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Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order to fully exploit the content of MTInSAR products, methods are needed for the automatic identification of relevant changes along displacement time series and the classification of the targets on the ground according to their kinematic regime. This work reviews some of the classical procedures for model ranking, based on statistical indices, which are applied to the characterization of MTInSAR displacement time series, and introduces a new quality index based on the Fisher distribution. Then, we propose a procedure to recognize automatically the minimum number of parameters needed to model a given time series reliably within a predefined confidence level. The method, though general, is explored here for polynomial models, which can be used in particular to approximate satisfactorily and with computational efficiency the piecewise linear trends that are generally used to model warning signals preceding the failure of natural and artificial structures. The algorithm performance is evaluated under simulated scenarios. Finally, the proposed procedure is also demonstrated on displacement time series derived by the processing of Sentinel-1 data.

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth, allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. This technique has been proved to be very useful for detecting and monitoring also slope instabilities. Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit time. The high-resolution X-Band COSMO-SkyMed constellation, acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA), provides a spatial resolution comparable to the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover, it allows collecting large data stacks in a short time period, thus improving the MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. on Montescaglioso in 2013) and also very recently (e.g. on Pomarico in 2019), causing damages to houses, commercial buildings, and infrastructures. SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to the slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, to non-linear trends that are possibly related to relevant ground instabilities and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to the well know events occurred in the area of interest.

Multi-temporal SAR interferometry (MTInSAR) techniques are able to derive displacement maps and displacement time series over coherent objects on the Earth, used for monitoring either geophysical ground deformation or structural instabilities. Nowadays, several datasets are available at different wavelengths, spatial resolutions, and revisit time, collectively covering long time periods (even more than 20 years). In particular, short revisit times, by improving the temporal sampling, make theoretically possible to catch high-rate and non-linear kinematics, which typically characterize warning signals like, for instance, those related to pre-failure of artificial infrastructures. However, MTInSAR algorithms generally fit the displacement signal by using a linear model, which is computationally convenient, and also, more robust than higher-order models for what concerns the errors affecting the InSAR phase. Moreover, the analysis of the MTInSAR products is often performed by only considering the mean displacement rate, computed over the monitoring period, which is the information typically displayed on the displacement maps. However, this approach suffer for two main limitations: 1) by analysing time series covering long time periods (as more and more common with the datasets available nowadays), it is more probable that nonlinear displacements occur; 2) the mean displacement rate, though useful for distinguishing between stable and unstable areas on the ground, often mask interesting nonlinear signals, useful for early warning. Therefore, in order to fully exploit the content of MTInSAR products, methods are needed for automatically identifying relevant changes along displacement time series, and, consequently, classifying the targets on the ground according to their kinematic regime. This also allows performing a more reliable ground deformation spatial analysis, by distinguishing among spatial patterns of different kinematics. Recently, approaches have been proposed to tackle this problem, which use different strategies. This work presents some general considerations and a possible new procedure based on statistical characterization of displacement time series, which allows, under certain constraints, recognising automatically different kinematic classes, and estimating the relevant parameters useful for target characterization in time. The theoretical formulation of the proposed method is introduced, then the algorithm performance is evaluated under simulated scenarios, by varying simulation parameters such as type of kinematic model, temporal sampling rate, number of observations, and signal to noise ratio. Finally, some results of the analysis of displacement time series derived by MTInSAR processing of real datasets are presented.

We present an example of integration of persistent scatterer interferometry (PSI) and in situ measurements over a landslide in the Bovino hilltop town, in Southern Italy. First, a wide-area analysis of PSI data, derived from legacy ERS and ENVISAT SAR image time series, highlighted the presence of ongoing surface displacements over the known limits of the Pianello landslide, located at the outskirts of the Bovino municipality, in the periods 1995–1999 and 2003–2008, respectively. This prompted local authorities to install borehole inclinometers on suitable locations. Ground data collected by these sensors during the following years were then compared and integrated with more recent PSI data from a series of Sentinel-1 images, acquired from March 2014 to October 2016. The integration allows sketching a consistent qualitative model of the landslide spatial and subsurface structure, leading to a coherent interpretation of remotely sensed and ground measurements. The results were possible thanks to the synergistic operation of local authorities and remote sensing specialists, and could represent an example for best practices in environmental management and protection at the regional scale.

Producing high-precision flood maps requires integrating and correctly classifying information coming from heterogeneous sources. Methods to perform such integration have to rely on different knowledge bases. A useful tool to perform this task consists in the use of Bayesian methods to assign probabilities to areas being subject to flood phenomena, fusing a priori information and modeling with data coming from radar or optical imagery. In this chapter we review the use of Bayesian networks, an elegant framework to cast probabilistic descriptions of complex systems, applied to flood monitoring from multi-sensor, multi-temporal remotely sensed and ancillary data.

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, atmospheric artifacts, and visibility problems related to ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new and interesting opportunity is provided by Sentinel-1, which has a spatial resolution comparable to that of previous ESA C-band sensors, and revisit times improved by up to 6 days. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications in terms of ground instability monitoring. Issues related to coherent target detection, mean velocity precision, and product geo-location are addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of a multi-sensor ground instability investigation over Lesina Marina, a village in Southern Italy lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been processed, coming from both legacy ERS and ENVISAT missions, and latest-generation RADARSAT-2, COSMO-SkyMed, and Sentinel-1A sensors. Results confirm the presence of a rather steady uplift process, with limited to null variations throughout the whole monitored time-period.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science. Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover. We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

The use of satellite Synthetic Aperture Radar Interferometry (InSAR) for monitoring ground instability due to landslide events, although advantageous over large spatial scales, still poses challenges related to the recurrently complex kinematics of the phenomena or to the unfavorable settings of the examined areas with respect to steep topography and vegetated land cover. This paper presents results obtained by using Multi-temporal InSAR techniques with high resolution TerraSARX (TSX) data for monitoring the Carlantino landslide, located in the Daunian Subapennine (Apulia region, southern Italy) on a slope overlooking a water reservoir, and subjected to several investigations and consolidation works. The targets detected by using Persistent Scatterer Interferometry (PSI) correspond to urban structures or peri-urban walls and guard rails, while the landslide body is almost completely devoid of stable targets, due to the widespread vegetation and variable land cover. To allow stability monitoring, a network of six Corner Reflectors (CR) was designed and deployed over the landslide test site. The TSX images were analyzed by using both the PSI processing and a procedure, based on the double difference analysis of InSAR phase values on the CR pixels. Despite residual noise and the loss of 2 CRs due to vandalism, the processing allowed verifying the stability of the upper and central part of the landslide body, and relating indirectly the movements at the toe of the landslide to the water level fluctuations of the reservoir. Finally, this experiment suggests some recommendations and guidelines in planning CR deployment in complex landslide sites.

Monitoring represents the main tool for carrying out evaluation procedures and criteria for spatial and temporal landslide forecast. The forecast of landslide behaviour depends on the possibility to identify either evidences of activity (displacement, velocity, volume of unstable mass, direction of displacement, and their temporal variation) or triggering parameters (rainfalls). Generally, traditional geotechnical landslide monitoring technologies permit to define, if correctly positioned and with adequate accuracy, the critical value of displacement and/or acceleration into landslide body. In most cases, they do not allow real time warning signs to be generated, due to environmental induced errors, and the information is related to few points on unstable area. Remote-sensing monitoring instruments are capable of inspecting an unstable slope with high spatial and temporal frequency, but allow solely measurements of superficial displacements and deformations. Among these latest technologies, the satellite Persistent Scatterer SAR Interferometry (PSInSAR) is very useful to investigate the unstable area both in terms of space and time. Indeed, this technique allows to analyse wide areas, individuate critical unstable areas, not identifiable by means detailed in situ surveys, and study the phenomenon evolution in a long time-scale. Although this technique usually adopts, as first approximation, a linear model to describe the displacement of the detected targets, also non-linear models can be used. However, the satellite revisit time, which defines the time sampling of the detected displacement signal, limits the maximum measurable velocity and acceleration. This makes it difficult to assess in the short time any acceleration indicating a loss of equilibrium and, therefore, a probable reactivation of the landslide. The recent Sentinel-1 mission from the European Space Agency (ESA), provides a spatial resolution comparable to the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of PSInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover, it allows collecting large data stacks in a short time period, thus improving the PSInSAR performance in emergency (post-event) scenarios. In the present work, we propose to match satellite data with numerical analysis techniques appropriate to evidence unsteady kinematics and, thanks to the high resolution of satellite data and improved temporal sampling, to detect early stages of land instability phenomena. The test area is situated in a small town in the Southern Apennine, Basilicata region, affected by old and new huge landslides, now close to a lived outskirt.

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

This paper describes a novel SAR wind direction estimation method based on the computation of local gradients over quasi-linear and quasi-periodic structures detected by SAR imagery. The method relies upon the standard LG method for the part relevant to the computation of the local gradients. The novelty is that the dominant local wind direction and related accuracy are estimated using results derived from the Directional Statistics. The LG-Mod is validated against in situ coastal wind measurements provided by instrumented buoys with 63 ENVISAT ASAR images. Results show an overall agreement with RMSE values obtained for off-shore areas, but residual effects due to the complex phenomena occurring in the proximity of shoreline may degrade the performance when running in automated mode.

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 exploitation of a multi-temporal stack of SAR intensity images seems to provide satisfactory results in flood detection problems when different spectral signature in presence of inundation are observed. Moreover, the use of interferometric coherence information can further help in the discrimination process. Besides the remote sensing data, additional information can be used to improve flood detection. We propose a data fusion approach, based on Bayesian Networks (BNs) , to analyze an inundation event, involving the Bradano river in the Basilicata region, Italy. Time series of COSMO-SkyMed stripmap SAR images are available over the area. The following random variables have been considered in the BN scheme: F, that is a discrete variable, consisting of two states: flood and no flood; the n-dimensional i variable, obtained by the SAR intensity imagery; the m-dimensional γ variable, obtained by the InSAR coherence imagery; the shortest distance d of each pixel from river course. The proposed BN approach allows to independently evaluate the conditional probabilities P ( i | F ), P (γ| F ) and P ( F | d ), and then to join them to infer the value P ( F = flood | i , γ, d ), obtaining the probabilistic flood maps (PFMs). We evaluate these PFMs through comparisons with reference flood maps, obtaining overall accuracies higher than 90%.

Near-infrared (NIR) satellite images of the oil spill event caused by the Fu Shan Hai wreck on 31 May 2003 in the waters between Sweden and Denmark were compared with numerical simulations provided by the MIKE 21 oil drift model. Assuming a skewed probability density function (pdf) of oil parcel thicknesses, a model of the NIR image oil-water contrast reflectance was developed to characterize the expected oil slick distribution in terms of average and maximum oil slick thickness. Since MIKE 21 Spill Analysis (SA) also allows non-uniform distribution of oil volume within the oil slick, both distributions were thus compared by coincidence of the Moderate Resolution Imaging Spectroradiometer (MODIS/Aqua) acquisition, which imaged the oil slick 3 days after the oil spill started. Results showed an excellent agreement in the numerical values of both the expected average and the maximum thickness. In addition, repartition of the oil volume within the slick in the usual thin (sheen) and thick (brown) parts resulted, consistent with the empirical rule of 20% and 80% of the total oil volume, respectively.

Multi-sensor satellite data are used to assess cyanobacteria blooms in the Curonian Lagoon. The exploitation of SAR, in combination with optical data, is investigated to take full advantage from the all-weather, night/day SAR imaging capability. A dataset of images has been analyzed to: 1) study the effect of cyanobacteria on microwave signals; 2) assess the daily evolution of cyanobacteria bloom from multi-sensors data; and 3) evaluate the dependence of dynamics of blooms on winds. The results show a significant correlation (R 2 > 0.8, p

We apply multi-temporal Persistent Scatterer Interferometry (PSI) analysis to investigate slope instability in the Daunia region in the Southern Apennine Mountains. Daunia includes many small hill-top towns affected by landslides and is of particular interest for the Civil Protection – Regione Puglia Authority, one of the end users of the PSI deformation maps. The SPINUA algorithm is used to perform interferometric analysis and detect, with mm precision, the presence of ground surface movements. Consistent results on very slow displacements are obtained using the radar imagery acquired between 2002 and 2010 by the ENVISAT ESA satellite (C-band, medium spatial resolution sensor) and the images acquired between 2010 and 2011 by the X-band high resolution sensor onboard the TerraSAR-X satellite. Thanks to the finer spatial resolution the X-band PSI applications are very promising for monitoring single man-made structures and slope/ground instability in areas where C-band PS density is low.

We report on the InSAR-related results of the National Research Project (PRIN) entitled "Advanced technologies in the assessment and mitigation of the landslide risk: precursors detection, previsional models and thematic mapping", funded by the Italian Ministry for Scientific Research. In the framework of the project, multi-temporal interferometric techniques were applied to time series of SAR data, from legacy ERS and ENVISAT, as well as high-resolution TerraSAR-X sensors. We report on the final outcomes of the project, which concentrate on two sites of the Apulia Region, representative of terrain instability problems widespread in the area. The fist one is the coastal area near the Lesina Marina tourist village, at the north of the Region, close to the Gargano promontory, where the excavation of a canal exposed grey micro- and meso-crystalline gypsum which is now showing a high density of cavities and sinkholes due to gravitational collapse processes. A slow but steady uplift phenomenon has been detected by processing through persistent scatterers interferometry (PSI) methodologies ERS and ENVISAT data, acquired in both ascending and descending geometries, and spanning a total time interval from 1995 to 2010. The displacement data were validated by comparison with leveling measurements performed in 2000 and 2010. Derived vertical displacement rates exceed 3-4 mm/y on locations adjacent to the canal, gently decreasing towards the western end of the built up area. These observations, supported by ancillary data and in situ investigations performed in the past, seem compatible with processes such as diapirism or the hydration of the residual anhydrite in the core of the gypsum mass. The second site is an inland landslide area close to the municipality of Carlantino, in the Daunia mountains. Here, a relatively large landslide affects the slopes spreading from the town outskirts to the banks of the Occhito lake, an artificial basin formed by a dam on the Fortore river. PS targets detected by both C- and X-band data correspond to urban structures or peri-urban walls and guard rails, while the landslide body is almost completely devoid of stable targets, due to the vegetation cover. In order to allow stability monitoring through spaceborne SAR interferometry, a network of passive reflectors was designed and deployed on the area of interest. To design the corner reflector (CR) network, different factors were taken into account: the visibility of the CR by the satellite in terms of geometry and radiometry, the accessibility of the location on the ground, and the relative distance between CR. Results of the comparison of phase data over the CR with that of surrounding objects are presented. Work supported by the Italian Ministry of Research in the framework of PRIN 2008 research grant "Advanced technologies in the assessment and mitigation of the landslide risk: precursors detection, previsional models and thematic mapping". TerraSAR-X data are provided by DLR under TerraSAR-X General AO Project ID MTH0432.

We propose a semi-automatic approach to detect, map and classify rainfall-induced shallow landslides. The approach combines the classification of a post-event multispectral satellite image with information on the morphometric signature of landslides in a Bayesian framework. We apply the approach in two steps. First, we detect and map the rainfall-induced landslides separating the stable ground from the failed areas. Next, we classify internally the landslides separating the source from the run out areas. We obtain the prior prob- ability from the Mahalanobis discriminant function used to classify the satellite image, and the likelihood from the frequency distribution of terrain slope and cross section convexity in the pre-existing shallow land- slides. We tested the approach in southern Taiwan, in a catchment where Typhoon Morakot caused abundant landslides in August 2009. Using the semi-automatic approach, we obtained a detailed event landslide inven- tory map that we compared to an inventory obtained through the visual interpretation of post-event ortho-photographs taken a few days after the landslide triggering rainfall event. Quantitative comparison in a Geographical Information System revealed a degree of matching between the two event inventories ex- ceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topo- graphic data. Best suited in landscapes where shallow landslides leave distinct radiometric and topographic signatures, the approach is expected to facilitate the production of event landslide inventory maps with pos- itive consequences for geomorphological investigations, landslide hazard and risk modeling, and for post event recovery efforts.

Space-borne SAR Differential Interferometry (DInSAR) techniques are attractive for landslide investigations because of their capability to provide regional scale coverage and, under favourable conditions, spatially dense information on small ground surface deformations. In particular, advanced multi-temporal InSAR techniques such as Persistent Scatterer Interferometry (PSI) allow detecting and monitoring, with millimetre precision, displacements occurring on selected radar targets (PS) exhibiting coherent radar backscattering properties. PS targets correspond mainly to man-made structures or to rock outcrops, and their spatial density depends on the ground coverage, and it is maximum over urban areas. The application of multi-temporal InSAR analysis to slope instability monitoring poses challenges related to the complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. This is the case of the Daunia region, located in the Southern Italian Apennine Mountains, which is characterised by scarce urbanisation (mainly small hill-top towns) and dense vegetation cover. The SPINUA (Stable Point INterferometry over Un-urbanised Areas) PSI multi-temporal processing technique was used in the past years to detect and measure ground displacements over this region. Both C-band medium resolution SAR data from ERS-1/2 and ENVISAT ESA satellites, and X-band high resolution SAR data from the TerraSAR-X (TSX) satellite were used. Results indicate that PSI can be profitably used to investigate slope instability, mainly over the urban and peri-urban areas, and that, on these sites, TSX data result very promising for monitoring areas where ERS/ENVISAT PS density is too low. Nevertheless, the application of PSI for slope instability monitoring still remain problematic or impossible in rural and mountainous areas. This is the case, for instance, of the Municipality of Carlantino, where PS targets detected by both C- and X-band data correspond to urban structures or peri-urban walls and guard rails, while a large landslide, extending for about 2 km from the hilltop down to the valley, is lacking stable coherent targets, due to the vegetation cover. In order to allow stability monitoring through spaceborne SAR interferometry, a network of passive reflectors was designed and deployed on the area of interest. The Corner Reflectors (CR) were designed for TerraSAR-X stripmap acquisitions, and consist of three triangular metal panels welded perpendicular to each others to form a trihedral shape which ensures that the radar signal is scattered back to the sensor. A small size is preferred to minimize the curvature of the side panels, the effect of wind, the exposition to vandalism, and to allow easier transportation and deployment in the harsh terrain setting. To design the CR network, different factors were taken into account: the visibility of the CR by the satellite in terms of geometry and radiometry, the accessibility of the location on the ground, and the relative distance between CR. The work discusses the problematic aspects of designing and deployment of the CR network and presents preliminary results.

Applications such as SAR interferometry [1] are increasingly used in "sparse" contexts, in which information about some geophysical parameters (e.g. millimetric terrain deformations) are only available over some of the imaged pixels, corresponding to stable objects [2]. In such cases, it is often necessary to adapt processing algorithms, developed and optimized for regular data grids, to work on sparse samples. One of such algorithms, at the basis of several InSAR processing chains, is the so-called phase unwrapping (PU), consisting of obtaining absolute phase values (i.e. defined over the whole real interval) from the corresponding principal values, i.e. limited to the interval [??, ?[. Recently, a method to reduce the unwrapping problem of a sparse-grid field to one corresponding to a regular grid, has been proposed [3], based on a preliminary nearest-neighbor interpolation step. The solution to the sparse problem is shown to be mathematically equivalent to that of a corresponding regular grid problem, properly derived from the former. The approach allows to employ existing algorithms for regular-grid PU, such as those based on network theory (e.g. the so-called Minimum Cost Flow, or MCF). In this work, stemming from an analysis of the above-mentioned methodology, giving as a solution an absolute phase significant only over the sampled pixels, we propose an alternate procedure, in which the principal phase interpolation step is based on algorithms more advanced than the simple nearest-neighbor scheme. Such interpolation can be performed over the unit-magnitude complex field obtained from the wrapped phase. In this way, the obtained wrapped phase field results more similar to the original, "physical" regular field from which the sparse samples have been obtained. In the case in which this latter field can be assumed to satisfy general conditions of smoothness and homogeneity [4], this allows to exploit at best such characteristics, and to have finally an absolute phase regular matrix more representative of the real data, and then more effective to use in the subsequent processing steps [5]. In the paper, several interpolators are considered, such as radial basis functions (RBF), as well as, more generally, Kriging [6], and their performances and application limits are evaluated in simulation, as a function of both the regularity conditions of the original sampled surface, and the sampling density. References [1]R. F. Hanssen, Radar Interferometry: Data Interpretation and Error Analysis, 308 pp., Springer, New York, 2001. [2]A. Ferretti, C. Prati, and F. Rocca, "Permanent scatterers in SAR interferometry", IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 1, pp. 8-20, Jan. 2001. [3]P. S. Agram, H. A. Zebker, "Sparse Two-Dimensional Phase Unwrapping Using Regular-Grid Methods", IEEE Geoscience and Remote Sensing Letters, 6(2), 327-331, 2009. doi:10.1109/LGRS.2009.2012445 [4]A. Refice, A. Belmonte, F. Bovenga, G. Pasquariello, "On the Use of Anisotropic Covariance Models in Estimating Atmospheric DInSAR Contributions", IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 2, pp. 341-345, March 2011 [5]F. Bovenga, A. Refice, R. Nutricato, L. Guerriero, and M. T. Chiaradia, "SPINUA: a flexible processing chain for ERS/ENVISAT long term interferometry," in Proc. 2004 Envisat & ERS Symposium, Salzburg, Austria, 6-10 September, 2004. [6]N. A. C. Cressie, Statistics for Spatial Data (rev. ed.), John Wiley and Sons, Inc., New York, 1993.

The application of multi-temporal differential SAR interferometric analysis to slope instability monitoring poses challenges related to the complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. The use of passive reflectors allows to extend the stability monitoring on areas lacking natural coherent reflectors. The present work discusses the problematic aspects of design and deployment of a corner reflector network, and presents preliminary results obtained on the Carlantino village in the Daunia Apennines (Italy) by using X-band TerraSAR-X data.

In the Northern part of the Apulia Region, between the Lesina Lagoon and the mouth of the Fortore River, is placed the Pietre Nere (black stones) Point, a very interesting site and the only outcrop of magmatic rocks in the Puglia region and in the whole Adriatic coast of Italy. The excavation of a canal through this area exposed grey micro- and meso-crystalline gypsum with intercalations of black limestones and marls of Upper Triassic age, mantled by loose sandy Quaternary deposits. The gypsum bedrock shows a high density of cavities, either dissolutional conduits or voids related to gravitational collapse processes. Starting from about 1970, a wide new touristic settlement has been built over the area, which in turn, starting from about 1990, began to suffer for the increasing formation of sinkholes. Several interpretations have been proposed to explain the outcrops of Triassic evaporites that occur in the Pietre Nere Point area, previously buried by a sequence several kilometres thick of Mesozoic rocks: diapirism; pushing upwards by compressional tectonics; both halokinesis and tectonic deformation. The presence of the sinkholes and of the karst reactivation leads to the demand of a monitoring system, for the detection of vertical displacements over a large area. Thanks to the ability of radar systems to operate in all weather conditions, day or night, and the possibility of accurately measuring small surface deformations (changes in altitude of a few millimeters), SAR (synthetic aperture radar) differential interferometry (DInSAR) is an ideal technique for detecting and monitoring ground deformation phenomena (subsidence, faults, landslides, etc.) over vast areas. As an evolution of DInSAR, persistent scatterers interferometry (PSI) [1] allows to follow millimetric movements of stable objects (mainly building and man-made features) present on the Earth surface through time, studying the interferometric response of such objects along series of SAR acquisitions. We used data from the ASAR sensor onboard the European Space Agency's ENVISAT satellite, from both ascending (34 acquisitions) and descending (28 acquisitions) geometry, covering a total time interval from May 2003 to December 2009. Data were processed with a combination of open-source and in-house developed software [2] in order to extract relevant information about mean velocities of stable points located on the Lesina Marina area. Relying on the relatively smooth nature of the investigated phenomenon, and assuming negligible north-south movements, as justified by the overall geometry of the site geomorphological units, information coming from ascending and descending geometries was interpolated over a common georeferenced grid, then combined to obtain vertical and horizontal (east-west) velocity components. We observe PS objects undergoing uplift displacements in both ascending and descending data, with uplift rates decreasing in a roughly W-SW direction from the Acquarotta canal. Derived vertical displacement rates exceed 4 mm/y on locations adjacent to the canal, decreasing to zero towards the western end of the built up area (about 600 m width). The east-west velocity component shows an apparent W-SW trend with maximum rates of about 2 mm/y over a sector about 200 m thick, starting at about 200 m from the canal. These data are consistent with other PSI data coming from the European ERS sensor and spanning the interval July 1992 - December 2000. High precision elevation measurements have been performed on the same topographic points, which are distributed in the study area, in 1999 and 2010. These measurements confirm the DInSAR data. These surprising results are not of easy interpretation, because the whole area affected by the vertical displacements has been also interested in the past by important geological phenomena, which could be not completely expired at present, like diapirism. Another possible explanation of the phenomenon is the hydratation of the residual anhydrite, in the core of the gypsum mass. In the present work, we analyze the potential effects of these processes, trying to test them against the PSI-derived displacements, and thus to assess on a quantitative basis the observed phenomena. References [1] Ferretti A., Prati C. & Rocca F. (2001), "Permanent Scatterers in SAR Interferometry", IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 1, pp. 8-20. [2] Bovenga F. et al. (2004), "SPINUA: a flexible processing chain for ERS / ENVISAT long term interferometry", Proceedings of ESA-ENVISAT Symposium, Salzburg, Austria, 6-10 September 2004.

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.

Understanding the temporal dynamic of the biophysical properties of the coastal/shallow waters is a challenging and important task. In the present work Chlorophyll-a concentration in coastal zone shallow waters is obtained by inversion of the MERIS and MODIS level 1 calibrated, geo-located reflectance data. For this purpose, we have used both sensors 1:0 retrieve the chlorophyll concentration, in a scheme of multisensor data assimilation. In fact, the new generation sensor MERIS, on ESA's ENVISAT, with its spatial resolution of 300 meters and 3 days revisitation time, offers a high number of narrow spectral bands in the visible and in the IR. At the same time, MODIS, on NASA's Terra and Aqua Satellites, has a lower space resolution but an higher number of bands in the IR portion of the electromagnetic spectrum and can improve our atmospheric correction algorithms. Due to the high variability of the aerosol (type and concentration) and the water type, an accurate atmospheric correction method based on the 6S radiative transfer code has been performed. The atmospheric data required as input to the 6S code have been retrieved from the MODIS data. The estimated chlorophyll-a concentration has been computed from MERIS and MODIS data using a semi-analytic model, based on the optical properties of water and its components. To validate the inversion procedure, ground measurements have been collected monthly during the year 2003 and have been compared with the values derived from the inversion model.

The objective of this paper is to investigate and compare two model-based methods for the soil moisture retrieval from SAR data. The overall accuracy of model-based methods in estimating geophysical parameters mostly depends both on the performances of the exploited direct model and on the intrinsic ambiguity of SAR data. The ambiguity is due to the complexity of the relationship between the geophysical parameters, such as soil moisture and soil roughness, and the backscattering values that makes 'ill posed' the inverse problem of the parameter retrieval. Moreover, the accuracy of soil moisture estimates depends also on the retrieval algorithm and on its robustness to the noise. In this study, the methods under investigation perform a probabilistic estimation of the parameters, finding solutions representative of an unknown distribution such as the mean or the most probable solutions. The model-based methods considered are a Neural Network algorithm, to explicit invert the direct model, and a Mixture Model algorithm, to approximate the parameter distribution function. The theoretical direct model adopted to tune the inversion algorithms is the Integral Equation Method (IEM) model. A comparison of the characteristics of the two algorithms is shown, and same evaluations of the accuracy in predicting soil moisture content from SAR data are performed. In particular, evaluations refers to ERS and ENVISAT ASAR data simulated by means of the IEM model.

By means of a backpropagation neural network a model has been built which is able to distinguish between noises and tones endowed with a detectable pitch in a (computer simulated) random acoustic environment where the information carried by the signals is compressed to its essential part by the reduction of the Fourier transform into templates of 12 numbers used as inputs. It is found that a neural network able to detect a pitch is also able to recognize the presence of a residue pitch in the signals of complex tones where the first (or the first two) harmonic has been subtracted. Finally, the correlations between the concept of consonance and the presence of a detectable pitch in the superposition of pairs of complex tones are briefly investigated.

Measurement of the asymmetry factor in D(γ, np) is reported and the result is discussed in the framework of Partovi’s theory.

The asymmetries ..sigma..(theta/sub c.m./ = ..pi../2) for the /sup 2/H(..gamma..,n)p reaction have been measured with use of the monochromatic and linearly polarized ..gamma..-ray beam (LADON), obtained by backward Compton scattering of laser light against high-energy electrons. Contributions from meson exchange currents must be included in the theoretical calculations to reproduce our data.

We apply persistent scatterer interferometry (PSI) techniques to synthetic aperture radar (SAR) data from ERS and ENVISAT satellites on the Lesina Marina area, a coastal tourist village in Apulia, Southern Italy, where the excavation of a canal exposed grey micro- and meso-crystalline gypsum which is now showing a high density of cavities and sinkholes due to gravitational collapse processes. We observe PS objects undergoing displacements, along the sensor line of sight, forming the same relatively smooth pattern in all the processed data stacks. Vertical displacement rates, derived through integration of ascending and descending geometries, reach about 4 mm/year on locations adjacent to the canal, gently decreasing towards the western end of the built-up area . High-precision leveling measurements, performed in 1999 and 2010, reveal a substantial agreement with the ENVISAT PSI data, taking into account a small bias due to the choice of the leveling reference point. The dataset, thus validated, suggests the presence of an uplift phenomenon going on steadily for the entire timespan covered by the SAR observations (1992–2009). These observations, supported by petrographic data and in situ investigations, seem only in part compatible with a residual diapirism, and hint instead to more complex processes, such as a combination of diapirism and the hydration of the residual anhydrite in the core of the gypsum mass. These results confirm the importance of the integration between in situ, geologic and geophysical, remotely sensed investigations, as the latter often represent an essential tool to infer whether a given phenomenon, which can be hypothesized by the former, is presently under development.