This study analyses the C-band multi-polarization backscattering properties of offshore wind farms. The data set includes a large time series of satellite synthetic aperture radar imagery collected by the European Space Agency Copernicus mission Sentinel-1. As a test site, the Robin Rigg offshore wind farm located in the Solway Firth, (UK) is selected. The aim of this study is to extract the multi-polarisation normalized radar cross section and reflection symmetry information over selected wind turbines and a reference target-free sea surface area to analyze the corresponding backscattering features over time. Preliminary results suggest that the detectability of wind turbines is strictly related to the considered multipolarization parameters, for which co-polarized backscattering dominates over the cross-polarized one. In addition, wind turbines are characterized by a correlation between co- and cross-polarized channels larger than the surrounding sea surface, which satisfies the reflection symmetry property.

World Heritage (WH) properties and heritage sites in general are exposed to the impacts of natural and man-triggered catastrophic events, which threaten their integrity and may compromise their value. The loss or deterioration of these outstanding properties has severely negative impacts on local and national communities, both because of their cultural importance, and because of their socio-economic value (https://whc.unesco.org/en/disaster-risk-reduction/). In the framework of the H2020-ARCH project a simplified approach was implemented for assessing and monitoring the subsidence hazards and the possible induced damages and short- and long-term impacts that these might cause on the buildings and other structures composing and characterising the WH sites. The InSAR data sets relating to the WH area of the Hamburg city were processed to evaluate the deformation velocity affecting each building, in order to project the Subsidence Related Intensity parameters in the short-and long-term, and thus estimate the expected damage levels through fragility curves. The results show a relatively stable situation in the area, however damage levels of a certain consistency are detectable for a long period due to the differential settlements. Furthermore, the results obtained allow us to evaluate the socio-economic impacts on the basis of some assumptions about potential losses.

In this study, Synthetic Aperture Radar (SAR) methods that exploit both co- and cross-polarized channels are used to detect volcanic deposits caused by the eruption occurred the Sangay Volcano, Ecuador, at the end of November and the beginning of December 2021. First, a change detection scheme, based on the ratio of a pair of images collected before and after the event, is separately applied to the co- and cross-polarized channels. Then, the outputs are analyzed to detect the different phenomena (i.e., lava flow, pyroclastic currents, landslides), that occurred over the scene. Results, undertaken over a pair of images collected before and after the event with the C-band Sentinel-1 Dual Polarimetric (DP) SAR sensor, show that the proposed approach allows for detecting the different phenomena that occurred on the volcanic area. In addition, the co- and cross-polarized channels carried out different information which suggests the combination of them to obtain additional information.

In this study, Synthetic Aperture Radar (SAR) methods are combined with optical ones for earthquake damage assessment after the 2023 Turkey earthquake. First, a change detection scheme, that exploits both SAR and optical images is applied to detect and classify the damaged buildings. Then, the SAR and optical data are combined using a data fusion step based on the k-means algorithm, to improve the reliability and accuracy of the damage assessment map. Results for the city of Kahramanmaras, and undertaken over images collected before and after the event with the X-band COSMO-SkyMed-Second Generation (CSG) SAR sensor and PLANETSCOPE (PLTS) optical sensor rely on demonstrating the effectiveness of the joint use of SAR and optical change detection methods in detecting damage to buildings and infrastructure that can be used for disaster management authorities to prioritize rescue and relief operations in the affected regions.

This study is to discuss the ability of full-resolution, i.e., speckled, Sentinel-1 single-look-complex (SLC) Synthetic Aperture Radar (SAR) imagery to monitor water-area coverage of salt ponds. Three scenarios are considered: 1) completely filled of water, 2) empty, 3) intermediate. The metric adopted to track the pond water area evolution is based on the speckle statistics and, in particular, on the departure from the fully developed speckle model.Experiments, performed using a time-series of Sentinel-1 SAR imagery collected over the salt pond "Sale ’e Porcus", Sardinia, Italy, show that Normalized Intensity Moments (NIMs) allow a good separation between the three cases under different wind conditions.

In this study, a dual-polarization (DP) processing chain is presented to identify lava flow after a volcanic eruption. The proposed methodology is applied to two very challenging case studies, related to the volcanic eruptions that occurred on 24 December 2018 on Mount Etna, Italy, and on 22 May 2021 on Mount Nyiragongo, Democratic Republic of the Congo. The different geomorphology of the two areas together with the different types of lava (i.e., different rheology and rock composition) and surrounding environment is an unprecedented opportunity to analyze the radar polarimetric contribution. First, a DP change detection scheme, which uses a pair of SAR images collected before and after the eruption, is used. Then, the lava flow is detected using a constant false alarm rate detector. Finally, to quantify the performance of the detector, the SAR-based lava flow maps are contrasted with the lava flow footprint provided by Copernicus Emergency Management Service (EMS). Experimental results show that the use of polarimetric information improves lava flow detection in different kinds of environments.

In this study, dual-polarimetric (DP) Synthetic Aperture Radar (SAR) time series collected by the Sentinel-1 SAR mission is exploited to evaluate the changes that occurred over the surface water area of the Po River, in Italy. First, a DP technique based on an incoherent combination of co–and cross-polarized SAR channels are used to enhance the separability between surface river water and land areas. Then, a binary image where the river and the land area are well separated is generated using a constant false alarm rate (CFAR) detector. Once the water-covered area is estimated, an in-depth analysis based on the Shannon entropy is applied to understand the time variability of the water extension of the Po River. Experimental results show that the dual-polarimetric approach can effectively monitor the water body changes that occurred over the river Po basin in an effective way.

Mt. Etna is one of the world's most active volcanoes and its activity is characterized both by explosive eruptive episodes and by effusive activity. On 24 December 2018 a new lateral eruption of Etna began. The phenomenon was caused by the intrusion of a magmatic dike in the upper eastern flank of the volcano which produced an intense seismic swarm and strong gaseous emissions from the summit craters. In this work Synthetic Aperture Radar (SAR) data acquired from the Sentinel-1 satellite mission were elaborated adopting the Small Baseline Subset technique (SBAS). This multi-temporal method of SAR Interferometry (InSAR) allowed us to obtain the ground displacement map of the area and the deformation evolution of some eruption-induced phenomena, i.e., we tracked the lava flows originated from the opening of eruptive fissures in the western flank of the Valle del Bove depression, and we monitored a paleo-landslide located about 5 kilometers West from the Paternò village, which reactivated during the volcanic unrest.

The rapidly evolving field of open science has made scientists agree on the idea that data, workflows and services, should be findable, accessible, interoperable, and thus optimally reusable. These principles apply to the geohazard community also, dealing with natural phenomena such as volcanic and seismic activity. However, there is still a weakness regarding research sharing and re-use through the scientific community, due to lack of technological solutions and their long-term implementation. We present a lifecycle management service ecosystem based on Research Objects to manage scientific works, in terms of methods, workflows and intermediate or final results, according to open science principles. We show a data cube service for scalable structured data discovery, access and interoperation. These services foster the adoption of remote sensing (e.g., Copernicus) data in the geohazard scientific community enabling research lifecycle documentation. We present test cases of ground deformation analysis and modelling, and volcanic cloud mapping.

In this study, the potentiality of an automatic processing system for fast implementation of interferometric SAR analysis is tested over a time series of COSMO-SkyMed data. A system architecture for the end-to-end implementation of Permanent Scatterer workflow is illustrated and applied to a test case located over the South-West area of the Rome area. The system architecture is aimed at providing a high degree of automation in the different steps of the procedures, from the Satellite products download and storage up to the data visualization. The proposed solution can be integrated with both commercial processing tools and open source frameworks. A two years of COSMO-SkyMed acquisitions from the MAPITALY archive have been analysed with SARscape and a customized open source workflow. The obtained results highlighted some interesting features on the considered area of interest. Moreover, the outcome of the analyses are in good agreement with previous studies on the same geographical region.

In this study, dual-polarization (DP) C-band Sentinel-1 SAR imagery are processed to generate a damage map related to an earthquake damaged area. A DP change detection scheme, which uses a pair of SAR imagery collected before and after the earthquake, is used. Finally, to quantify the levels of damage a decision tree procedure is exploited. The damaged area refers to the city of Amatrice and its surroundings that were devastated by the earthquake that hit central Italy on 24 August 2016. To generate the damage map and to verify it, ground information provided by Istituto Nazionale di Ge-ofisica e Vulcanologia (INGV) is processed to generate a new index, termed as Weighted Damage Index (WDI). Numerical results show that an accuracy of about 60% can be obtained that confirms the soundness of the proposed approach.

The use of SAR data for imaging hazardous events is nowadays a well-consolidated approach. Thanks to the synoptic capabilities of satellite data, their high spatial resolution and high revisit time, fast and accurate analysis can be performed at both regional and local scale. This work presents the results of SAR Interferometry applied to a multi-hazard event induced by the eruption of Etna volcano, occurred in late December 2018. We analyse both the syn-eruptive deformation of volcano edifice and the effects of an induced Mw4.9 earthquake and a small-scale landslide by exploiting ESA Sentinel-1 data. The data highlight a main horizontal displacement of the Etna summit of more than 40 cm and 65 cm along west and east directions, respectively. The earthquake caused about 15 cm displacement toward east and 20 cm toward west. Finally, approximately 5 cm displacement along satellite Line of Sight was observed associated to the landslide.

We exploit along-track offset estimation techniques applied to Optical and Synthetic Aperture Radar (SAR) data to constrain the co-seismic slip induced by the Mw7.5 earthquake occurred on September, 2018 in Sulawesi island, Indonesia. Such seismic event nucleated along the left-lateral NNW-SSE trending Palu-Koro strike-slip fault and strongly affected Palu Bay causing several effects such as tsunamis and soil liquefactions. Preliminary results show a deformation of more 1.5m along an approximately NS direction both on Optical and SAR data. Moreover, both techniques clearly highlight the surface rupture induced by the earthquake. Further analysis will be focused on accurate comparison and validation of the retrieved results.

In this study, multi-frequency Synthetic Aperture Radar Interferometry (InSAR) data are used to constrain the ground deformation due to the August 21st, 2017, Ischia earthquake. InSAR results are the input data to infer the source model of the seismic event. C-band Sentinel-l and X-band COSMO-SkyMed© InSAR results show a displacement pattern in the epicentral area, with a maximum value of ~3.5 cm and a dominant vertical component. Preliminary analytical and numerical models suggest that the retrieved InSAR ground displacements are due to the combination of two phenomena: the tectonic dislocation caused by the slip on the fault plane and the surficial displacement caused by the triggering of shallow landslides.

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-l (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 work investigates the capability of Interferometric SAR (InSAR) technique to monitor the atmosphere. Changes in the Atmospheric Phase Screen (APS) derived by InSAR are strictly related to changes in the atmosphere stratification and to the 2-D distribution of the water vapor columnar content. High spatial/temporal resolution APS maps provide by a SAR geosynchronous satellite (GEO-SAR) can be assimilated into limited area models (LAMs) for a better characterization of the local scale phenomena. As the observation of GEO-SAR are taken with high incidence angles, the path delay is measured along the slant direction (STD) and thus both the observation operator and the Global Navigation Satellite System (GNSS) products used for calibrating the APS must be considered along that direction. In this work, we compare the STD predicted by the Weather Research and Forecasting Model (WRF), and retrieved by a network of GNSS stations with the APS derived from Sentinel-1.

We exploit C-band Sentinel-1 and L-band ALOS-2 Synthetic Aperture Radar Interferometry (InSAR) data to constrain the seismic source of the Mw 6.0 intraplate earthquake occurred on May 20, 2016 in the central Australia. It is an uncommon seismic event since it occurred in a region characterized by low seismic activity. The retrieved source modeling revealed that the earthquake nucleated along a NW-SE-oriented reverse fault with a significant strike-slip component.

The assessment of satellite image classifications is usually carried out using a test sample assumed as the ground truth, from which a confusion matrix is derived. There are cases where the reference data, even those coming from a ground survey, are affected by errors and do not represent a reliable truth. In the field of geophysical parameter retrieval, the triple collocation (TC) technique is applied for validating remotely sensed products when the source of test data (e.g., ground data) does not represent a reliable reference. TC is able to retrieve the error variances of three systems observing the same target parameter, assuming that their errors are independent. In this paper, we exploit the same idea to test the classification accuracy in cases where the ground truth is not available. We extend the TC approach to the classification problem for a general number of classes, but we solve it numerically for a two-class problem (i.e., collapsed and noncollapsed buildings). The specific case refers to the detection of L'Aquila 2009 earthquake damage from very high-resolution optical data. The image classification, performed by exploiting an object-based analysis, is compared with those from two different ground surveys carried out after the earthquake by different teams and with different purposes. This paper demonstrates the power of the TC approach for assessing the classification accuracy with no reliable ground truth available, and provides an insight into the problem of assessing damage, from satellite and on ground, in a very critical and unsafe situation, like the one occurring after an earthquake. Moreover, it was found that the remotely sensed product can have an order of accuracy comparable to that of at least one of the ground surveys.

Nowadays very high resolution (VHR) Synthetic Aperture Radar (SAR) systems can provide near real time earthquake damage maps with an high degree of details to stakeholders in charge of managing the emergency phase. However, the increased resolution introduces new challenges to interpret and detect changes in urban areas caused by seismic events. In metric resolution SAR sensors a building appears as a complex of image structures associated to different scattering mechanisms, preventing the use of pixel-based algorithms. In this paper we propose an object oriented approach, focusing the attention on the double-bounce return from buildings, trying to detect damages looking at changes of these particular image patterns. The identification of double-bounce regions is performed using open and close morphological filters and assuming linear structuring elements with different orientation and length. The change detection analysis based on a pre- and a post-event image is carried out using four change detection indicators, such as: intensity ratio, interferometric coherence, intensity correlation and Kullback-Leibler divergence. All change features are extracted using all pixels within each identified object, i.e., double-bounce regions. The test case is the earthquake that hit L'Aquila city (Italy) on April 6, 2009, while the dataset is composed of two X-band COSMO-SkyMed SAR images acquired before and after the event. A macro-seismic survey map was available to evaluate the obtained results.

This work presents the main outcomes of a land use change detection analysis by means of a classification algorithm based on very high resolution (VHR) optical images. The satellite data we used were captured by the sensor on board of IKONOS and GeoEye-1, on January 2002 and June 2012, respectively. Land use map at each day has been obtained merging the results of a supervised multispectral per-pixel classification and an unsupervised hierarchical segmentation aiming at classifying the objects in the VHR images selecting the spatial scales which maximize the final classification accuracy. The change detection land use analysis has been performed in post classification phase, comparing the resulting land use maps. The implemented classification architecture, called Scale Object Selection (SOS), allowed to obtain an overall classification accuracy higher than 97%, and a K-coefficient of about 0.95.

The monitoring of Cultural Heritages is an operational issue that requires a multidisciplinary approach to provide their dynamic and sustainable surveying in response to climate changes and natural events. Within such a framework, an integrated system of proximal remotely-sensed tools is investigated for Cultural Heritage monitoring purposes in a seismic area. It consists of a Ground-Based Synthetic Aperture Radar (GBSAR), a GB Real Aperture Radar (RAR) and a Terrestrial Laser Scanner (TLS). Preliminary results, gathered over the Sant'Agostino Church in Cosenza city, allow demonstrating the benefits of the proposed approach for Cultural Heritages monitoring.

We applied the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to investigate and model the Mw 5.3 (Ml 5.2), June 21st, 2013 earthquake, occurred in the discontinuity zone between the Lunigiana and Garfagnana areas, NW Italy. We analyzed the coseismic displacement from two differential interferogram generated using COSMO-SkyMed X-band and RADARSAT2 C-band data. Both interferograms show a pattern of subsidence of a few cm located between the Lunigiana and Garfagnana basins. We then modeled the observed SAR deformation fields using the Okada analytical formulation and found them to be consistent with an extensional fault plane dipping towards NW at about 50°. Our results suggest that the fault responsible for the June 2013 earthquake may correspond to a breached relay ramp connecting the Lunigiana and Garfagnana seismogenic sources.

The present work focuses on the analysis of post-seismic surface deformation detected in the region of L'Aquila, Central Italy, after the strong earthquake that hit the city and the surrounding villages on April 6, 2009. The analysis has been carried out thanks to a new dataset of SAR COSMO-SkyMed images, and to the adoption of the Persistent Scatterer Pairs (PSP) approach. This method allow the estimate of surface deformations by exploiting the SAR images at full resolution. Two patterns of subsidence have been identified reaching a maximum value of 45 mm in the northeast area of the L'Aquila town. Here the subsidence is mainly ascribable to the post seismic slip release of the Paganica fault and it does not coincide with the maximum measured coseismic subsidence. The time series of the ground deformations also reveal that a large amount of deformation is released in the first three months after the main shock. The second pattern of deformation interests the Mt. Ocre ridge, where a detailed photogeological analysis allowed us to identify widespread evidence of morphological elements associated with Deep-seated gravitational slope deformation (DGSD). In this sector the observed deformation is mainly ascribable to a gravitative cause.

In this letter, we report the presence of a localized Doppler anomaly occurring during the focusing of a Radarsat-2 data set acquired on the Merapi volcano (Indonesia) during the devastating 2010 eruption. The Doppler anomaly is manifested as ~ 3-km-wide bull's-eye-shaped azimuth pixel shifts between two subaperture images. The Doppler anomaly is centered on the summit-south flank of the Merapi volcano. The pixel shifts reach up to 11.6 m. Since the Merapi volcano was undergoing a large eruption during the data acquisition, it is possible that there is a volcano-related phenomenon that has delayed the radar signal so much to create measurable pixel offsets within a single synthetic aperture radar (SAR) data set, similar, but more extensive, to the signal generated by targets motions; similar, but less extensive, to the signal generated by ionospheric perturbations. It is known that the SAR signal is delayed as it passes through heterogeneous layers of the atmosphere, but this delay typically affects the SAR signal to a fraction of the phase cycle or few centimeters depending on the radar wavelength employed by the system. We investigate the source of this anomalous metric signal; we review the theoretical basis of SAR image focusing, and we try to provide a consistent physical framework to our observations. Our results are compatible with the SAR signal being perturbed during the actual process of image focusing by the presence of a contrasting medium located approximately between 6- and 12.5-km altitude, which we propose being associated with the presence of volcanic ash plume.

On March 11, 2011 a Mw 9.0 earthquake hit Honsu island in Japan. The so called “Tohoku-Oki” giant earthquake occurred near the northeast coast resulting from thrust faulting on or near the subduction zone plate boundary between the Pacific and North America plates. The rupture zone is roughly centered on the earthquake epicenter alongstrike, while peak slips were up-dip of the hypocenter, towards the Japan Trench axis. The rupture was also responsible of a big tsunami that struck a large portion of the coastal area of Tohoku-Oki region. Soon after the earthquake numerous space remote sensing sensors were tasked to acquire as much as possible information about the situation on ground. In particular, a large numbers of SAR images were collected from the ENVISAT and ERS-2 satellite. For this event, a group of researchers from the Istituto Nazionale di Geofisica e Vulcanologia (INGV), named Tohoku-Oki INGV Team, decided to apply a multidisciplinary approach to carry on some analysis addressed to achieve added value outcomes. In this work we present part of the work done by the Team concerning DInSAR data analysis and the methodology to retrieve the seismic source of this extraordinary and disastrous event.

The volume estimation of products is critical for volcanic hazard mitigation, especially for lahars (mudflow) occurrences during rainy season at Merapi, and at active volcanoes subject to lahars elsewhere. Lahars can affect inhabited areas around a volcano, even several years after an eruption. In this work, we present an innovative approach to detect and estimate the volume of pyroclastic flow deposits. We exploited data collected from the very high resolution SAR sensor on board of the COSMO-SkyMed satellite constellation. By comparing a pre-eruption airborne Digital Surface Model (DSM) with a new one obtained applying SAR Interferometry technique to COSMO-SkyMed data, we estimate the volume of the pyroclastic material emitted during the 2010 Merapi eruption. Results show pyroclastic flow deposit thicknesses of up to 75 m that fill canyons on flank of volcano, and are observed up to 16 km far from the mountain summit. The total volume of the deposits is around 117×106m3.

As far as Ku band is concerned, the backscatter coefficient (sigma naught) of the radar altimeter (RA-2) aboard the ENVISAT satellite has been calibrated using a transponder (TPD) developed at ESA/ESTEC. The transponder has been exploited during the 6 month Commissioning Phase to generate early calibration results. In order to consolidate this calibration results and to check the altimeter during the satellite lifetime, a continuous monitoring was performed by operating the transponder as much as possible. The first calibration campaign dates back to February 24th, 2004. The last campaign occurred on October 5th, 2010. Since then the change of the ENVISAT orbit hampered the prosecution of the activity. This paper aims to review the entire effort for calibrating the RA-2 sigma naught measurements, which lasted for almost seven year. It presents the final outcomes of the activity, providing the users with the correction (bias) to get the calibrated sigma naught and analyzing its stability during almost the entire ENIVISAT lifetime.

In this work, the outcomes of a research activity, that lasted approximately seven years (2003-2010), in which soil moisture was monitored on a test site in Northern Italy by collecting a series of SAR images and in situ data are presented. Radar data were provided by the C-band ENVISAT/ASAR instrument. The research activity aimed at calibrating and validating a pre-operational algorithm, conceived to be used by the Italian Civil Protection, for high resolution soil moisture mapping from SAR data. The algorithm is focused on the Bayesian theory of parameter estimation. The Maximum A Posteriori (MAP) probability criterion or the Minimum Variance one are used to retrieve soil moisture by inverting a forward scattering model. Ancillary data such as optical images and land cover data are also used. The results of the validation activity have confirmed the validity of the proposed mapping approach. In particular, the algorithm allowed us to retrieve soil moisture with a R2coefficient of 0.77 and with a root mean square error in the order of 0.07 m3/m3.

This letter compares the coseismic deformation maps obtained from different synthetic aperture radar (SAR) sensors using the well-known differential SAR interferometry technique. In particular, four deformation maps have been obtained from X-, C-, and L-band SAR sensors onboard COSMO-SkyMed, Envisat, and ALOS satellite missions correspondingly. The test case is the April 6,2009, earthquake (Mw= 6.3). This seismic event struck a densely populated region of the Apennines and was felt all over Central Italy. The SAR data set is rather inhomogeneous, since it includes interferograms with three different wavelengths, four acquisition geometries, different spatial resolutions, variable temporal and spatial baselines, and differently emphasized signal noise. However, we find that the detected displacements are highly comparable. The outcome of this work is that, even though such differences have an impact on the properties of the interferograms, the displacements can be measured with an overall discrepancy of about half the value of the shortest wavelength (COSMO-SkyMed) data set.

In this paper, we investigate the performance of pulse-coupled neural networks (PCNNs) to detect the damage caused by an earthquake. PCNN is an unsupervised model in the sense that it does not need to be trained, which makes it an operational tool during crisis events when it is crucial to produce damage maps as soon as the post-event images are available. The damage map resulting from PCNN was validated at a block scale of 120×120m using ground truth obtained by a combination of ground survey and visual inspection of the before- and after-event images. The comparison showed agreement between the change measured by PCNN on block scale and the damage occurred.

A destructive (Mw 7.9) earthquake affected the Sichuan province (China) on May 12, 2008. The seismic event ruptured approximately 270 km of the Yingxiu-Beichuan fault and about 70 km of the Guanxian-Anxian fault. Surface effects were suffered over a wide epicentral area (about 300 km E-W and 250 km N-S). We apply the differential synthetic aperture radar interferometry (DInSAR) technique to detect and measure the surface displacement field, using a set of ALOS-PALSAR L-band SAR images. We combine an unprecedented high number of data (25 frames from six adjacent tracks) to encompass the entire area which has coseismically displaced. The resulting mosaic of differential interferograms covers an overall area of about 340 km E-W and 240 km N-S. We investigate the source of the Sichuan earthquake by modeling the DInSAR data. The geometry and position of the fault parameters are inferred by a nonlinear inversion, followed by a linear inversion to retrieve the relative slip distribution. Our results show two different source mechanisms for the 145-long Yingxiu-Beichuan fault and for the 105-long Beichuan-Qingchuan fault. Both faults are characterized by slip concentrations of up to 8 m.

Two different approaches to deal with the problem of estimating soil moisture content from SAR data in the presence of vegetation are presented. They exploit also the information about the biomass provided by ancillary optical data. The first method is suitable for sparse vegetation and is founded on the application of the well-known water cloud model. As for dense vegetation canopy, we have designed a model that expresses the variation of the component of the backscattering coefficient due to the soil characteristics as a function of the variations of the measured backscattering coefficient and of the biomass, assuming the availability of a time series of radar and optical data. To carry out the soil moisture retrieval, a multi-temporal inversion algorithm, based on the Bayesian MAP criterion, has been developed. It integrates all the samples of the time series of SAR data corrected for the vegetation effects. The approaches were evaluated on two case studies; the first one concerning an ENVISAT/ASAR observation of an agricultural site located in Northern Italy. The second test was performed on the AirSAR data collected during the SMEX02 experiment. The comparison between the estimated soil moisture contents and the in situ measurements has given encouraging results.

This letter is focused on the microwave signature characterization of the Greenland ice sheet. Such characterization is carried out by exploiting the S- and Ku-band brightness temperatures measured by the radar altimeter RA-2 when it operates as a radiometer during the ENVISAT Commissioning Phase for the purpose of calibrating the receiver. Despite the poor radiometric resolution and the calibration issues, this activity represented a unique opportunity to gather brightness temperatures at frequencies that are not available from current spaceborne microwave radiometers. The analysis of the passive RA-2 data investigates the influence of terrain height and of the temperature of the snow layers on the brightness temperatures at RA-2 bands. The effect of the different penetration depths of the electromagnetic radiation at S- and Ku-bands is also pointed out. Measurements from the Advanced Microwave Scanning Radiometer for the Earth Observing System are used to complement the data provided by RA-2 and to verify their reliability.

SIGRIS (SIstema di osservazione spaziale per la Gestione del RIschio Sismico) is a pilot project aiming to the realization of a system, based on satellite remote sensing data, for the seismic risk management. The project is funded by the Italian Space Agency (ASI). ASI is deeply interested on the development of new applications, using satellite data, dedicated to the monitoring and management of the natural hazards. SIGRIS is focused on providing the information services for mapping, monitoring, forecasting and awareness of seismic risk. The Earth Observation products are generated by using GPS data, optical and SAR (Synthetic Aperture Radar) images. This project deals with the data exploitation of the new Italian Earth Observation mission: COSMO-SkyMed, a constellation of four satellites equipped with an X-band high resolution SAR.

Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks to their very high ground geometric resolution, especially when more frequent revisit times will be feasible to the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the single building scale. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the December 26th, 2003, earthquake that hit the ancient city of Bam (Iran) has been investigated. The urban area was very close to the epicenter of the seism thus causing strong damage to the urban structures. Pre- and post-earthquake QuickBird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided byinsitusurvey.

An inundation event occurred in Italy on November 1994 has been analysed in order to asses the capability to map the flooded areas by radar (SAR) images in support to civil protection interventions. ERS images collected before and after the inundation have shown the presence of different scattering mechanisms occurring in the flooded areas and originating either an increase or a decrease of the backscattering coefficient, depending on the land covers type. This analysis has allowed us to develop a flood discrimination procedure based on a fuzzy approach able to integrate all the possible sources of available data (SAR images, land cover and a DEM) and prior information (cover dependent backscattering behaviour). A comparison with a ground survey providing the maximum flood extension has given encouraging results.

The passive calibration of the Radar Altimeter (RA) consists of characterizing the receiver gain by observing natural surfaces with known emission in the so-called noise-listen mode. It is based on the comparison between the simulated values of the brightness temperature impinging on the altimeter antenna, and the digital counts at the output of the altimeter receiver in the absence of echo. The proposed method aims to calibrate measurements of the backscattering coefficient performed by a spaceborne altimeter based on the assumption that the receiver gain is the main source of uncertainty. This paper focuses on the general approach undertaken to characterize the receiver and to simulate the brightness temperature at the top of the atmosphere observed by the Envisat RA-2. The simulations rely on emissivity models for land and sea, as well as on atmospheric radiation models supported by a continuous flow of online data used as model inputs. To assess the accuracy, the model outputs are compared with observations from calibrated radiometers, namely the Special Sensor Microwave/Imager and Tropical Rainfall Measuring Mission Microwave Imager, with particular attention to the low-frequency channels (10 and 19 GHz). The new method has been first tested on European Remote Sensing satellite data and has been subsequently adopted for Envisat RA-2 in the framework of the Envisat Calibration and Validation activities managed by European Space Agency. The evaluation of the receiver gain at both Ku-band and S-band is presented and compared to the preflight values, as well as to transponder calibration done for Ku-band. An error budget for the final estimates is also presented and discussed

The prompt detection, mapping and assessment of urban damages due to earthquakes is a key point, particularly in remote areas or where the infrastructures are not well developed to ensure the necessary communication exchanges or where their operability has strongly decreased as a consequence of the event. The combination of Synthetic Aperture Radar (SAR) data and optical images is a promising and suitable approach. We propose two test cases, the 1999 Izmit (Turkey) and the 2003 Bam (Iran) earthquakes where we investigate the capability to detect urban changes and classify them. Moreover, a comparison with ground based data is also shown.

This work present some experimental campaigns aiming to assess the potential of monitoring soil moisture by ENVISAT ASAR (Advanced Synthetic Aperture Radar). Soil moisture measurements were carried out on November 2003 and June 2004 close to Alessandria (Northern Italy) by using a TDR probe simultaneously to ENVISAT overpasses. In situ measurements had been collected in agricultural fields that were subsequently identified on ENVISAT ASAR images by means of a geo-coding process. Pixels of each area have been averaged in order to compute the mean backscattering coefficient. Results have been derived concerning the sensitivity of the backscattering coefficient to soil-moisture compared to what is predicted by a semi-empirical scattering model.