (S-1) C-band synthetic aperture radar (SAR), and multispectral Sentinel-2 (S-2) multispectral data are used. First, a consolidated fuzzy-based methodology, specifically designed for retrieving burned areas, is considered. Then, an investigation based on the analysis of the maps of backscattering coefficients, as obtained by processing stacks of SAR images, is performed. The outcomes obtained with SAR data are validated by comparison with the results obtained with the optical multispectral data. Presented experiments are related to the Soberanes fires, California, which ignited on July 22, 2016, and affected more than 400 square kilometers. In addition, we explore the potential use of stacks of spatial coherence maps generated from pairs of complex-valued SAR images that encompass a fire event to recover new, additional information on the vegetation state and extent of the fire scars. To the purpose, stacks of S-1 SAR images acquired at different polarizations over the Mt. Vesuvius, Italy, in the 2017 summer season has been used. Some preliminary results, which evidence how the variations of the spatial coherence values can be somehow related to a fire and used to recover additional pieces of information, are presented.

This paper shows the design parameters and specifications of the AXIS system, which is an Italian single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR) based on the frequency modulated continuous wave technology. The activity is framed within a cooperation agreement signed between Elettra Microwave S.r.l and the Institute for Electromagnetic Sensing of the Environment (IREA) of the Italian National Research Council (CNR).

In the theoretical purview of the discrete Calculus, a rigorous gradient-based formulation of the multichannel phase unwrapping (MCh-PhU) problem is systematically established in terms of discrete differential operators, which are defined by the topology of the intrinsically discrete spaces upon which they act, thus capturing the essential topological character of the problem within a suitable matrix formalism and providing interesting implications. Within this methodological framework, the extended minimum cost flow (EMCF) algorithm, which provides an effective strategy aimed at solving the MCh-PhU problem, is revised, and its computational structure is analyzed. A parallel formulation of the computational-intensive EMCF algorithm is then presented. Emphasis is placed on the methodological and practical aspects leading to a novel dual-level parallel computational model in which the parallelism is hierarchically implemented at two different levels. Performance evaluation relevant to the implemented prototype solution is also carried out, thus quantifying the benefit of parallelism at different levels. The significant experimentally achieved speedup demonstrates the validity of our approach. As a result, the attained parallel prototype enables the large-scale solution of the MCh-PhU problem in a reasonable time frame, with a great impact on systematic exploitation of the available SAR archives.

This paper discusses, through two selected case studies based on real data, how the availability of the new generation of Synthetic Aperture Radar (SAR) sensors, characterized by reduced revisiting time and improved spatial resolution or coverage, is impacting the exploitation of Differential SAR Interferometry (DInSAR) techniques for the detection and monitoring of deformation phenomena. The presented analysis is carried out using X-band data of the COSMO-SkyMed constellation satellites, as well as C-band data acquired by the Sentinel-1A sensor; furthermore, we compare the achieved results to those based on first-generation ERS-1/2 and ENVISAT satellite data. The first case study shows how the COSMO-SkyMed X-band SAR systems open new opportunities for the detection and monitoring of deformation phenomena at the scale of a single building, even when they are characterized by a rather fast dynamic. The second experiment is based on the Sentinel-1A DInSAR measurements and permits us to envisage new scenarios for deformation analysis of very wide areas. The final discussion is devoted to summarise the lessons learned through the presented case studies and to identify the main future actions needed for a full exploitation of the surface deformation measurement capability provided by the new generation of SAR sensor.

Digital elevation model (DEM) generation through interferometric processing of synthetic aperture radar (SAR) data requires the calculation of a constant phase offset present in the unwrapped interferograms. This operation is usually carried out by exploiting the external information provided by GPS measurements in correspondence of corner reflectors (CRs) properly deployed over the illuminated area. This is, however, expensive in terms of cost and time. Moreover, deployment of CRs along with the corresponding in situ GPS measurements can be difficult (if not impossible) in unfriendly areas or in natural disaster scenarios. To circumvent these limitations, we address in this work the estimation of the required phase offset by exploiting a low-accuracy external DEM, without using CRs. More specifically, a two-step approach is proposed. The first step exploits the synthetic phase computed by means of the external DEM and represents a straightforward extension of the procedure that is usually applied in the presence of CRs. Subsequently, in order to refine the achieved solution, a second step is introduced. It is based on a least squares approach that properly exploits the difference between the available low-accuracy DEM and the interferometric DEM generated by means of the phase offset value roughly estimated through the first step. The presented approach is very easy to implement and allows us to achieve an accurate and fast estimate of the needed phase offset, even in the presence of an external DEM affected by a vertical bias and/or a planar shift. The algorithm performances improve in the presence of a large variation of the look angle, as it generally happens in airborne systems. On the other side, the effectiveness of the algorithm may be impaired by the possible presence of artifacts in the unwrapped interferograms, such as those due to the residual motion errors typical of repeat-pass airborne SAR scenarios. Accordingly, the proposed solution is particularly suitable for single-pass interferometric airborne SAR systems, as demonstrated through the presented experimental results achieved on real data.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

We present an innovative region-growing-based technique that permits to improve the surface displacement time-series retrieval capability of the two-scale Small BAseline Subset (SBAS) Differential Interferometric Synthetic Aperture Radar (DInSAR) approach in medium-to-low coherence regions. Starting from a sequence of multitemporal differential SAR interferograms, computed at the full spatial resolution scale, the developed method “propagates” the information on the deformation relevant to a set of high coherent SAR pixels [referred to as source pixels (SPs)], in correspondence to which SBAS-DInSAR deformation measurements have previously been estimated, to their less coherent neighbouring ones. In this framework, a minimum-norm constrained optimization problem, relying on the use of constrained Delaunay triangulations (CDTs), is solved, where the constraints represent the displacement values at the SP locations. Such DInSAR processing scheme, referred to as Constrained-Network Propagation (C-NetP), is easy to implement and, although specifically developed to work within the two-scale SBAS framework, it can be extended to wider DInSAR scenarios. The validity of the method has been investigated by processing a SAR dataset acquired over the city of Rome (Italy) by the Cosmo-SkyMed constellation from July 2010 to October 2012. The achieved results demonstrate that the proposed C-NetP method is capable to significantly increase the spatial density of the SBAS-DInSAR measurements, reaching an improvement of about 250%. Such an improvement allows revealing deformation patterns that are partially or completely hidden, by applying the conventional two-scale SBAS processing. This is particularly relevant in urban areas where the assessment and management of the risk associated to the deformation affecting infrastructures is strategic for decision makers and local authorities.

In this work, we investigate the sea surface monitoring capabilities of a Synthetic Aperture Radar (SAR) system equipped with a three-antenna hybrid Across Track (XT)/Along Track (AT) inteferometric configuration. To do this, we focus on the X-Band airborne InSAeS4 SAR system. Moreover, we propose a simple but effective methodology that allows simultaneous retrieval of the sea surface height and velocity by means of a straightforward, easy-to-implement, linear inversion procedure, which is very general and can be implemented with any system equipped with a three-antenna hybrid XT/AT Interferometric SAR (InSAR) configuration. In our case, we present an experiment carried out in January 2013 in South Italy over the coastline stretch of the Campania region including the Volturno River outlet. In this regard, we highlight that in situ measurements of the retrieved sea surface height and velocity at the time of the airborne mission are unfortunately not available. Notwithstanding, the obtained results show some interesting evidence that the estimated quantities are physically sound. This, on the one side, provides a preliminary validation of the effectiveness of the overall presented methodology and, on the other side, highlights the potentialities of the three-antenna hybrid XT/AT InSAR configuration of the InSAeS4 system for sea state monitoring.

In this work, a new technique for the correction of a digital elevation model (DEM) and the retrieval of the ground mean displacement rate of an investigated area, by the analysis of a sequence of (wrapped) differential SAR interferograms is presented. The method is based on the solution of a non-linear optimization problem that does not require the preliminary execution of any phase unwrapping operation. As a consequence, the achievable estimates are not disturbed by phase unwrapping (PhU) errors. The estimation of the displacement rate may reveal (in most cases) particularly suitable for retrieving geophysical information on the sources responsible for the observed deformations. Moreover, the estimates of the DEM inaccuracies and the ground displacement rate can be used within advanced multi-temporal DInSAR processing chains for the retrieval of surface deformation time-series, with the aim to facilitate phase unwrapping operations and discriminate the desired signals (due to deformation) from residual phase artifacts. The first experimental results, carried out by applying the proposed method to stacks of simulated and real interferograms, demonstrate the validity of the proposed approach, and get a first coarse evaluation of the estimates accuracy.

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is one of the Italian regions most affected by fire events over the summer season. The detection capability of fire scars in such an environment is demonstrated by exploiting information extracted from dual-polarized SAR data. Our results reveal a significant decrease of the VH response over the fire-disturbed forests, thus, highlighting the effectiveness of such cross-polarized observations. In order to prove the validity of the proposed approach for the detection of fire scars in the vegetation layer, the results of the conducted experiments have been suitably compared with burned areas identified by using an existing fuzzy-based algorithm, which has been applied to multispectral Landsat-8 operational land imager data. This investigation opens the way to systematic methods for monitoring fire scars in heterogeneous environments, and in particular in fire-prone Mediterranean ecosystems.

We present a solution for simplifying a recently proposed two-step processing technique that allows to retrieve the constant phase offset present in the unwrapped synthetic aperture radar (SAR) interferograms by exploiting an external, even low-accuracy, digital elevation model (DEM) of the illuminated area and without using corner reflectors. In particular, we show in this letter that the second processing step, namely, the slope-topography-based estimate, can be avoided without impairing the accuracy of the final phase offset estimate. To this aim, we introduce a simple modification to the first step, referred to as phase-based estimate, by considering the vertical bias of the available external DEM as the second unknown parameter in the carried out estimation. The simplified algorithm is very easy to implement and is particularly suitable for airborne SAR interferometry. It has been tested on real airborne SAR data and the obtained results show that the achieved accuracy is the same or better than that achieved through the original two-step approach.

Detection and monitoring of deformations affecting cultural heritage sites and their surroundings represent a key issue for developing strategies for their preservation, particularly in the case of extended archaeological areas. In this chapter, we first introduce the differential SAR interferometry (DInSAR) techniques that, thanks to their capability of performing noninvasive deformation analyses, provide valuable information about the spatial and the temporal evolution of the detected displacements. Subsequently, we present the DInSAR results achieved over the whole archaeological site of the ancient Roman city of Pompeii (Italy), and focus on some historical buildings where small displacements, involving portions or the whole complex structure, have been detected.

In this work we present an advanced interferometric processing chain, which is based on the DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, for the massive processing of SENTINEL-1 (S1) Interferometric Wide Swath (IWS) data. The P-SBAS S1 processing chain produces surface deformation time series, and the relevant mean velocity maps, in automatic and systematic way by efficiently exploiting Cloud Computing infrastructures, thus allowing us to perform DInSAR analyses at very large scale in reduced time frames. As experimental results, the overall mean deformation velocity map relevant to the Central and Southern Italy zone (from Lazio to Sicily), generated by processing in parallel about 300 S1 acquisitions within the Amazon Web Services Cloud Computing platform, is presented. Moreover, the displacement time series of some pixels located in volcanic deforming areas such as the Campi Flegrei Caldera (Napoli Bay area) and the Mt. Etna (Sicily) are shown.

We present an efficient Cloud Computing (CC) implementation of the Parallel Small BAseline Subset (P-SBAS) algorithm, which is an advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for the generation of Earth surface displacement time series through distributed computing infrastructures. The rationale of our approach consists in properly distributing the large data volumes and the processing tasks involved in the P-SBAS chain among the available (virtual and/or physical) computing nodes of the CC infrastructure, so that each one of these elements can concurrently work on data that are physically stored on its own local volume. To do this, both an ad hoc management of the data flow and an appropriate scheduling of the parallel jobs have been also implemented to properly handle the high complexity of the P-SBAS workflow. The proposed solution allows minimizing the overall data transfer and network load, thus improving the P-SBAS efficiency and scalability within the exploited CC environments. The presented P-SBAS implementation has been extensively validated through two experimental analyses, which have been carried out by exploiting the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) resources. The former analysis involves the processing of a large (128 SAR images) COSMO-SkyMed dataset, which has been performed by exploiting up to 64 computing nodes, and is aimed at demonstrating the P-SBAS scalable performances. The latter allows us to show the P-SBAS capability to generate DInSAR results at a regional scale (150 000 km2 in Southern California) in a very short time (about 9 h), by simultaneously processing 18 ENVISAT frames that correspond to a total of 741 SAR images, exploiting in parallel 144 AWS computing nodes. The presented results confirm the effectiveness of the proposed P-SBAS CC solution, which may contribute to further extend the frontiers of the DInSAR investigation at a very large scale.

In recent years, a second generation of Synthetic Aperture Radar (SAR) satellite sensor has been designed and, partially, put into operation, leading to an important breakthrough in Earth Science studies. The common characteristics of such new systems are, indeed, a reduced revisit time (as short as a few days) and, in most cases, an improved spatial resolution (as small as a few meters), providing scientists with unprecedented data for the mapping and monitoring of natural and human-induced hazards. This paper provides an overview on the new observational capability offered by the second generation of SAR sensors, especially in the field of ground deformation analysis for mitigating the risk associated with natural and human-induced hazards. In particular, we exploit the high resolution X-band data acquired by the COSMO-SkyMed (CSK) constellation to show how deformation phenomena characterized by limited spatial extent and extremely fast dynamics can be detected and investigated in details. Whenever possible, we compare the achieved results with those obtained by using data collected by the first generation ERS-1/2 and ENVISAT systems. A comparison with one ALOS satellite dataset is also included. Most of the results, based on the application of Differential SAR Interferometry (DInSAR) techniques, highlight how this technology is not anymore just a sophisticated tool for remotely studying surface deformation phenomena, but it is becoming an operational system for near-real time deformation monitoring. Moreover, we also show how the improved spatial resolution extends the possibility to exploit SAR image amplitude, instead of phase, for direct comparison with optical data and for imaging large deformation episodes, typically associated with strong seismic events, for which DInSAR may fail.

This paper presents the integration of the advanced Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) within the ESA's Grid Processing on Demand (G-POD) environment in the framework of ESA Geohazards Exploitation Platform (GEP). The aim of this activity is to set up a scientific service that allows, in unsupervised manner, the generation of SBAS-DInSAR products, such as surface mean deformation velocity map and the corresponding time series. In particular, such a web tool is aimed at efficiently exploit the huge ESA's SAR data archives (ERS and ENVISAT), giving a support to scientific users, especially those non-expert of SAR data processing, for interferometric analysis in a short time frame.

The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.

We present a simple and effective filtering algorithm to mitigate noise effects in a time-redundant sequence of multi-look small baseline (SB) differential synthetic aperture radar (SAR) interferograms by exploiting the temporal relationships among the selected interferometric data pairs. The proposed method relies on the estimation of the (wrapped) filtered phase terms associated to each SAR acquisition; this result is achieved via a non-linear minimization procedure which is applied to the phase signal of conventional multi-look interferograms without any pixel selection process, and with no a-priori information on the statistics of the involved complex-valued SAR images. Following their estimation, the phase images are paired to reconstruct a new sequence of filtered SB differential interferograms, which are used to generate surface deformation products, such as deformation velocity maps and displacement time-series. The filtering algorithm effectiveness is demonstrated by analysing a set of SAR images...