In this work, the perspective of micro-Doppler analysis from synthetic aperture radar (SAR) images is assessed for large-scale areas. We propose a processing chain that implies a coarse vibrometry estimation based on azimuth sub-aperture decomposition and local Doppler centroid computations, followed by an in-depth analysis based on conventional SAR micro-Doppler algorithms. The methdology is assessed quantitatively using simulated data in keeping with Sentinel-1 IW imaging parameters.

In this work, we investigate the assimilation of InSAR-derived water vapor into the Weather Research and Forecast Data Assimilation model (WRFDA) and evaluate the impact within the InSAR footprint and also over time and space. We ingested three sequential InSAR-derived water vapor maps, estimated from Sentinel-1 images acquired over southern Portugal and Spain during Storm Barbara in October 2020, and assessed the water vapor anomaly fields thousands of kilometers downstream and their impact on rainfall. We show that the assimilation of InSAR-derived water vapor into NWP models has the potential to enhance the understanding of mesoscale meteorological events and improve the model's capability to simulate extreme weather events. This is particularly relevant for flood forecasting and timely weather predictions.

This paper aims to contribute to clarifying the relationship between phase decorrelation and water content in vegetation and to study the impact of the temporal variability of vegetation water content on InSAR estimates of terrain deformation. We combine three interferograms obtained from three SAR images, of the same area acquired at different times, to derive maps of decorrelation phases. It was found that the magnitude and temporal variability of the decorrelation phase is low in bare soil and high in agricultural and forest areas. Furthermore, it was found that the temporal variation of vegetation water content is related to the phase bias of the cumulative displacement computed with short-interval interferograms.

In this work, the perspective synergy of Ground-Based Real Aperture Radar (GBRAR) measurements and micro-Doppler analysis of space-borne Synthetic Aperture Radar (SAR) images for the monitoring of vibration frequencies of large viaduct is discussed. A methodology for the processing GBRAR data and merging of ground-based and space-borne data is described. GBRAR data are interferometrically processed to provide time-range maps of Line-of-Sight (LoS) displacements. First results of GBRAR Ku-band measurements are presented. Vibration frequencies of different structural elements of the viaduct are derived. As a co-product, displacements of the bridge deck due to the crossing of vehicles are also obtained.

A methodology is presented to map the vegetation biodiversity based on the hypothesis of the spectral variation (SV) which has been proposed to assess the forest biodiversity by means of Earth Observation (EO) data. Hyperspectral data acquired by the PRecursore Iperspettrale della Missione Applicativa (PRISMA) mission of the Italian Space Agency to spectral signature with a high spectral resolution. The NDVI is computed from PRISMA data and used to identify pixels corresponding to vegetation cover. The spectral signatures at those pixels are then clusterized using the convolutional autoenconders technique and the final map with the location of pixels belonging to the different classes is produce. The methodology is applied to assess the vegetation biodiversity in National Parks of Gargano, Alta Murgia, Cilento-Vallo di Diano-Alburni, Appennino Lucano Val D’Agri Lagonegrese and Pollino, all located in Southern Italy.

The interferometric synthetic aperture radar (InSAR) technique has demonstrated its ability to capture temporal variations in tropospheric water vapor, providing a valuable source of information for numerical weather prediction (NWP) models. Integrating InSAR data into NWP models has the potential to significantly enhance their forecasting capabilities, especially for predicting local extreme weather events. The challenge lies in extracting a single epoch from the InSAR differential observations. In this work, we introduced a method based on the least-squares approach to estimate single epochs using the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWFs) as a first guess. By leveraging ERA5 data, distinct atmospheric components can be disentangled without additional assumptions or external measurements. Since ERA5 is globally available at 1-h temporal resolution, the proposed method can be applied in remote areas without in situ data, providing improved high-resolution maps at all times (day/night) and in all weather conditions.

InSAR maps of water vapor can improve the accuracy of Numerical Weather Prediction (NWP) models, especially under extreme precipitation events. However, integrating InSAR data into NWP models is challenging due to the differential nature and absolute phase ambiguity introduced by the unwrapping algorithm. To address this, we propose a new approach that uses ERA5 reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the absolute epoch from InSAR data. The proposed method is based on constrained least-squares estimation, and ERA5 data are used as a first guess to untangle distinct atmospheric components without applying additional assumptions or external measurements. This approach will allow us to obtain absolute maps over regions without external in situ measurements (e.g., GNSS or radiosondes). The results are validated using a large GNSS dataset, improving about 29% over the field of the original ERA5.

The availability of C-band Synthetic Aperture Radar (SAR) images by the Sentinel-1 mission every six days boosts the concept of SAR interferometry (InSAR) meteorology. The general idea involves producing integrated water vapor maps using InSAR and assimilating them into high-resolution Numerical Weather Prediction (NWP) models to improve precipitation prediction. The temporal resolution is the disadvantage of the Sentinel-1 mission compared to other techniques (e.g., GNSS). Therefore, this study focuses on the minimum inter-station distance below which a dense GNSS network can catch atmospheric information similar to InSAR maps. The study uses an InSAR water vapor map to generate synthetic GNSS measurements of PWV. Results show that the WRF model's correct prediction of observed rainfall is more frequent when the inter-distance between GNSS stations becomes smaller than 20 km. A numerical instability of the WRF results is observed at shorter distances, indicating the need to handle the spatial correlation of assimilated measurements of PWV.

Results of an experiment aiming to estimate soil moisture (SM) using indexes based on hyperspectral and multispectral data, and decorrelation phase from SAR interferometry are presented. SM estimates are compared with in-situ measurements of SM. The issue of spaceborne and in-situ datasets not overlapping in time is studied. Results are obtained using PRISMA, Sentinel-1 and Sentinel-2 data.

Soil moisture is an important component in investigations of land-surface climate and hydrology. Recently several methodologies and techniques have been proposed, allowing soil moisture retrieving from SAR remote sensing techniques at large scales. In this work we investigate the relation between the closure phases and the time varying soil moisture. We combine three interferograms obtained from three SAR images of the same area acquired at different times, to derive maps of bi -coherence and phase triplet. The results show that there is a linear correlation between the modelled phases derived from soil moisture measurements and the closure phases. The correlation coefficient was R2=0.76 and R2=0.86 for the descending and ascending passes, respectively. However, a scale effect of the closure phases was found when compared with the derived model phases. The scale is about 10% for both passes, meaning that the estimated closure phases underestimate the soil moisture changes.

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the$k$-means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared within situdata collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m3/m3was found over a variety of meteorological conditions.

The observation of soil moisture is fundamental for several climate sciences. Remote sensing had proved that it is possible to observe soil moisture from both Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) observables. This paper shows the use of machine learning regression algorithms to estimate soil moisture change using the InSAR coherence and phase and the soil type. Random Forest Regression and Extra-Tree and Bagging Regression were used. The purpose is to evaluate the improvement gain with the inclusion of “non-conventional” data such as the soil type on the estimation of soil moisture variations in time. The results point out that the inclusion of the soil type improves the estimation with coefficient of determination - R2 up to 72%.

This work presents the results of a simulated analysis concerning algorithms of self-coordination of a swarm of Unmanned Surface Vehicles (USV) for the mitigation of plastic pollution in oceans. The analysis is based on real scenarios provided by the Copernicus Marine Service. The scenario includes the localization of plastics on the sea surface and their movement in time based on the sea surface currents. A swarm intelligence algorithm is used for the decentralized coordination of the USV swarm. Results are presented on a study area located in the northern Tyrrhenian sea between Corsica and the Tuscan coast, in the period of July 2016.

This work presents an application of the Perceptually Important Points (PIP) technique for the analysis of VLF time series. The aim of the analysis is to detect anomalies with respect to the normal variations of the data trends. Such anomalies could reveal possible radio precursors of the earthquake. Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10–50 kHz) and LF (150–300 kHz) radio precursors of earthquakes. The time series used for experiments was collected during the Dodecanese islands earthquakes ($\text{MW}=5.6$and$\text{MW}=5.7$) occurred on January 30, 2020.

This work presents a study on the enhancement of Numerical Weather Prediction (NWP) model simulations when assimilating SAR interferometry (InSAR) Precipitable Waver Vapor (PWV). A 3dVar assimilation scheme is used to assimilate InSAR data. The rotationally invariant spectrum of PWV maps obtained by GNSS, InSAR, and the NWP model is computed before and after the 3dVar assimilation. The average distance of GNSS stations in this region is about 100 km. The spatial resolution of NWP model simulations is 3 km. The spectra analysis shows that there are two cut frequencies, at 10−5m−1÷ 2.10−5m−1and at 2.10−4m−1÷ 3.10−4m−1. The lower and higher cut frequencies seem to be related to the average distance of GNSS stations and the spatial resolution of NWP simulations.

Propagation delay in the atmosphere is the most important artifact that has to be mitigated in the interferometric SAR applications for displacement estimates. In the case of 3D spatial heterogeneous refractivity distribution, propagation delay in the atmosphere known as atmospheric phase screen (APS) is difficult to be corrected without a priori knowledge of displacement location. Especially for ground-based radar interferometric (GRI) measurement in the mountainous area suffers from a significant effect of APS which is difficult to fully be compensated by conventional compensation methods. This paper presents a novel approach to compensate for the APS, especially for the GRI application. In this framework, the time-series clustering approach is proposed to identify the local APS pattern due to the heterogeneity of refractivity. The correlation of each couple of temporal profiles is employed to cluster phase profiles by the rational of the k-means clustering approach. The proposed method further compensates the residual APS by estimating the temporal behavior of each cluster. Synthetic displacements are generated to assess the performances of the method in disentangling terrain displacements and propagation delay in the atmosphere.

We investigate the influence of the perturbed (by a solar X-ray flare) ionospheric D-region on the global navigation satellite systems (GNSS) and synthetic aperture radar (SAR) signals. We calculate a signal delay in the D-region based on the low ionospheric monitoring by very-low-frequency (VLF) radio waves. The results show that the ionospheric delay in the perturbed D-region can be important and, therefore, should be taken into account in modeling the ionospheric influence on the GNSS and SAR signal propagation and in calculations relevant for space geodesy. This conclusion is significant because numerous existing models ignore the impact of this ionospheric part on the GNSS and SAR signals due to its small electron density which is true only in quiet conditions and can result in significant errors in space geodesy during intensive ionospheric disturbances.

In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.

Synthetic Aperture Radar (SAR) interferometry is currently providing displacement measurements over large areas and recently also maps of water vapor in atmosphere. However, the interferometric phase can also capture tiny contributions due to soil moisture. In this work the results obtained in a 1-year experiment with in-situ measurements of soil moisture in agricultural area, in the flat land around the Tagus river, Lisbon, Portugal, are presented. We analyze two interferometric models for the estimation of soil moisture from SAR images. Sentinel-1 SAR images in the C-band are used due to their regular acquisition plan and the short revisiting time. We present examples of soil maps from Sentinel-1 data, characterized by a high spatial resolution and short updating time and comments on their use in precision farming.

In this study, we used the digital television signal broadcasted by geostationary satellite for passive bistatic synthetic aperture radar (SAR) imaging and interferometry applications. A prototype system was designed and fabricated with commercial-off-the-shelf (COTS) components. Specifically, three COTS low noise block downconverters (LNBs) were synchronized for a longtime coherent measurement. Multiple TV channels were combined to achieve a wider bandwidth to improve the range resolution. The potential applications of this ground-based radar system are SAR imaging, displacement estimation, and digital elevation model generation. Hardware design and data processing algorithms are described. Experimental results are presented to validate the performance of the designed system and the proposed methods.

In this study, a ground-based radar system with two multiple input multiple output (MIMO) arrays is developed for 2D displacement estimation applications. To reduce the influences of antenna position error, channel delay, and phase/gain difference on the imaging quality, calibration methods and phase coherence factor (PCF) based filtering methods are applied. For each MIMO array, target displacement can be measured along its line of sight direction. Co-registration is conducted to help to derive the 2D displacement vector estimations of the targets.

In this work, we present a methodology to estimate 3D moisture structures in atmosphere based on the use of SAR interferometry (InSAR), and a Numerical Weather Prediction model (NWP). Maps of propagation delay in atmosphere with sub-kilometer spatial resolution are obtained by assimilating high resolution Precipitable Water Vapor (PWV) maps into the Weather Research and Forecast (WRF) model using the WRF Data Assimilation (WRFDA) System. The PWV maps are obtained by interferometrically processing of Sentinel-1 images. The comparison of WRF modeling of atmosphere parameters, before and after the assimilation is used to study the response of the model to the InSAR information and assess the physical consistency of atmosphere parameters modeled by the system. The visualization of moisture structure in atmosphere is carried out in terms of the 3D spatial distribution of atmosphere refractivity, hydrometeors, thermodynamics quantities and wind patterns. The proposed procedure is applied to the visualization of Atmospheric Gravity Waves (AGW) and Convective Systems (CS), two examples of moisture structures in atmosphere.

We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.

To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.

A Global Navigational Satellite System (GNSS) tomography experiment has been performed for 1 week, introducing Atmospheric Infrared Sounder (AIRS) remote sensing data to initiate and update a 3D wet refractivity hourly solution series of the troposphere. Some qualitative and quantitate studies have been performed, taking advantage of a local radiosonde campaign with a 4-hour sampling data. 3D wet refractivity maps with an accuracy close to 2 g/m3are obtained.

In this paper we summarize the results of an experiment aiming to compare soil moisture estimates obtained by Sentinel-l interferometric data with in-situ measurements. The study area, located close to Lisbon in Companhia das Lezirias, Portugal is characterized by a flat topography, large agricultural areas and sparse vegetation. In a test site, four soil moisture sensors were deployed and soil moisture was measured (at a depth of 5 cm) for a period of 7 months in an hourly basis. For the same interval of time and with a temporal resolution of 6 days C-band Sentinel-l SAR images were interferometrically processed and coherence, phase and phase triplet images were derived. The in-situ soil moisture measurements have been used to predict the analytical interferometric phases, coherences and phase triplets and compared with the measured interferometric phases in both VV and VH polarimetric channels. As a further analysis, a regression analysis of in-situ soil moisture measurement and Sentinel-l backscattering images has been carried out.

We study the imaging and interferometric performances of a MIMO radar on board of an airship as alternative to airborne and spaceborne SAR remote sensing techniques. Four different MIMO radar arrays are designed working in L, C, X and Ku frequency bands. A frequency bandwidth of 250 MHz has been considered for the MIMO radars. The spatial resolution is 0.6 m in range and 0.3 degree in azimuth. The imaging and interferometric performances of the MIMO radar are analyzed in terms of the airship stability. A synthetic raw data set is generated assuming a target deployed on a flat area at different azimuth angle. This MIMO imaging solution is intended for continuous imaging over an area of interest.

In this work we present a methodology to estimate the 3D distribution of water vapor in atmosphere based on the use of SAR interferometry (InSAR) and Sentinel-l data. Maps of propagation delay in atmosphere are assimilated in a high resolution Numerical Weather Model to enhance the forecast of atmosphere parameters. These are used to compute the atmosphere refractivity. Furthermore, 3D maps of hydrometers in atmosphere are derived after the assimilation of InSAR data. Both refractivity and hydrometeors maps are used to map 3D Water vapor patterns in atmosphere. Examples of InSAR signatures of water vapor in atmosphere are shown. We show how the 3D maps liquid refractivity and hydrometeors can be a useful tool to map moisture in atmosphere in case of convective phenomena in atmosphere.

In this work, we study the problem of assimilating high resolution Precipitable Water Vapor (PWV) maps using the Weather Research and Forecast 3D Variational Data assimilation system (WRF-3DVar). The PWV maps are obtained using the Sentinel-1 Synthetic Aperture Radar (SAR) images and the SAR interferometry (InSAR) technique. The influence of the high resolution PWV data on the initial condition of WRF and during the next 12 hours is studied. We demonstrate that the assimilation of InSAR PWV maps increases both the water vapor concentration and temperature over areas affected by extreme weather events so correctly generating localized convection cells. The PWV forecast, after the assimilation of InSAR maps, are compared with the PWV estimates provided by a dense GNSS network. The precipitation pattern and amount are compared to meteorological radar measurements. The case study of the extreme weather event that affected the city of Adra, Spain, on 6thSeptember 2015, is used to demonstrate how the assimilation of high resolution PWV maps.

Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density.

A new methodology for the mapping of intertidal terrain morphology is presented. It is based on the use of synthetic aperture radar (SAR) images and the temporal correlation between the SAR backscatter intensity and the water level on the intertidal zone. The proposed methodology does not require manual editing, providing a set of geolocated pixels that can be used to generate a digital elevation model of the intertidal zone. The methodology is validated using TerraSAR-X SAR images acquired over Tagus estuary. This methodology can be useful for the regular updating of intertidal bathymetric models useful for both flood hazard mitigation and morphodynamics modeling.

This paper presents a methodology to generate maps of atmosphere's precipitable water vapor (PWV) over large areas with a length of hundreds of kilometers and a width of about 250 km, based on the use of interferometric Sentinel-1A/B C-band synthetic aperture radar (SAR) data with a high spatial resolution of 5 × 20 m2and the revisiting time of six days. An algorithm to calibrate and merge PWV maps from different swaths of Sentinel-1 acquired along the same track, using global navigation satellite system (GNSS) measurements, is described. The proposed methodology is tested on Sentinel-1A SAR images acquired over the Iberian Peninsula, along both descending and ascending tracks. The assessment with an independent set of GNSS measurements shows a mean difference of a fraction of millimeter and a dispersion lower than 2 mm. Both the use of Sentinel-1A/B SAR images and the proposed methodology open new perspectives on the application of SAR meteorology for the high-resolution mapping of PWV over large region-spanning areas and the assimilation of interferometric SAR data into numerical weather models.

The integration of interferometric synthetic aperture radar (InSAR) and GPS tomography techniques for the estimation of the 3-D distribution of atmosphere refractivity is discussed. A methodology to use the maps of the temporal changes of precipitable water vapor (PWV) provided by InSAR as a further constraint in the GPS tomography is described. The aim of the methodology is to increase the accuracy of the GPS tomography reconstruction of the atmosphere's refractivity. The results, which are obtained with SAR and GPS data acquired over the Lisbon area, Portugal, are presented and assessed. It has been found that the reconstruction of the atmospheric refractivity is closer to the real atmospheric state with a mitigation of the smoothing effects due to the usual geometrical constraints of the GPS tomography.

This paper studies the problem of the assimilation of precipitable water vapor (PWV), estimated by synthetic aperture radar interferometry, using the Weather Research and Forecast Data Assimilation model 3-D variational data assimilation system. The experiment is designed to assess the impact of the PWV assimilation on the hydrometers and the rainfall predictions during 12 h after the assimilation time. A methodology to obtain calibrated maps of PWV and estimated their precision is also presented. The forecasts are compared with GPS estimates of PWV and with rainfall observations from a meteorological radar. Results show that after data assimilation, there is a correction of the bias in the PWV prediction and an improvement in the prediction of the weak to moderate rainfall up to 9 h after the assimilation time.

GPS tomography provides a unique opportunity to sense the 3D state of the atmosphere. However, the setting of the model grid can affect the water vapor solution since GPS data can be insufficient to cover all the domain, leading to an ill-posed conditioning, usually solved by constraints. In this work we present the result of a simulation analysis to study the impact of Galileo data on the reconstruction of 3D atmospheric water vapor. GPS tomography results are compared with those provided by merging GPS and Galileo data in order to ascertain the enhancement of the 3D water vapor refractivity reconstruction.

Microwave sensing of the atmosphere with GPS data, particularly using GPS tomography, provides a unique opportunity to measure the 3D state of the atmospheric water vapor, since it acquires data in a high temporal sampling. However, this technique requires a full domain coverage which is not fulfilled by GPS data, leading to an ill-posed conditioning only solved by constraints. SAR interferometry can be used to get integrated water vapor maps with high spatial resolution, but with a temporal frequency depending on the SAR acquisitions. In this work we describe a methodology to include integrated water vapor maps provided by SAR interferometry into the GPS tomography processing scheme. The differential water vapor spatial distribution occurred between the acquisition time of master and slave images is used to constrain the GPS tomography solution. Results are obtained over Lisbon area using GPS and ENVISAT-ASAR data and validated with regional radiosonde data.

In this work we study the problem of focusing Synthetic Aperture Radar (SAR) images by means of Compressive Sensing (CS). The presented technique is introduced as an alternative to the traditional focusing methods, suggesting new modes for data acquisition in a more efficient configurations or on-board processing in case of spaceborne sensors. In this paper the method is tested on both simulated and real images acquired by a Ground Based Synthetic Aperture Radar (GB-SAR), for which images of reduced size can be generated with no difficulty. Results of comparison of CS processing with an exact focusing algorithm are shown in terms of root mean square error of amplitude and phase as a function of the number of focused targets and undersampling of the acquisition lines. Coherence of a CS-processed couple of images is also evaluated. The purpose of the paper is to show the potential of CS applied to SAR systems, regardless (at the moment) of the efficiency in computational load. In particular, we show that the image can be reconstructed without loss of resolution after dropping a fair percentage of the received pulses.

Image mining consists of the procedures that allow to access, search and explore very large databases of data. Institutions like spatial agencies have to manage huge archives of Earth Observation (EO) images and need solutions to make data available to users from both the algorithmic and the infrastructural point of views. On the other side, users would need to explore the variety of images not just based on metadata, like time of acquisition or sensor parameters, but also by getting knowledge of their content. In this contribution, we investigate methodologies for content-based EO image retrieval via example-based queries. In particular, we present a procedure for the indexing of large-scale unstructured archives, built on top of a cluster analytics framework, Apache Spark. The procedure is based on a hierarchical and scalable implementation of a space partitioning algorithm and allows O(log n) response query times. Scalability analyses are conducted on polarimetric data from NASA/JPL archives, by using virtualized computing resources distributed over the Internet. In particular, the effects of the cluster size and of the hardware scale-up are demonstrated. The results also reveal the applicative potential of using on-demand cloud-based resources.

In this study we compare the results obtained using three different atmospheric datasets for the mitigation of atmospheric effects in TerraSAR-X imagery. The used datasets are: ERA-interim re-analysis model, MODIS sensor precipitation water vapour data and GPS derived precipitable water vapour (PWV). The PWV maps were converted to atmospheric path delay and projected into the SAR interferograms geometry. Subsequently the PWV contribution was removed from the interferograms. The Persistent Scatterers technique was applied to the atmospherically corrected interferograms and the obtained displacement rate compared with GPS surface displacement. It was observed that the mitigation of atmospheric effects influences the estimated displacement rate.

In this paper, the uncertainty of atmospheric delay maps obtained by a numerical weather model (NWM) is assessed. These models can simulate 3-D fields of meteorological parameters at coarse scales. A few approaches, which are mainly based on zenith wet delay estimates derived by NWM data, have been recently developed to mitigate atmospheric propagation artifacts in synthetic aperture radar interferometry applications. However, an assessment of these estimates is still missing. We present a methodology based on radiosonde data to assess the uncertainty of the meteorological parameters derived from an NWM and the dry and wet delays calculated using these parameters. The methodology is applied to the data generated by the Weather Research and Forecasting model that currently is the model with the higher accuracy relative to its predecessors.

Recently, synthetic aperture radar interferometry (InSAR) has been recognized as a promising tool to generate high-resolution maps of atmospherical precipitable water vapor temporal changes (ΔPWV) from the propagation delay of radar signal in atmosphere. The relationship between ΔPWV and propagation delay mainly depends on the vertical profiles of temperature and water vapor pressure. In this letter, we present a methodology to study the spatial and temporal variations of the temperature's vertical profile and generate more accurate high-resolution ΔPWV maps by means of InSAR.

Ground-Based Synthetic Aperture Radar (GB-SAR) is emerging as a relatively new system for specific short ranges and frequent monitoring applications. The main GB-SAR focusing algorithms proposed in literature are compared in terms of focusing quality and ability to manage the approximation of the space-varying two-dimensional kernel. Residual migration and phase errors which have implications on the target impulse response deformation, are given both in analytic and numerical forms, using simulations and real data acquired with a Ku-band system.

This work presents the result of an experiment aiming to derive high resolution Digital Elevation Models (DEMs) of areas spanning at most few squared kilometers by means of Ground-Based Synhetic Aperture Radar (GBSAR) interferometry. Spatially dense maps of topographic height are obtaned by exploiting pixels of SAR interferograms characterized by high values of coherence and applying kriging interpolation to generate regular-grid maps.

In this paper, we present the results of an experiment aiming to compare measurements of atmospheric delay by synthetic aperture radar (SAR) interferometry and GPS techniques to estimates by numerical weather prediction. Maps of the differential atmospheric delay are generated by processing a set of interferometric SAR images acquired by the ENVISAT-ASAR mission over the Lisbon region from April to November 2009. GPS measurements of the wet zenith delay are carried out over the same area, covering the time interval between the first and the last SAR acquisition. The Weather Research and Forecasting (WRF) model is used to model the atmospheric delay over the study area at about the same time of SAR acquisitions. The analysis of results gives hints to devise mitigation approaches of atmospheric artifacts in SAR interferometry applications.

In this work we investigate the use of Synthetic Aperture Radar (SAR) images acquired by the TerraSAR-X satellite operating a very high resolution X-band SAR sensor to generate maps of temporal and spatial variations of the water vapour distribution with a horizontal resolution of 27×27 m over the Lisbon region, Portugal. We present the result of a time series of delay maps obtained by processing a set of interferometric SAR images acquired by the TerraSAR-X mission. The maps are calibrated by means of GPS estimations of the delay over the same area and covering the time interval between the first and the last SAR acquisition. The availability of maps with a high spatial resolution could increase the quality of quantitative precipitation forecasting and open interesting perspectives for nowcasting applications.

Tidal flats are considered an invaluable natural resource. Generally, they show dynamic morphologic changes that arise from high tidal energy and sediment transportation. The study of sediment budget processes is important in many ecological systems. The sediment budget can be estimated only if maps of morphological changes are available. Remote sensing, combined with in situ surveying, is an effective tool for monitoring tidal flats. The aim of this work is to precisely map tidal flats changes using multi-temporal Synthetic Aperture Radar (SAR) images acquired by the TerraSAR-X sensor. For that purpose, is proposed the application of K-Means clustering (in a gray level mode) to both original and preprocessed SAR amplitude data sets, followed by a morphological sequence task to generate optimized datasets, which will be compared for change detection between tides. Results will show the segmentation of intertidal regions from flooding tide's comparison.

A method to derive accurate spatially dense maps of 3-D terrain displacement velocity is presented. It is based on the merging of terrain displacement velocities estimated by time series of interferometric synthetic aperture radar (InSAR) data acquired along ascending and descending orbits and repeated GPS measurements. The method uses selected persistent scatterers (PSs) and GPS measurements of the horizontal velocity. An important step of the proposed method is the mitigation of the impact of atmospheric phase delay in InSAR data. It is shown that accurate vertical velocities at PS locations can be retrieved if smooth horizontal velocity variations can be assumed. Furthermore, the mitigation of atmospheric effects reduces the spatial dispersion of vertical velocity estimates resulting in a more spatially regular 3-D velocity map. The proposed methodology is applied to the case study of Azores islands characterized by important tectonic phenomena.

A method that is used to generate synthetic interferograms of the atmospheric phase delay temporal changes is presented. The Weather Research and Forecasting Model is used to forecast the spatial distribution of the main atmospheric parameters at the acquisition times of synthetic aperture radar (SAR) images. The method is applied to mitigate atmospheric artifacts in SAR interferograms. The Lisbon Region and the Pico and Faial Islands in the Azores archipelago are chosen as case studies. They are characterized by a different temporal behavior of atmospheric phase delay properties. Results are assessed by means of a statistical analysis.

In this work we analyze the properties of integration time and azimuth coverage of bistatic SAR configurations. The kinematics of antennas is modelled as a motion at a constant velocity along a linear path. Orbits can be at different heights above the ground and have crossing projections on a horizontal plane. Footprints on the reference ground plane move at different velocities. Performances of bistatic SAR configurations are compared to those of monostatic ones.

Bistatic synthetic aperture radar (SAR) is an extension of traditional monostatic SAR, which increases the flexibility in designing SAR missions. We describe a scheme for the computation of integration time and azimuth coverage of bistatic SARs based on space-time diagrams. A classification of bistatic SAR configurations is introduced in terms of size and velocity on the ground of antenna footprints. Bistatic SAR regimes are also identified.

A novel technique for the retrieval of a digital terrain model (DTM) with a ground-based synthetic aperture radar (GB-SAR) system is described. A set of SAR images collected over a test site located at Salice d'Ulzio, Italy, is used to validate experimentally the proposed DTM retrieval technique. The topographic height is obtained using an algorithm specifically tailored for a GB-SAR system equipped with two receive antennas. The comparison with an existing DTM of the test site area shows that the retrieved DTM is accurate, with root mean square height differences between the two DTMs below 5 m.

In this paper we present some preliminary results of the application on real data of a statistical method to solve the height estimation problem in Interferometric Synthetic Aperture Radar (InSAR). The method is based on maximum a posteriori (MAP) estimation and Markov Random Fields (MRF) image modeling, and makes use of multifrequency/baseline SAR raw data. The real data set is acquired by a Ground-Based SAR (GB-SAR) interferometer based on the LiSA technology

This short communication studies the problem of terrain mapping by means of the ground-based synthetic aperture radar (GB-SAR) interferometry technique. A phase-to-height relationship tailored for the GB-SAR interferometric configuration is introduced and verified by a simulated analysis. A sensitivity study is carried out aiming to optimize the use of a GB-SAR system for the terrain mapping and to derive the precision of this technique.

This paper concerns the use of Ground-Based Synthetic Aperture Radar (GB-SAR) interferometer for the measurement of terrain movements and structural deformations in man-made structures. This instrument can measure displacements with a precision up to a fraction of millimetre. The spatial extension of the monitored area can be of a few squared kilometres. With respect to other traditional geotechnical instruments, the presented GB-SAR interferometer can remotely monitor the target scene, providing deformation maps and not only point-like measurements.

A procedure to derive a digital elevation model (DEM) over a region spanning a few square kilometres by means of a ground-based interferometric synthetic aperture radar (GB-InSAR) is described. The results obtained at a test-site near the village of Sauze d'Oulx, Italy, are presented. The comparison with an existing DEM shows the good accuracy of the proposed DEM retrieval technique.

This work concerns the application of ground-based synthetic aperture radar (GB-SAR) interferometry as a tool for the landslide monitoring during emergencies. An example of application of this technique to the Cortenova landslide, Italian Alps, is described. The deformation maps provided by GB-SAR interferometry allows for the study of the landslide temporal evolution and the volume estimation of the material involved in the mass movement. This information is important for the assessment of future risk scenarios needed to draw up the emergency plans for civil protection purposes.

Interferometric SAR data are used to derive morphometric terrain attributes required by geomorphological and hydrogeological applications. Instead of generating an interferometric Digital Terrain Model (DTM) and subsequently derive the terrain attributes, we compute these quantities directly from the interferogram. Experiments demonstrate that, when compared with traditional DTM-based measures, SAR based attributes seem more stable and offer best performances. Results shown in this paper have been obtained by processing ERS-1/2 SAR data referring to a typical Alpine mountain basin.

This work concerns the application of space-borne SAR interferometry (InSAR) to the study of the land subsidence problem affecting the urban areas of the Firenze-Prato-Pistoia basin (Central Italy). Such a phenomenon is mainly related to the large amount of ground-water pumped for the industrial activities of this area. The interferometric analysis has been carried out by means of both traditional Differential Interferometry (DInSAR) and Permanent Scatterers technique (PS) by using SAR data acquired by the ESA ERS1/2 satellites. Different patterns of terrain subsidence have been detected in the industrial areas of the monitored basin.

A ground-based synthetic aperture radar (GB-SAR) interferometer is used to retrieve the velocity field of a landslide. High-resolution images are obtained by means of a time domain SAR processor. An in-depth analysis of the sequence of SAR interferograms enables the recognition of a slowly deforming upper scarp in the scene, and a debris flow that feeds the accumulation zone of the landslide, where a fast change in terrain morphology is observed. The estimated deformation map is in agreement with the available measurements obtained by means of Global Positioning System receivers. Results show that GB-SAR interferometry is a cost-effective solution for the monitoring of landslides. The proposed method is shown to be a valid complement to space- and airborne SAR and to the traditional geodetic instruments.

This work concerns the application of groundbased SAR interferometry (GB-InSAR) for the monitoring of the Tessina landslide, using the LISA (Linear SAR) system developed at the JRC. This landslide is continuously monitored by a topographic system. For this reason it has been chosen as a test site to fully confirm the accuracy of the, GB-InSAR. The GB-InSAR-derived ground displacements have been compared with those obtained by a set of topographic benchmarks placed in the unstable areas.

We investigate the potentialities of differential SAR interferometry (DInSAR) to estimate the morphological changes of the channel bed induced by processes of erosion and deposition. The Cellina stream (North-East Italy) has been chosen as a test site. The stream has a braided channel morphology with a gravel bed. A set of ERS-1/2 SAR images acquired in this area has been interferometrically processed. The DInSAR analysis enabled areas of erosion and sedimentation to be mapped.

Exact closed-form algorithms for InSAR-derived digital elevation model (DEM) geolocation are described. They are based on the knowledge of orbit parameters and time/Doppler frequency coordinates of each SAR image, as well as of their interferometric phase. The proposed scheme gives a simple framework for deriving the geolocation accuracy.