A. Fusco

A new approach for analyzing the temporal evolution of Earth surface deformations based on the combination of DIFSAR interferograms

We present a new approach for the evaluation of the Earth surface deformation evolution based on the combination of several differential interferograms spanning a time interval of interest. In particular we present a method that extends the least squares combination technique presented by S. Usai et al. (2000) by applying the singular value decomposition (SVD) method. Experiments carried out on ERS data validate the proposed approach.

Fitting a statistical model to SIR-C SAR images of the sea surface

A suite of statistical procedures aimed at assessing to what extent polarimetric and/or multifrequency synthetic aperture radar (SAR) images of the sea surface can be modeled in terms of spherically invariant random vectors and matrices (SIRVs and SIRMs) is presented. The proposed tests assume that images can be described by resorting to the compound-Gaussian model, but do not require any a priori knowledge about the actual first-order probability density function (pdf) of the texture. The tests have also been used to analyze three data sets from STR-C/X-SAR missions.

Sentinel-1 results: SBAS-DInSAR processing chain developments and land subsidence analysis

This work is aimed at describing the development of an efficient interferometric processing chain, based on the well-known advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) algorithm referred to as Small BAseline Subset (SBAS) technique, for the generation of Sentinel-1A (S1-A) Interferometric Wide Swath (IWS) deformation time-series. Due to the TOPS mode characterizing the IWS acquisitions, the existing SBAS processing chains was properly adapted with new procedures for efficiently handling the S1-A data. The developed SBAS-DInSAR chain has been tested on both S1-A and TOPS RadarSAT-2 interferometric dataset, clearly demonstrating the capability of the developed SBAS-DInSAR processing chain to effectively investigate land subsidence phenomena affecting large areas.

Unsupervised parallel SBAS-DInSAR chain for massive and systematic Sentinel-1 data processing

In this work we present an efficient interferometric processing chain, based on the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-1A (S1A) Interferometric Wide Swath deformation time-series, which is able to exploit distributed computing architectures. The presented S1A P-SBAS processing chain has been successfully implemented within the ESA Geohazard Exploitation Platform to provide an on-demand automatic service for the unsupervised generation of P-SBAS displacement time-series. To give an idea of the effectiveness of the presented S1A processing chain, as a preliminary result we show a 12-days interferometric analysis at continental scale, carried out by exploiting 150 S1A interferometric pairs acquired over Europe for an overall covered area of about 7,500,000 km2.

Stochastic modelling of atmospheric effects in SAR differential interferometry

A stochastic model for the atmospheric disturbance in DInSAR images is presented. Using a ray-propagation approach, the random path between two points in a turbulent atmosphere is modeled as a three dimensional random walk. First and second-order probability density functions of the differential phase of the atmospheric disturbance are derived and the model is experimentally validated by comparison between the characteristic function of real data and that predicted from the theoretical model.

Wind map retrieval from SAR data for offshore wind turbines positioning

The feasibility study for the selection of the optimal site for offshore wind farms is usually made by in situ measurements. This methodology suffers from point information that is not sufficient for this purpose, because it is needed a dense spatial wind information. Remote sensing overcomes this problem by using the high capability to cover periodically wide areas in one acquisition. From this point of view satellite synthetic aperture radar (SAR) wind mapping can be a useful tool in selecting optimal sites and may therefore increase the cost-effectiveness of planning wind farms. In this paper an overview on the physical backscattering mechanisms that models the wind signatures in SAR images is illustrated. Several strategies to derive wind map are described and some results using CMOD algorithm are shown.

Sentinel-1 data exploitation for automatic surface deformation time-series generation through the SBAS-DInSAR parallel processing chain

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

Random Walk Approach for Wave Propagation through Atmospheric Layers for DInSAR Applications

The accuracy and reliability of the measured differential path in satellite synthetic aperture radar (SAR) interferometry are strongly affected by the uncertainty in the estimation of the atmospheric contribution. Changes in the physical parameters of the medium, due to turbulence layers or gas concentrations, induce slight variations in the curvature of the propagation path that finally generate an overall disturbance term that is usually comparable, or even larger, than the displacement to be observed. A stochastic model for the three dimensional path field is derived by considering a plane wave propagating in a random layered medium. In the vertical direction a piecewise-linear walk, made by straight ray subpaths, is assumed, wherein the length of each path (thickness of the layer) and the number of paths are modelled as random variables. Along the horizontal plane mutual interactions among cells are defined through an interaction equation that closely resembles typical competitions in biological evolution models. The resulting parametric model is fitted with observations from the residual atmosphere, as measured after topographic removal in SAR images, and results are shown.

The role of spatial interactions for prediction of the spectral structure of the atmospheric phase screen

The atmospheric phase screen is one of the main error sources that affect the precise phase measurements in many fields of earth remote sensing. The atmospheric effects can be mitigated if a precise knowledge of the power spectral density of the process is available and if same sample observations can be retrieved on a sparse grid. At smaller scales, where interactions are no longer isotropic, the behaviour is not easily predicted by the ultimate dissipative behaviour of turbulence eddies. We start by assuming that the propagation of the electromagnetic wave in the lower atmosphere can be represented by a ray travelling in a layered medium where the refractive index is constant along each layer. In a turbulent atmosphere, the interaction among eddies induces a diffusion process that propagates with different rates in both vertical and horizontal direction with the final effect of ruling the number of effective layers in the atmosphere. In this way, the overall path travelled by the electromagnetic wave is governed by the accumulated number of such effective layers whose interactions play a primary role in our model. A good model for the interactions among different layers is the linear interaction model. The power spectrum of the process can be found by solving a differential equation with given initial conditions. It can be demonstrated that an asymptotic power law decay is found under binomial competitive interactions and that, at a smaller scale, the behaviour observed in the observed data is naturally induced by the interaction process itself. The model predictions have been tested using samples of the atmospheric phase screen extracted from Synthetic Aperture Radar interferograms. To this end, the model parameters have been estimated from the data set and the predicted spectrum has been compared with the measured one.