High-resolution flood monitoring can be achieved relying on multi-temporal analysis of remote sensing SAR data, through the implementation of semi-automated systems. Exploiting a Bayesian inference framework, conditioned probabilities can be estimated for the presence of floodwater at each image location and each acquisition date. We developed a procedure for efficient monitoring of floodwaters from SAR data cubes, which adopts a statistical modelling framework for SAR backscatter time series over normally unflooded areas based on Gaussian processes (GPs), in order to highlight flood events as outliers, causing abrupt variations in the trends. We found that non-parametric time series modelling improves the performances of Bayesian probabilistic inference with respect to state-of-the-art methodologies using, e.g., parametric fits based on periodic functions, by both reducing false detections and increasing true positives. Our approach also exploits ancillary data derived from a digital elevation model, including slopes, normalized heights above nearest drainage (HAND), and SAR imaging parameters such as shadow and layover conditions. It is here tested over an area that includes the so-called Metaponto Coastal Plain (MCP), in the Basilicata region (southern Italy), which is recurrently subject to floods. We illustrate the ability of our system to detect known (although not ground-truthed) and smaller, undocumented inundation events over large areas, and propose some consideration about its prospective use for contexts affected by similar events, over various land cover scenarios and climatic settings.

This contribution proposes a time-depth conversion of processed GPR data accounting for sharp discontinuities visible in the data, if any. Specifically, the focus is on the presence of buried cavities. Once that the presence of a cavity is deduced from the collected time-domain GPR data, the proposed time-depth conversion strategy allows a realistic final GPR image by accounting for different propagation velocities in the cavity and in the surrounding soil.

Radar imaging algorithms generally exploit linear models of the electromagnetic scattering phenomenon. This assumption leads to qualitative and computationally effective data inversion schemes, which only account for direct scattering from targets, whereas multipath signal contributions are neglected. As a result, multipath ghosts, i.e., false targets reconstructed at positions where no real target exists, affect the radar images, thus preventing a reliable interpretation of the observed scene. This article proposes a fully data-driven deep learning (DL) approach based on a convolutional neural network (CNN) and microwave tomography to face this challenge. The approach achieves multipath ghost suppression for the case of small targets in terms of probing wavelength. In the proposed training scheme, the tomographic image affected by ghosts represents the input of the network while a ghost-free reconstruction is the output. Numerical simulations addressing the detection of metallic rebars via ground penetrating radar (GPR) are presented. As shown, the proposed ghost removal strategy is effective and robust to variations of the scenario parameters on which the network is trained. Finally, an experimental validation shows the effectiveness of the proposed strategy even in operative conditions.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

Il progetto BRIDGES intende sperimentare, sul caso di studio della salute del suolo, un modo innovativo di fare ricerca, orientato alla transdisciplinarita?, necessaria per affrontare efficacemente le sfide poste dai problemi socio-ecologici complessi. Conseguentemente, la dimensione legata allo scambio di competenze ed esperienze all'interno del gruppo esteso di ricerca e? fondamentale. In questo quadro, un ruolo particolare era stato riservato, nella proposta di progetto, a un'attivita? di tutoraggio in cui i membri senior del team avrebbero dedicato parte del loro impegno nel progetto al mentoring di un giovane ricercatore, su aspetti specifici della ricerca partecipativa e transdisciplinare. In particolare, era previsto che Il piano di tutoraggio si svolgesse in collaborazione tra il CEST e il Partner 2 Pianpicollo, specificamente coinvolti nella formazione dei giovani ricercatori che avrebbero intrapreso il percorso di lavoro di BRIDGES. Secondo la proposta iniziale, il tutoraggio avrebbe incluso una serie di seminari ad alto livello, e in particolare: "(i) seminari di ricerca per giovani, sostenuti dal CEST, sulle narrazioni della fertilita? e della sostenibilita? e sul loro ruolo all'interno del contesto accademico italiano; (ii) due seminari internazionali; uno ospitato dal CNR a Milano, sul ruolo del giovane studioso nel XXI secolo, e uno ospitato dall'OCSE, a Parigi, per discutere con giovani studiosi e decisori politici di temi legati alla sostenibilita?." Tuttavia, lungo il corso del progetto si e? presentata la necessita? di ripensare l'attivita? di mentoring, le persone coinvolte e le caratteristiche del tutoraggio, alla luce della riflessione derivante dall'esperienza di ricerca transdisciplinare in corso. Tale evoluzione e? oggetto di questa relazione.

In this work, we demonstrate a novel mechanism to localize the stimulated Brillouin interaction over a specific portion of an optical fiber, using a Brillouin Optical Frequency-Domain Analysis (BOFDA) interrogation system and an array of tapers. We show, both numerically and experimentally, that the frequency-domain fiber response, acquired over a narrow range by a vector network analyzer, can be uniquely associated to the multi-taper array whose period matches the swept spectral range. This opens the way to high-resolution (cm-scale) dynamic strain measurements, in addition to fully distributed static strain (or temperature) measurements over the same optical fiber and using the same apparatus.

We present the results of an experimental campaign aimed at demonstrating the use of a distributed optical fiber sensor based on Brillouin scattering in static and dynamic temperature measurements at cryogenic temperatures (? 84 K). The experimental results, obtained through Brillouin Optical Frequency-Domain Analysis (BOFDA) at a spatial resolution of 16 mm, are compared with temperature measurements using thermocouples and fiber Bragg gratings. The distributed sensor is able to capture local temperature variations of ? 2 °C at an acquisition rate of 1 Hz.

Dal 16 al 20 luglio 2022, il gruppo di ricerca del progetto BRIDGES si è riunito a Pianpicollo Selvatico con un gruppo di 12 giovani ricercatori e ricercatrici, provenienti da diversi percorsi formativi e professionali. Qui, insieme ad artisti, educatori, specialisti del suolo, agronomi, e altri esperti, i ricercatori hanno partecipato ad alcune giornate di attività teoriche e pratiche incentrate sulla tematica della fertilità del suolo, affrontata nella sua complessità socio-ecologica, con metodi di ricerca-azione e integrando i contributi delle diverse discipline ed esperienze rappresentate. La residenza è rientrata tra le attività di BRIDGES che mirano a sviluppare metodologie relazionali, riflessive e transdisciplinari applicabili nella pratica di ricerca quotidiana e per affrontare questioni socio-ecologiche complesse e controverse. Durante la residenza di ricerca si è entrati nel merito del caso studio della fertilità del suolo attraverso una serie di moduli di ricerca-azione, alternando pratiche di ricerca artistica e scientifica sul campo con momenti di condivisione teorica ed esperienze conviviali. Il gruppo di partecipanti e collaboratori del progetto si è confrontato con l'esplorazione delle relazioni tra soprasuolo e sottosuolo, fitosociologia e microbiologia, microbiota umano e del suolo. Le osservazioni e le misure scientifiche sul microbiota sono coordinate da CNR-IBBA e da esperti di SeaCoop. Gli esperimenti scientifici sono stati intrecciati con esperienze estetiche, a cura del gruppo di ricerca di Pianpicollo Selvatico ETS. Alcuni dei ricercatori coinvolti a Pianpicollo si sono impegnati in seguito, insieme a reti di cittadini, in un'esperienza di ricerca ibrida e partecipata svoltasi nel Settembre del 2022 a Milano, finalizzata a discutere e produrre indicatori di fertilità del suolo costruiti collettivamente, a partire da osservazioni di citizen science e analisi metagenomiche del suolo e sulla base delle indicazioni sviluppate nelle attività esperienziali e riflessive del progetto.

La complessità dell'attuale crisi socio-ecologica globale evidenzia i limiti della scienza specialistica come unica voce in grado di informare le decisioni. Nello scenario contemporaneo, caratterizzato da condizioni di incertezza radicale, da una molteplicità di valori e visioni e da decisioni urgenti, il contratto sociale tra scienza, società e politica è in una fase di profonda trasformazione. Per affrontare questo cambiamento, la comunità scientifica si trova a dover rivisitare il proprio ruolo sociale, creando nuove relazioni non solo con la società ma anche con i sistemi ecologici, per generare delle pratiche di ricerca respons-abile, in grado di produrre conoscenza rilevante. Il workshop del progetto BRIDGES - Building Reflexivity and response-ability Involving Different narratives of knowledGE and Science descritto in questo documento vuole introdurre e sviluppare metodi di ricerca transdisciplinare e partecipata per rafforzare la relazione tra scienza, società e sistemi ecologici nel contesto italiano.

Il presente piano analizza i potenziali rischi connessi alle attività effettuate durante lo svolgimento del Progetto BRIDGES, in modo da sviluppare risposte appropriate, là dove necessario. Il piano è ispirato ai concetti chiave dell'approccio RRI: Responsiveness, Reflexivity e Anticipatory. Per quanto riguarda invece il quality assessment, per ciascun WP sono stati identificati degli indicatori di Performance (quality KPIs) sia qualitativi che quantitativi, di modo da misurare la qualità delle risposte fornite a ciascuna delle domande di ricerca.

L'Action Plan è concepito come un "foglio di lavoro", che elenca in maniera sintetica tutte le attività previste nella proposta BRIDGES e il loro piano operativo di realizzazione. A partire dalla domanda di ricerca e dalle sotto-domande cui le varie attività del progetto intendono "rispondere", viene indicata la loro progressione temporale - quella prevista e quella reale - i partner coinvolti, l'articolazione in task e Deliverable previsti. In una serie di note alla fine di ogni Work Package, vengono menzionate le criticità incontrate durante lo sviluppo del progetto e gli eventuali aggiustamenti, le questioni affrontate o da affrontare in merito allo svolgimento di alcune azioni.

Non-photosynthetic vegetation (NPV) detection and quantification represent a key variable in remote sensing of conservative agriculture, and, more recently, in carbon farming due to its important role in water, nutrient and carbon cycling. For this reason, both mapping and characterization of NPV represent a relevant topic in the exploitation of Earth Observation (EO) data for agriculture monitoring. Studies on NPV mapping by EO data benefit from the availability of hyperspectral data due to the high spectral resolution particularly at wavelengths from 1.6 to 2.3?m, where the spectral features of carbon-based constituents of plants are distinctive. The launch of new generation hyperspectral satellites, as PRISMA (PRecursore IperSpettrale della Missione Applicativa) and, more recently, EnMAP (Environmental Mapping and Analysis Program) offers research opportunities in the field, which before was mainly investigated by proximal and aerial sensing. Early studies already proved the potential of PRISMA in NPV due to the prominence of the cellulose-lignin key absorption feature at 2.1?m. More recent studies on PRISMA make use of machine learning regression algorithm (MLRA) trained on the basis of radiative transfer model simulations, or on the basis of Exponential Gaussian Optimization (EGO) of specific absorption features on sensed data. This second approach, proposed in this study, is aimed at the determination of Crop Residue Cover (CRC) using PRISMA hyperspectral imagery by a two-step approach making use of: i) firstly, an Exponential Gaussian Optimization to model pre-selected absorption features, also reducing the spectral dimension; ii) secondly, a Random Forest paradigm, performing non-linear regression to finally predict and map CRC. This study exploits for the training phase an extensive and well documented spectral library, namely "Reflectance spectra of agricultural field conditions supporting remote sensing evaluation of non-photosynthetic vegetation cover" made available online by USGS (https://doi.org/10.5066/P9XK3867). It consists of 916 in situ surface reflectance spectra collected using a proximal full range spectroradiometer (350 to 2500 nm). Spectra are annotated with the corresponding fractions of NPV, Soil and (if any) Green Vegetation, as estimated by point sampling digital photograph of the radiometer field-of-view. This spectral library was resampled to PRISMA spectral resolution, prior to the Gaussian Exponential Optimization (EGO) on 4 spectral intervals of interest, already tested in previous studies, and corresponding to absorption bands of: cellulose-lignin, plant pigments, vegetation water content and clays. The EGO algorithm optimizes continuum-removed spectra by 4 parameters - absorption band depth, center, width and asymmetry - and since this is performed for each spectral interval, it results in 16 parameters. This is a reduced space as compared to the one of the input spectra (around 230 bands). This parameter space was used to train a Random Forest to model the regression between Crop Residue Cover percentage and EGO parameters, achieving a determination coefficient around 0.8 (RPD ~2.1; MSE ~ 0.02) on the test set. The RF model was firstly validated against an independent spectral library of around 100 spectra, collected during a proximal sensing survey with a portable full range spectroradiometer, conducted in a large farm test site (3800ha) located in Jolanda di Savoia (Italy). Also in this case, spectra are annotated with Crop Residue Cover percentages, and resampled to PRISMA spectral resolution. The model performance on this dataset is in agreement with the test on the USGS spectral library. Finally, the regression model was applied to a PRISMA image , acquired on the Jolanda di Savoia farm (June 21st 2021), for CRC mapping. The resulting map was validated against field observations: the CRC map show values and patterns in good agreement with ground data confirming encouraging prediction capabilities of the model In conclusion, the proposed classification approach, trained on a spectral library is predictive, as proved on an independent spectral data set and on the PRISMA image. Further work will encompass testing the robustness of the model by collecting field ground data of Crop Residue Cover at the PRISMA scale; monitoring CRC dynamics on PRISMA time series; and, the use of Radiative Transfer Model simulations to enlarge the training set, accounting also for different factors controlling reflectance (e.g. soil moisture).

The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

Valuable tools for SMOSar application and other stuff. This is a multi-language/mult-tools toolkit based on FOSS components, such as GDAL, GRASS, and other geospatial software.

The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65-0.98 and 0.06-0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes.

The application and comparison of U-Net convolutional neural network architectures is proposed in this work to guarantee a fast and accurate convergence of inverse scattering problems solved by Born iterative method, even in the presence of strong scatterers. Starting from a preliminary configuration proposed by the authors in some recent papers, two variants are introduced and discussed to significantly reduce the computational cost, while guaranteeing convergence with very high accuracy in the dielectric profiles reconstruction when considering strong scatterers, such as tumors, thus working as a regularization process to mitigate the induced non-linearity. As a further enhancement, a novel approach is introduced which integrates U-Net and Resnet models to realize a segmentation process, thus leading to the effective feature extraction and the accurate identification of anomalies within healthy tissue. Numerical assessments on a variety of breast models including abnormal lesions are discussed to successfully validate the proposed machine learning approach, through the adoption of properly defined evaluation metrics.

Microwave sensors are gaining increasing interest in blood glucose detection, for their potential ability to perform a continuous non-invasive monitoring of the glucose concentration, by relating the change in the blood dielectric properties to a variation in the glucose level. Usually, the involved body part (phantom) is placed on the sensor to perform the reading. However, the placement modality, as well as other external factors not related to the blood glucose concentrations (BGC) (i.e. system noise, environmental temperature, human tissues variations other than blood tissue) may also have an effect on the sensor response, due to the change in the propagation path of the electromagnetic field inside the body part under test. In this work, the variation effects induced on the microwave sensor response by the changes in the thickness and the dielectric properties of skin and fat tissues are analyzed and faced. In particular, to mitigate the above drawback in terms of sensor instability, a solid "matching layer" is interposed between the resonant sensor and the phantom under test. A specific optimization procedure is performed to design microwave sensors with a stable response not influenced by variations in tissues different from the blood. Various sensors configurations with related resolution metrics are considered to assess the proposed idea and design methodology. Numerical results confirm the possibility to achieve a good trade-off between the measurement stability against undesired phantom variations and the sensitivity toward blood glucose levels, allowing to discriminate concentrations in the range of [100-300] mg/dL.

Thinned arrays are a class of non-uniform arrays in which the magnitudes of the excitation coefficients usually take on binary values. They are obtained by removing or connecting to matched loads the elements of a filled array, such that the final combination of active elements resembles that of a reference unequally-excited filled array. However, the advantage of reducing the complexity of the feeding network can lead, in some applications (such as in microwave sensing), to an unacceptable discrepancy between the actual radiation pattern and the desired one, especially for small to medium-sized arrays. In this work, an excitation diversity technique is included in thinned arrays design to overcome the above drawback. Data distributions achieved with the above approach are averaged to obtain potential high-quality final images. Moreover, the proposed methodology can be easily implemented in real-time adaptive arrays. The reported numerical results successfully prove the suitability of the proposed diversity technique, to be usefully applied for microwave sensing applications.

We develop a boundary analysis method, called unsupervised boundary analysis (UBA), based on machine learning algorithms applied to potential fields. Its main purpose is to create a data-driven process yielding a good estimate of the source position and extension, which does not depend on choices or assumptions typically made by expert interpreters, such as low-pass filtering or weights in the enhanced horizontal derivative case. We first test the simple synthetic case of two vertical faults, to understand the robustness of the method. We recognize three classes based on their centroids and find that the source edges could be detected at the transition between the two of them. Subsequently, we apply the UBA to the real magnetometric data of the archaeological site of Torre Galli (Calabria, Italy). We compare the results with those from two different boundary analysis techniques, the enhanced horizontal derivative and the tilt derivative. The main sources are well recognized by our approach and in good agreement with the enhanced horizontal derivative results, but UBA leads us to have a more complete description of the lineaments and to retrieve further features of archaeologic interest in the area. Instead, the tilt derivative features are affected by noise, which makes interpretation more complicated.

A detailed tolerance analysis for antenna arrays focused on the Fresnel zone is presented in this work, with the aim to derive the field distribution guaranteeing health safety issues. In particular, random errors related to the amplitudes and phases of the radiators, and random element failures, are considered. As such, the presented tolerance analysis falls within the more prominent theory of random arrays. A particular stochastic function related to the electric field distribution is analyzed and partially characterized by first- and second-order statistics. Subsequently, a discussion is carried out on the estimation of the cumulative distribution function (cdf) for the squared magnitude of the aforementioned random function. This leads to determine (confidence) level curves inherent to the squared magnitude of the electric field, representing a crucial aspect, in particular for safety issues in biomedical applications. The achieved results confirm the validity of the proposed approach, by extending also the literature for far-field focused arrays.