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.

In the recent decades, the availability of Earth Observation technologies (from satellite, aerial and ground) for the study and preservation of the human past is stepping into a golden age. The currently available data, tools and services (including open cloud resources to process big satellite data) opened up a new frontier of possibilities and applications which at the same time also pose several challenges to be faced. All of the diverse aspects ranging from the research and innovation to the application issues, namely from the science to services, must to be tackled by the scientific community in conjunction with the "end uses needs" to ensure an effective and reliable applicability. The use of space technologies, big data, artificial intelligence (AI) for the study of the human past highlights the multi-trans and inter-disciplinary scientific and technical aspects not only for the novel concepts and approaches proposed, but also for the development between and across diverse disciplines In this paper, the state of the art in the field of EO Big data and artificial intelligence for the study of human past is brief summarized.

Egypt has a long history extended to the prehistory, Neolithic, pre-dynastic, Pharaonic, Greek and Roman, Coptic, and Islamic eras. Because of the climate changes, the land use/land cover changes, and the land-reclamation activities, the groundwater level under the reconstruction and the monuments walls became high and present big risk on these heritage sites. This study aims to show some studies applied on some archaeological sites in Egypt using Satellite data and GIS tools. In this study, Optical (Landsat, Quickbird, Orbview, Spot, and Sentinel2) have been analysed by RS and GIS software (ArcMap, Snap, and Envi) used to detect the geo-environmental problems around the archaeological in Egypt. In order to detect the land use/land cover changes close to the heritage sites, the spatial distribution analysing, spatial autocorrelation, Ripley's K function, Hot Spot Analysis, Spatial Cluster Analysis (Ripleys K Function), and the supervised classification methods were applied. The results of this study proved that the urban sprawling close to the studied areas was enormous and caused big risk on the archaeological areas. Such studies can support the decision makers with the required information about the environmental status around the archaeological sites for keeping these sites in safe condition.

We propose an accurate and rapid methodology for the extraction of spatio-temporal fire features using Sentinel 2 products and the Google Earth Engine (GEE) platform. All Sentinel 2 images available in the GEE platform were clipped using the fire area mask and then the NBR, NDVI. dNBR and RdNBR indices were derived. The differential values of NBR, NDVI, dNBR and RdNBR were obtained by calculating the difference of the index values between two temporally adjacent images. The use of all available images in GEE restricted the time of occurrence of the images 5 days, excluding cloud-covered images and shortening the processing time of each satellite image. The results obtained showed that the proposed methodology allows for the rapid and accurate identification and classification of burnt areas, and also allows for the efficient and accurate extraction of the spatio-temporal characteristics of post-fire vegetation recovery. The results obtained can be used to implement targeted post-fire vegetation restoration practices.

Despite the importance of the atmospheric boundary layer (ABL) for many aspects of our life, including weather, telecommunication, renewable energy, it currently represents the most under-sampled part of the atmosphere and an observational gap at global level. Cooperation actions at European level are significantly contributing to the development and application of ABL profiling systems within the European observation network. Among these initiatives are the COST action PROBE, the E-PROFILE program, and the ACTRIS research infrastructure. In addition, the European Space Agency (ESA) deployed radiometric thermodynamic profilers at few sites of their satellite tracking station network and is promoting consistent atmospheric retrievals among them. This initiatives are reviewed here, with a focus on microwave radiometers, presenting preliminary results from an ESA funded project aiming at developing reference and harmonized monitoring and retrievals at several ESA sites.

Sun-tracking microwave radiometry is an effective technique aimed at estimating the tropospheric attenuation in all-weather conditions by using the Sun emission as an equivalent beacon source. Though such technique definitely represents an interesting alternative to Earth-space electromagnetic propagation experiments, especially in the frequency region beyond 50 GHz for which space-borne beacons are hardly currently available, its application is not trivial, mainly because of the issues arising from the need to precisely pointing at the Sun: catching the Sun peak radiation might not be an easy task, especially if the frequency increases, i.e. the beamwidth gets narrower. This contribution focuses on the advanced experimental techniques developed in the framework of the ESA WRad project (funded by ESA) to maximize the accuracy of the W-band measurements collected by the multi-channel RPG MWR installed at Politecnico di Milano. Issues of the experimental campaign are highlighted and the associated solutions are discussed in detail.

Aerosols are significant atmospheric constituents that modulate radiation and cloud processes. We evaluated 17-year aerosol profile trends in Barcelona, Spain, from lidar measurements. In summer aerosol reaches 5 km, while in the other seasons it exhibits clear exponential decay. Sahara dust transport affects all seasons, with winter layers above and others penetrating the boundary layer. This study informs the formation of haze and urban preservation strategies in the Mediterranean. The analysis puts in evidence that the averaged net radiative effect is of cooling at both surface level and top of the atmosphere.

In this paper, we wish to explain the contradiction of quality assessments of pansharpening carried out at full and reduced spatial scales. It seems that at full scale, methods based on Component Substitution (CS) are quantitatively poorer than the other methods, but this depends on the intrinsic space varying misregistration between the two datasets. At reduced scale, the local shifts are divided by the MS-to-Pan scale ratio and thus they tend to vanish. The problem of full-scale quality indexes is that they were originally validated on aerial Multispectral (MS) data, with synthetic panchromatic (Pan) and thus total absence of misregistration. In the presence of local misregistration due to inaccurate information of the height of the imaged surface, CS methods locally align the lowpass MS components towards the sharpening Pan, thereby preserving the geometry of the scene; all the other methods produce fading contours because of shifts. The favorable property of CS, however, impacts against the (spectral) consistency property of Wald's protocol, developed when the misalignments between MS and Pan was a small fraction of the pixel size, and hence negligible. In this perspective, methods that do not shift the original MS information are better, even though the visual quality of fading contours is worse. After exposing and explaining the contradiction between full- and reduced-scale assessments, we perform an in-depth analysis of the spectral and spatial consistency indexes of three widespread full-scale protocols: QNR, KQNR and HQNR. We investigate the robustness to shifts of all consistency indexes and propose to couple the spectral index and the spatial index that are least sensitive to shifts. In this way, the ranking of methods of reduced-scale assessments is preserved in full-scale assessments.

Air pollution is considered a very critical environmental risk to human health. The World Health Organization reports that it is responsible for almost seven million deaths. As so, motivation is enough to decrease population exposure. However, several unsolved issues that require additional research remain. In particular, despite global monitoring development, coverage is insufficient to accurately describe the spatial variability for specific pollutants within different areas. The TROPOshperic Monitoring Instrument mounted on Sentinel-5P is one of the satellite instruments that retrieve atmospheric pollutants' concentration with a comparatively high spatial resolution, around 5 km. However, the spatial detail of the available products is often unsuitable for the purpose at hand. Also, physical constraints prevent enhancing the sensor's nominal spatial resolution further. So, there is no alternative way to collect high-resolution information than through processing algorithms. In this research, we investigated the problem of super-resolving Sentinel-5P products by employing traditional and deep learning-based approaches. While the former do not require a training phase because they rely on simple physical models, the latter can attain higher performance by reproducing highly complicated models. However, the lack of high-resolution reference data makes the needed training phase of network parameters extremely challenging. In this paper, we studied different approaches tailored to the imagery at hand and evaluated their accuracy with Sentinel-5P data. This study provides insights into the techniques and how they should be employed to monitor air quality accurately. The results of this work give significant information for the development of suitable super-resolution algorithms.

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks for pansharpening of multispectral images to the problem of hyperspectral image pansharpening, i.e., the fusion of a single high-resolution panchromatic band with a low-resolution hyperspectral data cube. This is achieved by means of a PCA transformation which allows to compact the most of the HS image energy in a few bands, which are then suitably super-resolved using a pansharpening network designed for few spectral bands. Our experiments show very encouraging results which compare favorably against the state-of-the-art methods.

Millimitere (mm) and sub-millimiter (sub-mm) radiometer observation of the atmosphere from space is an appealing topic given the variety of information obtainable. The exploitation of window frequencies and various gaseous absorption bands at 50/60, 118, 183 allow for a better representation of tropospheric temperature profiles, water vapor and cloud liquid contents, as well as for hail detection, and to some extent, rainfall and snowfall estimates. These observations have shown tangible impacts on numerical weather prediction and data assimilation, climate benchmarking, hydrometeorology, extreme weather nowcasting, and civil protection. Further benefits for ice cloud retrievals are expected from observations at higher frequency, such as 243 and 664 GHz channels foreseen in the upcoming EUMETSAT Polar System-Secon Generation (EPS-SG) Ice cloud imager (ICI) sensor [1] , [2]. The increase in frequency, and consequently the reduction in wavelength, from mm to sub-mm also gives the technological advantage of reduced size of the overall system, maintaining performances unchanged, thus making it easier to implement constellation of radiometers with the glaring benefit of incrementing the repetition time of the satellite overpasses. A precursor on this topic was proposed by Prof. Marzano in 2009 [3] with the FLOwer constellation of MM-wave RADiometers (FLORAD) mission. The FLORAD concept consisted in tree small satellites ([removed]

This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer - New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feedforward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the ?-IASI radiative transfer model to simulate IASI/IASI-NG radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.

The international community has definitively recognized the urgent need for a holistic understanding of land systems in the context of Global Change. Our approach to the integration of climate information in the assessment of sustainability and land degradation vulnerability is based on the analysis of climate data within the geographical constraints imposed by the land cover heterogeneity. This study uses the CHIRPS dataset (Climate Hazards Group InfraRed Precipitation with Station data) to evaluate potential rainfall impacts in Basilicata (Italy), a very complex Mediterranean area. Two indices, accounting for rainfall erosivity and, by contrast, for the local degree of dryness are integrated in a unique rainfall exposition layer. For each land cover, this information layer enables us to detect the areas most exposed to rainfall erosion risk or to water scarcity and drought. This layer can be profitably used within multivariate analyses for the estimation of land degradation vulnerability.

The project "Topsoil properties Estimation from Hyperspectral Remote sensing for Agriculture" (TEHRA), funded by the Italian Space Agency (ASI), aims at developing methods and algorithms for the estimation of soil properties of agronomic and environmental interest from PRISMA satellite hyperspectral data, that could support: 1) the adoption of more sustainable and climate-smart farming practices, e.g. through the implementation of precision agriculture applications; 2) monitoring in support of agricultural and environmental policies, e.g. related to climate change and for the encouragement of the adoption of practices preserving soil health.In this paper, some results of the first year of the project are illustrated. They concern: 1) a scenario definition study; 2) studies on the confounding effect of soil moisture and crop residues; 3) exploitation of multi-temporal PRISMA data and 4) data fusion with proximal soil sensing.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

PRISMA is a hyperspectral pushbroom sensor, launched by the Italian Space Agency in 2019. PRISMA collects the reflected Earth signal from VNIR to the SWIR with 230 spectral bands with a variable FWHM according to the prism dispersion element. This work intends to develop a procedure suitable to monitor the consistency of photon and thermal noise components across a times series of L1 radiance images collected on different Mediterranean scenarios (i.e. rural and coastal). To improve the retrieval of the useful signal and the random noise on PRISMA images the spatial variability of the scenes has been considered in the new version of the HYperspectral Noise Parameters Estimation (HYNPE) algorithm. The procedure, tested on two PRISMA time series, has assessed quite stable and coherent values for the retrieved noise coefficients, not significantly affected by seasonal radiance variations and scene characteristics

The time dynamics of the instrumental seismicity recorded in the area of the Lai Chau reservoir (Vietnam) between 2015 and 2021 were analyzed in this study. The Gutenberg-Richter analysis of the frequency-magnitude distribution has revealed that the seismic catalog is complete for events with magnitudes larger or equal to 0.6. The fractal method of the Allan Factor applied to the series of the occurrence times suggests that the seismic series is characterized by time-clustering behavior with rather large degrees of clustering, as indicated by the value of the fractal exponent (Formula presented.). The time-clustering of the time distribution of the earthquakes is also confirmed by a global coefficient of variation value of 1.9 for the interevent times. The application of the correlogram-based periodogram, which is a robust method used to estimate the power spectrum of short series, has revealed three main cycles with a significance level of (Formula presented.) (of 10 months, 1 year, and 2 years) in the monthly variation of the mean water level of the reservoir, and two main periodicities with a significance level of (Formula presented.) (at 6 months and 2 years) in the monthly number of earthquakes. By decomposing the monthly earthquake counts into intrinsic mode functions (IMFs) using the empirical decomposition method (EMD), we identified two IMFs characterized by cycles of 10 months and 2 years, significant at the 1% level, and one cycle of 1 year, significant at the 5% level. The cycles identified in these two IMFs are consistent with those detected in the water level, showing that, in a rigorously statistical manner, the seismic process occurring in the Lai Chau area might be triggered by the loading-unloading operational cycles of the reservoir.