The GEORES project is funded by the Italian Space Agency (ASI) and aims to develop a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, based on the synergistic use of the most advanced Earth Observation (EO) technologies, Artificial Intelligence (AI) and eXplainable AI (XAI). GEORES is organized into four main modules to support management of the main risks associated with land degradation: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data, calculation models and algorithms are integrated to identify "hot-spots" of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators ("geo-analytics") calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and Gargano Promontory, Apulia Region, southern Italy, and foresees the engagement of final users (i.e. Regional Civil Protection and Municipality of Bari).

Bayesian estimation of posterior probabilities for the presence of floodwaters, coupled with accurate time series regression methods, show good performance in the monitoring of inundations at high temporal and spatial resolution from long stacks of synthetic aperture radar (SAR) data. We report results on the integration of SAR intensity and cascaded InSAR coherence time series in different polarization channels within a Bayesian framework. The method is being tested over sites in both northern and southern Italy, with X- and C-band SAR data. The results indicate some advantage in using more than one independent channel in the Bayesian inference for some types of land cover, in terms of area under the curve (AUC) when compared to independent flood maps acquired over known events. Stacks of surface water confidence levels computed over both test sites show promising characteristics, both on agricultural and coastal areas.

Multi-temporal remotely sensed data are a precious source of information for high spatial and temporal resolution flood mapping. We present a methodology for flood mapping through processing of long time series of Sentinel-l SAR data, as well as ancillary information. A Bayesian framework is adopted to derive probabilistic maps of the presence of flood waters, through modeling of backscatter time series, based on the as-sumption that floods represent impulsive temporal anomalies. We illustrate some results on a time series of Sentinel-l data acquired from 2015 to 2021 over a test area on the Basento river watershed, Basilicata Region, in Southern Italy, recurrently subject to floods.

We observe relatively high InSAR mean coherence levels over badlands, i.e. clayey bare soil areas, on a test site in the Basilicata region, in southern Italy. Time series of InSAR coherences on cascaded short-baseline image pairs, obtained from stacks of Sentinel-1 SAR images, exhibit oscillating behaviour, with significant correlation with cumulated rainfall levels on bad-land areas, while on other areas with crops or spontaneous vegetation the correlation is lower, and a seasonal trend is instead statistically significant. These observations seem to point to the possibility of investigating erosion phenomena over badland areas through InSAR time series, which involves a significant step forward, in terms of spatial and temporal resolution, with respect to traditional measurements which require repeated topographic surveys at long intervals, or sparse in-field point measurements.

The Earth Observation is proven to be extremely useful in the management of the aftermaths of tsunami and earthquakes. ESA-Disaster Risk Reduction (DRR) is a project within the Earth Observation for Sustainable Development (EO4SD) program aiming to demonstrate on large scale in developing countries the usefulness of Earth Observation to reduce disaster risks and to support the management of the post-event rehabilitation and reconstructions. Among the activities of the project, the ESA-DRR team, following the dramatic event of the Sulawesi earthquake (28 September 2018) supported the Indonesian authorities, through the cooperation with the Asian Development Bank (ADB), the reconstruction and rehabilitation of Palu area. The provided EO services are presented.

Indonesia is periodically affected by severe volcanic eruptions and earthquakes, which are geologically coupled to the convergence of the Australian tectonic plate beneath the Sunda Plate. Multi-temporal SAR interferometry (MTI) can be used to support studying and modelling of terrain movements. This work is aimed at performing an analysis of ground displacements over Indonesian sites through MTI techniques. Two test sites in Sumatra and Java have been selected according to the availability of archived SAR data, GNSS networks, and geological data. Both COSMO-SkyMed (CSK) and Senitnel-1 data-sets have been processed through MTI algorithms. The derived displacement maps have been interpreted according to the available geological and geophysical information.

The Rheticus® cloud-based platform provides continuous monitoring services of the Earth's surface. One of the services provided by Rheticus® is the Displacement Geoinformation Service, which offers monthly monitoring of millimetric displacements of the ground surface, landslide areas, the stability of infrastructures, and subsidence due to groundwater withdrawal/entry or from the excavation of mines and tunnels. To provide this information, the Rheticus® platform processes a large amount of Geospatial Big Data. In particular, Rheticus® is capable to process Synthetic Aperture Radar images acquired by the X-band COSMO-SkyMed constellation, as well as satellite Open Data provided by Copernicus Sentinels, and it is capable to integrate local INSPIRE data sources. In this paper, we summarize the main features of the Rheticus® services and we provide examples of the detection and monitoring of geohazard and infrastructure instabilities through Multitemporal InSAR techniques. Furthermore, we outline the porting activity and the efficient implementation of the most time-consuming algorithmic kernels in the GPGPU environment.

Satellite remote sensing radar technologies provide powerful tools for geohazard monitoring and risk management at synoptic scale. In particular, advanced Multi-Temporal SAR Interferometric algorithms are capable to detect ground deformations and structural instabilities with millimetric precision, but impose strong requirements in terms of hardware re-sources. Recent advances in GPU computing and programming hold promise for time efficient implementation of imaging algorithms, thus enhancing the development of advanced Emergency Management Services based on Earth Observation technologies. In this study, a preliminary assessment of the potentials of GPU processing is carried out, by comparing CPU (single- and multi-thread) and GPU implementations of InSAR time-consuming algorithm kernels. In particular, it is focused on the fine coregistration of SAR interferometric pairs, a crucial step in the interferogram generation process. Experimental results are discussed.

Thanks to the technological maturity as well as to the wide availability of SAR data, Multi-temporal SAR Interferometry (MTInSAR) can be used to support systems devoted to environmental monitoring and risk management. In particular, high resolution X-band MTInSAR applications are also suitable for monitoring single man-made structures (buildings, bridges, railways and highways). The paper presents examples concerning the application of MTInSAR techniques and COSMO-SkyMed constellation for instability monitoring of infrastructures and, in particular, harbor docks and railways.

We apply a Bayesian Network (BN) paradigm to the problem of monitoring flood events through synthetic aperture radar (SAR) and interferometric SAR (InSAR) data. BNs are well-founded statistical tools which help formalizing the information coming from heterogeneous sources, such as remotely sensed images, LiDAR data, and topography. The approach is tested on the fluvial floodplains of the Basilicata region (southern Italy), which have been subject to recurrent flooding events in the last years. Results show maps efficiently representing the different scattering/coherence classes with high accuracy, and also allowing separating the multitemporal dimension of the data, where available. The BN approach proves thus helpful to gain insight into the complex phenomena related to floods, possibly also with respect to comparisons with modeling data.

The Multi-Chromatic Analysis (MCA) uses interferometric pairs of SAR images processed at range sub-bands and explores the phase trend of each pixel as a function of the different central carrier frequencies. The MCA technique introduces the concept of targets exhibiting stable radar returns across the frequency domain (PSfd). In this work we compare this stability along frequencies with the temporal stability which is at the base of persistent scatterers interferometry (PSI) techniques. Different populations of PSfdand “temporal” PS were derived by using COSMO-SkyMed SAR data. An ad hoc processing scheme was developed to derive PSI products by processing the same range sub-bandwidth used by the MCA in order to guarantee the same scattering conditions. The populations of PSfdand “temporal” PS were compared and preliminary considerations provided concerning the scattering properties of the targets selected by the two criteria.

The Multi-Chromatic Analysis can be applied to interferometric pairs of SAR images processed at range sub-bands, and consists of exploring the phase trend of each pixel as a function of the different central carrier frequencies. The phase of stable scatterers evolves linearly with the sub-band central wavelength, with a slope proportional to the absolute e.m. path difference. The technique appears optimally suited for the new generation of satellite sensors, which operate with larger bandwidths than previously available instruments, generally limited to few tens of MHz. A first experiment on satellite data was carried out by processing a spotlight interferometric pair of images acquired by TerraSAR-X on the well-known Uluru monolith in Australia. In the present work, we illustrate MCA processing on SAR data acquired over the same site by the COSMO-SkyMed constellation. The topographic profile of the monolith is successfully reconstructed. Furthermore, the results are also compared with those previously derived by processing TerraSAR-X data.

We present a research project, funded by the Italian Space Agency (ASI), aimed at performing 2D and 3D Focusing of COSMO/SkyMed (CSK) SAR Data. We describe the main objectives of the project, briefly illustrate employed techniques, and finally present the obtained results. The latter show that sub-meter resolution can be achieved in the enhanced spotlight CSK acquisition mode, and that by using 3D focusing it is possible to resolve scatterers at different slant heights within the same range-azimuth resolution cell, even in areas characterized by severe height discontinuities and large thermal dilations effects.

The present study is aimed at investigating the potentialities of the COSMO/SkyMed (CSK) constellation for ground elevation measurement with particular attention devoted to the impact of the improved spatial resolution wrt the previous SAR sensors. Assuming no movement and successful orbital error removal, the main problem in height computation through InSAR techniques derives from the interferometric phase artifacts related to the interaction between microwave and the lower layers of the atmosphere (APS, Atmospheric Phase Screen). Different strategies can be adopted to filter out this signal, ranging from the exploitation of the well-known spatial and temporal statistics of the APS to the estimation of independent APS measurements through Numerical Weather Prediction (NWP) models. Their feasibility and the achievable accuracies are discussed here.

COSMO/SKYMED is currently the unique constellation of synthetic aperture radar (SAR) sensors operative, which is also for civilian use. On April 6, 2009, an Mw 6.3 earthquake struck the city of l'Aquila in Central Italy. The constellation acquired data stacks over the hit area at an unprecedented temporal rate. In this letter, the results obtained by processing several data set via two independent multitemporal differential interferometric SAR techniques are presented to demonstrate the capability of this constellation in postseismic deformations monitoring.

Image alignment is a crucial step in synthetic aperture radar (SAR) interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than a few tenths of a resolution cell to avoid significant loss of phase coherence. In conventional interferometric precise coregistration methods for full-resolution SAR data, a 2-D polynomial of low degree is usually chosen as warp function, and the polynomial parameters are estimated through least squares fit from the shifts measured on image windows. In case of rough topography or long baselines, the polynomial approximation may become inaccurate, leading to local misregistrations. These effects increase with spatial resolution of the sensor. An improved elevation-assisted image-coregistration procedure can be adopted to provide better prediction of the offset vectors. This approach computes pixel by pixel the correspondence between master and slave acquisitions by using the orbital data and a reference digital elevation model (DEM). This paper aims to assess the performance of this procedure w.r.t. the “standard” one based on polynomial approximation. Analytical relationships and simulations are used to evaluate the improvement of the DEM-assisted procedure w.r.t. the polynomial approximation as well as the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different resolutions and baselines. The two approaches are then evaluated experimentally by processing high-resolution SAR data provided by the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO/SkyMed) and TerraSAR-X missions, acquired over mountainous areas in Italy and Tanzania, respectively. Residual-range pixel offsets and interferometric coherence are used as quality figure.

On April 6th2009 a Mw=6.3 earthquake struck the area around the city of L'Aquila in Italy. SAR systems have been proven to be valuable sensors for analyzing the effect of earthquakes and monitoring post-seismic displacements. Due to the low deformation rate, the study of post-seismic events requires the use of a multi-temporal InSAR approach. COSMO/SKYMED is a constellation of SAR sensors of 4 X-band sensors operative also for the civilian use. Thanks to the availability of a stack of ascending acquisitions, ad hoc programmed by ASI on the area stricken by the earthquake, it was possible to provide post-seismic deformation maps by using two different multi-temporal interferometric approaches: the SPINUA and SBAS techniques. The work is carried out in the framework of the MORFEO project dedicated to the monitoring of the landslides risk by means of Earth Observation data. The displacement maps related to the post-seismic activity are presented and commented. The results clearly show the potentiality of the COSMO/SKYMED constellation use for emergency monitoring.

This work is focused on the phase validation of interferograms obtained by combining COSMO-SkyMed SAR images acquired by a single satellite (temporal baseline coincident with the orbital repeat cycle) or even by two satellites of the SAR constellation in equi-phased configuration on the orbital plane (temporal baseline: 8 days), thus minimizing the temporal decorrelation. Both qualitative and quantitative analyses have been therefore carried out for HI (HIMAGE: stripmap, single polarization) and S2 (enhanced spotlight) imaging modes, in order to proof whether or not COSMO-SkyMed constellation is well suited for SAR interferometry.

The detectability of the deformation pattern produced by the June 15, 1995 Aigion earthquake with DInSAR techniques is ensured by its magnitude (Mw=6.3), shallow depth and dip-slip mechanism. In this paper, stacking procedures are applied to a series of ERS interferograms in order to filter out from the differential phase field the atmospheric signal, and an a posteriori test is used to check the statistical properties of the atmospheric signal both in time and space. Based on the DlnSAR-derived deformation pattern, a new fault model is proposed that takes into account the crustal layering of the western part of the Gulf of Corinth.