Several companies are developing massive satellite constellations aimed to deliver global broadband internet and provide in the near future benefits and opportunities. These opportunity signals also provide additional capabilities if exploited for remotes sensing purposes. The present study shows first results of an Open Space Innovation idea supported by the European Space Agency (ESA), where the concept of microwave reflectometry using signals of opportunity from the OneWeb satellites constellation will be used for wind speed measurements over the sea surface. This big constellation of satellites operate in the Ku band, which is well suited for detecting sea surface geophysical parameters as well as soil soil moisture. This work focuses on the simulation of the normalized bistatic radar cross section scattered from the sea surface using a full polarimetric approach in first-order small slope approximation.

The main problem related to Persistent Scatterer (PS) interferometry is the lack of large point clouds in rural areas, although it has proven to be a powerful tool in urban scenarios, especially in monitoring buildings with possible slow temporal deformations. The identification of PSs in low Signal to Noise Ratio (SNR) areas is crucial and can be done using a multiple hypothesis test to detect the possible presence of multiple scatterers [1]. In this paper, we frame this problem by exploiting the Kullback-Leibler Information Criterion (KLIC), developed in [2], to address the design of one-stage adaptive sensing architectures for multiple hypothesis testing problems in PS interferometry. Theoretical analysis shows the equivalence between the algorithm developed in [1] and [2] for a single scatterer. In this context we use the scheme of multiple hypothesis, provided in [2], for both the formalization of rural PS detection problem and its solution.

Phase altimetry has been recently introduced for high precision surface height measurements in satellite global navigation satellite system reflectometry. In the presence of coherent scattering the effectiveness of the technique has been demonstrated for sea surface at low grazing angle as well as for rivers and lakes. This study develops the concept of differential phase altimetry for land surfaces, provided that the surface roughness is small and the surface undulations are gentle. Differential phase altimetry is more tolerant with respect to atmospheric and systematic errors but its applicability is difficult in the presence of surface slope variations that may generate phase cycle slips that require complex post processing to unwrap the retrieved phase. To mitigate cycle slips an overlapping correlation process is introduced to generate phase values with high spatial resolution. A first evaluation of performance is presented using the CYclone Global Navigation Satellite System raw intermediate frequency acquisitions over land surface with gentle undulations.

In remote sensing, target detection in hyperspectral systems is a crucial duty since it enables the localization and discrimination of target features. For this purpose, reflectance spectra are frequently utilized, and the spectral signatures with corresponding component abundances in the observed scene are displayed. Nevertheless, many hyperspectral sensors have restricted spatial resolution, namely only a part of the pixel is occupied by the targets, and the spectra of multiple sub-pixel targets, along with the background spectrum, gets combined within a single pixel. Therefore, we propose in this paper a generalized replacement model that considers different sub-pixel target spectra and execute the detection process as a binary hypothesis test. This method shows to work well in handling this problem.

One of the problems associated with electromagnetic imaging is that the interaction of photons with targets occurs only on part of their surface, namely those exposed to the transmitted energy rays. Imaging of deep localized objects is very hard especially in the presence of short electromgnetic wavelengths. In this paper we propose a new method for through wall imaging, based on photons and sound waves analysis. The technique investigates Doppler analysis in terms of estimating vibrations generated on infrastructures. The proposed method estimates target's vibration energy in order to perform tomographic imaging of man-made objects, such as buildings. Unlike traditional imaging, this technique allows for through wall imaging. The experimental results are distributed over one case study, where we show the to-mographic imaging of a reinforced concrete infrastructure. We consider this preliminary work very promising for future applications performed from the processing of satellite synthetic aperture radar images.

In this letter, a new approach for the retrieval of the vertical column concentrations of trace gases from hyperspectral satellite observations is proposed. The main idea is to perform a linear spectral unmixing by estimating the abundances of trace gases’ spectral signatures in each mixed pixel collected by an imaging spectrometer in the ultraviolet region. To this aim, the sparse nature of the measurements is brought to light and the compressive sensing paradigm is applied to estimate the concentrations of the gases’ endmembers given by ana prioriwide spectral library, including reference cross sections measured at different temperatures and pressures at the same time. The proposed approach has been experimentally assessed using both simulated and real hyperspectral datasets. Specifically, the experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data collected by the TROPOspheric Monitoring Instrument. To validate the procedure, we also compare the obtained results with the sulfur dioxide total column product based on the differential optical absorption spectroscopy technique and the retrieved concentrations estimated using the blind source separation.

This letter deals with the problem of clutter edge detection and localization in training data. To this end, the problem is formulated as a binary hypothesis test assuming that the ranks of the clutter covariance matrix are known, and adaptive architectures are designed based on the generalized likelihood ratio test to decide whether the training data within a sliding window contains a homogeneous set or two heterogeneous subsets. In the design stage, we utilize four different covariance matrix structures (i.e., Hermitian, persymmetric, symmetric, and centrosymmetric) to exploit the a priori information. Then, for the case of unknown ranks, the architectures are extended by devising a preliminary estimation stage resorting to the model order selection rules. Numerical examples based on both synthetic and real data highlight that the proposed solutions possess superior detection and localization performance with respect to the competitors that do not use any a priori information.

In this paper, the problem of classifying radar clutter returns into statistically homogeneous subsets is addressed. To this end, latent variables, which represent the classes to which the tested range cells belong, in conjunction with the expectation- maximization method are jointly exploited to devise the classification architecture. Moreover, two different models for the structure of the clutter covariance matrix are considered. At the analysis stage, numerical examples based on simulated data for the classification performance are presented showing the effectiveness of the proposed classification schemes.

This study aims at estimating the Earth deformations due to the nuclear test carried out by North Korea on the 3rdof September 2017 by processing a time series of synthetic aperture radar images acquired by the COSMO-SkyMed satellite constellation. For active satellite sensors working in the X-band, phase information can be unreliable if scenarios with dense vegetation are observed. This uncertainty makes difficult to correctly estimate both the interferometric fringes and the information phase delay generated by the variation in the space-time domain of the atmospheric parameters. To this end, in our research we apply the Sub-Pixel Offset Tracking technique, so that the displacement information is extrapolated during the coregistration process. The results reveal an accurate estimate of the spatial displacement of similar pixels due to the nuclear explosion. The work also reveals a hypothetical underground tunnel network.

This letter addresses the detection problem of dim maneuvering targets in the presence of range cell migration. Specifically, it is assumed that the moving target can appear in more than one range cell within the transmitted pulse train. Then, the Bayesian information criterion and the generalized likelihood ratio test design procedure are jointly exploited to come up with six adaptive decision schemes capable of estimating the range indices related to the target migration. The computational complexity of the proposed detectors is also studied and suitably reduced. Simulation results show the effectiveness of the newly proposed solutions also for a limited set of training data and in comparison with suitable counterparts.

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference-plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.

This letter addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the “position” of the target within the CPI and eventually detect it. The performance analysis shows that the newly proposed architectures can provide an accurate estimate of the target position along with improved detection performance with respect to existing competitors.

Recent studies have demonstrated that Global Navigation Satellite System-Reflectometry (GNSS-R) can be used for global wind-speed measurements over the ocean. Delay-Doppler Maps (DDM) are produced continuously by CYGNSS observatories are currently used for wind speed retrievals either directly or after calculation of global observables. The work presented in this paper shows that fine results can be obtained using the scattered power function observable. Experiments are based on real DDMs collected by the space CYGNSS observatories and by ground truth data processed within the collaborative NASA CYGNSS science team.

This paper deals with ground displacement measurements with Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. These ground modifications often occur as a consequence of an earthquake. The island of Ischia (Southern Italy) has been chosen as case study since it was hit by a severe earthquake on the 21stof August 2017. The National Institute of Geophysics and Volcanology (INGV) and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) provided the displacement maps considering interferometric pairs close to the main shock event. In this work, a further interferometric pair, which also includes some aftershocks, has been used to calculate ground modifications. The results confirmed a ground subsidence up to 4 centimeters in the epicenter area, in agreement with the reference data. This study rely on open access data as well as on open software, both provided by the European Space Agency (ESA) under the Copernicus program. In particular, data from Sentinel-1 radar satellite mission and SNAP software have been used. The availability of such data and software is relevant for public institutions that can produce valuable information at almost no charge.

The main purpose of this study is to propose and validate a new algorithm for wind speed retrieval over the ocean using Global Navigation Satellite System-Reflectometry (GNSS-R). The proposed method is based on a Least Squares (LS) estimation from the volume of the scattered power function. Real DDMs collected by the space GNSS receiver-remote sensing instrument (SGR-ReSI) on board the TechDemoSat-1 (TDS-1) satellite and the Advanced SCATterometer (ASCAT) wind speed measurements are used for validation.

In this paper the Two-Scale Model has been used to derive the theoretical Normalized Radar Cross Section (NRCS) for sea clutter in the L-band, taking into account also the circular polarization of GPS signals. Using this theoretical model and the theoretical formula for NRCS, authors aim to investigate the possibility to extend target detection through GPS signals in backscattering configuration by varying the incidence angle and the wind speed, whereas results obtained previously were related only to a single value of the incidence angle and to two values of the wind speed. Target Scattered Power and Sea Clutter Power are derived and compared finding that: 1) smaller targets can be detected as the incidence angle increases; 2) the Sea Clutter Power is lower than the one estimated when the “worst case” of a VV-polarization signal is chosen to derive the NRCS value from experimental data. Some final considerations are made as future work.

Authors in this work aim to present new analysis methods for Earth Observation, developed by processing Sentinel-1 and Landsat-8 satellite data and combining them in an original way. Comparing SAR and Optical/Multispectral data is a procedure already in use because they are two acquisition systems that provide very different and therefore complementary and useful information. Even if the combination of such different data is not a simple process, the overall information greatly improves when both satellite data are jointly used, as application of our procedure to some case studies demonstrates.

A stochastic model for delay-Doppler map (DDM) simulation from global navigation satellite system reflectometry (GNSS-R) systems is presented. The aim is to provide a useful tool for investigating the performance of estimation and retrieval algorithms that are based on finite time series. The scattering inside a delay-Doppler cell is modeled as the sum of a random number of contributions from inner specular points that, as the mean number of such contributions gets larger, tends to a compound-Gaussian process. The statistical averages reveal that the model is fully consistent with the previous results provided by Zavorotny and Voronovich. Numerical simulations of large airborne and spaceborne DDMs are easily practicable and show the clear patterns due to signal fluctuations and thermal noise that fade away when the number of averaged observations increases. Comparisons with TechDemoSat-1 data show that the model and the simulation scheme provide accurate realizations of the onboard-processed DDMs.

A new approach for simulation of delay-Doppler maps for ocean global navigation satellite system reflectometry is presented. The simulator is based on a stochastic forward scattering model for generation of realizations of the sea surface reflected signal and allows a flexible definition of system and environmental parameters. The signal scattered from a delay Doppler cell is modelled as the coherent random sum of a large number of components from specular points; the sum converges to a compound-Gaussian random variable when the mean number of points is sufficiently large. In the proposed simulation process, the discretization of the DDM arises from a natural sampling of the delay-Doppler domain rather than on sampling the ocean surface. Instantaneous DDMs are simulated and consistency with the theoretical model is verified.

In this work, authors outline how measurements from different types of sensors can be put together to characterize in an extensive way a sensitive waste dump site. Terrestrial surveying systems and aerial images have been combined with satellite remote sensing techniques to get a complete overview of the surface stability and its thermal characteristics in conjunction with geomorphological conditions and water distribution. The objective has been to implement a broad monitoring system to control the area under observation. Interesting observations have been deducted from the comparison among different data since surface temperature retrievals and variations can be correlated to soil moisture and water exchange by showing that the proposed monitoring system performs correctly in taking under control deformation, terrain displacements, temperature and water surface modifications.

The Nadir reflectances, measured by the SCIAMACHY hyperspectral sensor, are used to retrieve vertical column concentrations of nitrogen dioxide (NO2), resulting from anthropogenic pollution. The estimation process is realized via a blind source separation method: the unmixing of the NO2spectral waveform from the overall atmospheric absorption contribution within the logarithmic reflectance spectra is realized.

Atmospheric concentrations of trace-gases are retrieved from hyperspectral data using a blind source separation method. The algorithm relies on the assumption that the absorption cross sections of the gas components are weakly dependent on the overall atmospheric background. The unmixing of contributions from the logarithm of the spectral reflectance provides estimates of both individual trace-gas absorption cross sections and their concentrations. In the experimental analysis, nadir reflectances received by SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY are considered in two scenarios: the sulfur dioxide emissions from a volcanic eruption and the nitrogen dioxide production from anthropogenic pollution. In both cases, it is demonstrated that the algorithm performs very similarly to the Differential Optical Absorption Spectroscopy algorithm but with very little ancillary information.

We propose a new stochastic model governing bistatic returns from GNSS-R systems. The main mechanism exploited in the model is the scattering from specular points and the fluctuations of their number. The scattering inside a delay-Doppler cell is modelled as the sum of a random number of contribution from specular points that, when the number of such contribution is large, converges to a compound-Gaussian process. This process is simple to simulate and is able to model both the spatial and temporal dynamics of the sea surface. After simulation of the waveform received by the receiver, averaging over long time sequences produces results that fully resembles the theoretical results predicted by the Zavorotny and Voronovich model [1].

A new approach for the retrieval of the vertical column concentrations of trace gases, from hyperspectral satellite reflectances, is presented. The investigation moves from the general rationale of independent component analysis, but the constraint of perfect independence among sources is replaced by a minimum dependence concept that proves more reasonable for the application at hand. The unmixing of the gas spectra and their concentrations is achieved from linear mixtures obtained from the logarithm of the spectral reflectance. After a proper preprocessing stage aimed at reducing major residual dependences caused by atmospheric scattering, trace-gas retrieval is carried out through a minimization of a statistical cost function, subject to the physical constraint that the resulting spectra must be nonnegative. The experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data from the National Aeronautics and Space Administration Ozone Monitoring Instrument. To validate the procedure, reference reflectance spectra having a known profile of sulfur dioxide are generated with the MODerate resolution atmospheric TRANsmission software, and the retrieved concentration is compared with the theoretical one. Performance in the presence of shot and detector noise has also been analyzed starting from pure simulated spectral reflectances.

A new approach is proposed for the retrieval of trace gases, vertical column concentrations, from hyperspectral satellite observations. This technique consists of a semi-blind recovery of concentrations and pure spectra from their linear mixtures. This decomposition is based on the Least Dependent component Analysis (LDA) technique, a method that generalizes the well known Independent Component Analysis, to find least dependent components, after a proper preprocessing for reducing some residual dependencies. Some results are shown for the retrieval of sulphur dioxide SO2volcano emission using data from the NASA Ozone Monitoring Instrument (OMI).

We propose a new method for trace gases concentration using satellite measurements. The main idea is to use the full retrieved waveform through a blind source separation algorithm, instead of processing differential observation taken from a few absorption lines. The main assumption in this work is that the spectral waveforms from atmosphere contributions can be considered statistically independent. Results show that the algorithm is able to operate quite good although some unwanted features from clouds are clearly observed.