Our work investigates the potential of high-resolution hyperspectral satellite data for detecting atmospheric methane concentrations. We employ the matched filter with Albedo correction and reweiGhted L1 sparsity Code (MAG1C) algorithm, which integrates a sparsity prior, a matched filter, and albedo correction techniques. For the analysis, we utilize hyperspectral data from the PRISMA mission, leveraging its high spatial resolution to potentially enable more accurate localization of point emission sources. Comparing the methane column estimation resulting from our work with corresponding products provided by both the Sentinel-5P and GHGsat missions, a good agreement was found. In particular, a bias of 5 ppb with respect to the methane abundance estimated from GHGsat was reached.

Land surface temperature (LST) is one of the most important variables for the terrestrial ecosystem. [1] It stands as a fundamental Essential Climate Variable (ECV) [2] and hold paramount significance across different environmental and agricultural domains [3].Temperature estimation from satellites is increasingly widespread, which allows to obtain large-scale and almost real-time informations.However, estimating temperature using satellite data has some limitations: presence of cloud cover has an impact on remotely sensed observations [4].This paper presents a data fusion approach for enhancing Sentinel-3 LST products by replacing cloudy pixels with data from MODIS, GCOM-C and ERA5-Land. The model has been tested on a trial study area, but its versatility allows it to be applied worldwide.The resulting fused LST product is subjected to an evaluation against LANDSAT 8 and 9 LST data. The performance of the data fusion demonstrates the efficacy of the proposed fusion method, with Pearson correlation values ranging from 0.60 to 0.93.The study not only contributes to advancements in LST data quality but also establishes a benchmark for future research in satellite data fusion.

The activities undertaken within the EcoNet project aim at the design and development of an integrated system for the monitoring of changes in surface waters natural status based on different sensoristic techniques. The proposed integration approach combines ground measurements and hyperspectral satellite images. The promising dialogue that occurs between these two multi-sensoristic technologies requires the implementation of appropriate tools for data handling and analysis which in this work are represented by Artificial Intelligence (AI), particularly suitable to retrieve very subtle relationships among the data. This integration can open enormous potential for overcoming the limits of traditional environmental monitoring and diagnostic techniques.

The presented methodology aims to create a daily Aerosol Optical Depth (AOD) fusion product by integrating observations and forecasts from various EO data sources. The data used for this purpose are from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) sensors on Sentinel−3, the Second Generation Global Imager (SGLI) on the Global Change Observation Mission−Climate (GCOM−C), and the forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Leveraging the strengths of each dataset, the developed algorithm adapts to different formats and resolutions, providing a unified and higher−resolution AOD dataset.The Sentinel−3 AOD product ensures high resolution (4.5 km), the GCOM−C product ensures dataset accuracy, and CAMS forecasts offer predictive insights. The algorithm employs a mathematical averaging technique for coincident pixels, facilitating precise AOD estimates in overlapping regions, while a mosaic technique seamlessly integrates non−coincident areas.This fused dataset enhances spatiotemporal coverage, contributing to a more in−depth understanding of atmospheric composition variations. Emphasizing the importance of complete AOD data, the methodology is versatile and has been validated against Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, achieving a Pearson coefficient of 0.87.The product was tested on the Italian Po River Basin area for the year 2021. This region represents an area of great interest for the study of air quality and atmospheric dynamics due to its geographic complexity and the significant impact of anthropogenic activities.The final product is an improvement over the reliable SYN−AOD product from Sentinel−3, providing daily data obtainable in less than an hour through an automated algorithm.

The PRIMARY (PRIsma for Monitoring AiR quality) project objective is to address air quality monitoring, especially in urban areas, exploiting the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission. Utilizing PRISMA's hyperspectral data, the project aims to gain insights into atmospheric aerosol content and composition, crucial for understanding environmental and health impacts, especially in urban areas. Overcoming spatial resolution limitations and the inverse problem's complexity in satellite-based characterization, PRISMA's decametric spatial resolution and artificial intelligence play crucial roles. A synthetic PRISMA-like dataset, relying on data provided by the Copernicus Atmosphere Monitoring service (CAMS), was generated for training neural networks for estimating aerosol characteristic exploiting PRISMA data. Preliminary results are encouraging. Properly field campaigns were performed in Rome (autumn 2022) and Milan (winter to summer 2023) to support the validation of the PRIMARY project's outcomes. In addition, drone-based campaigns are currently ongoing.

This paper introduces a hybrid approach to dimension reduction of PRISMA hyperspectral data, employing both linear and non-linear techniques: Principal Component Analysis (PCA) and autoencoders. The study aims to validate the efficacy of autoencoders by comparing results with the well-established PCA method. Our primary objective is to harness the complementary strengths of both methods in a hybrid framework, wherein certain bands may exhibit superior performance with autoencoders, while others fare better with PCA in dimension reduction. This strategic amalgamation not only accelerates data transfers and lowers computational costs for real-time applications but also leverages the specific advantages offered by each technique. The paper underscores the potential of this hybrid approach for optimizing hyper-spectral data for enhanced feature extraction in various neural network applications.

This research utilizes radiance data from the PRISMA hyperspectral satellite mission, employing the MAG1C algorithm that incorporates sparsity and albedo correction. The algorithm is applied to convert level 1b data into methane concentration-pathlength maps, providing essential insights for addressing methane emissions. Data from the TROPOMI imaging spectrometer on board the Sentinel-5P mission will be used to compare the experimental results obtained through the algorithm. To bridge the difference in resolutions, the data comparison will be performed by degrading the PRISMA data by means of a weighted average on the different data resolutions, resulting in an RMSE (Root Mean Square Error) of 73.7175 ppb. The use of new high resolution data is one of the factors on which the maximum effort must be focused with the aim of being able to localize the point emission sources.

Super-resolution is aimed at enhancing image spatial resolution and it has been intensively explored for many years. The recent advancements, underpinned with deep learning, also include techniques developed specifically for hyper-spectral data. However, most of the emerging methods are validated in application-independent scenarios, which often rely on an unrealistic experimental setup—the reconstruction is performed from simulated low-resolution images (degraded from an original image) with the goal of inverting the degradation process and restoring the original image. This leads to over-optimistic assessment of super-resolution capabilities and limits their practical applications. In this paper, we demonstrate task-based validation for different types of hyperspectral PRISMA image super-resolution, including pan-sharpening, fusion of multispectral and hyperspectral data, as well as single-image super-resolution. The obtained results reported in the paper are encouraging and they help better understand the value of super-resolved PRISMA images.

There is an urgent need to assess the condition of transport network in many countries all over the world as in Italy and, in this context, the consideration of non-destructive techniques has particular importance. These techniques, such as space-born systems, laser scanners, ground-penetrating radar (GPR), and monitoring tests, provide valuable data but have limitations in assessing specific aspects of the infrastructure. This research aims to overcome these limitations by using a "data fusion" approach to achieve a comprehensive understanding of the asset’s condition. The ongoing project "EXTRA-TN" focuses on road infrastructures selected as case-studies, located in Salerno (Italy), utilizing the various aforementioned methods. This integrated approach aims to enhance infrastructure resilience, provide valuable information for maintenance scheduling, and monitor both external and internal factors affecting safety and functionality.

The InnEO’Space PhD program addresses the growing demand for skilled individuals in Earth Observation (EO) data management. It offers modern and transferable courses to enhance researchers' innovation-oriented skills. The program includes InnEO Startech, focusing on entrepreneurship and leadership, and the Machine Learning for Earth Observation InnEO Summer school, emphasizing open science and technical skills. Small Private Online Courses (SPOCs) provide blended learning through a user-friendly platform. The InnEO PRO section facilitates interaction among PhD students and professionals, offering various resources and self-assessment tests. The program's success has led to collaborations with UNIVERSEH and FabSpace, while AI4AGRI aims to utilize its resources for AI in EO research.

EcoNet is a joint project between the Italian Space Agency (ASI) and the Institute of Nanostructured Materials of the National Research Council of Italy (CNR-ISMN) with the participation of University of Tor Vergata (UTOV) that aims to develop an integrated sensor-driven system managed by artificial intelligence (AI) for monitoring surface waters near human settlements. The present paper outlines the foundational concepts of the project, the methodology and the ongoing activities, with an outlook towards the innovation for downstream applications in the field of aquatic ecosystem monitoring and management.

Air pollution and its effects on human health pose a significant challenge in modern society. The PRIMARY (PRIsma for Monitoring AiR quality) research project aims to utilize the capabilities of the Italian Space Agency's (ASI) PRISMA (PRecursor HyperSpectral Application Mission) to enhance air quality monitoring, particularly in urban areas. In particular, the project focuses on the exploitation of the hyperspectral PRISMA data to obtain detailed qualitative and quantitative data on atmospheric aerosol load and composition in urban environments. Current satellite-based characterization of particulate matter is limited due to spatial resolution constraints and to the complexities of the underlying inverse problem involving multiple variables. The PRIMARY project addresses the first issue through the decametric spatial resolution of PRISMA images, while the second issue is tackled by leveraging artificial intelligence approaches.

In this paper, we present our study focused on applying a vision transformer-based pansharpening technique to enhance PRISMA satellite hyperspectral data. The PRISMA mission, launched by the Italian Space Agency, captures hyperspectral images comprising visible and near infra-red, as well as short-wave infra-red channels. By integrating the panchromatic image of high spatial resolution with the hyperspectral data of high spectral resolution, the pansharpening process consists in producing spatially-enhanced hyperspectral imagery. Our research involves modifying and adapting the state-of-the-art HyperTransformer architecture to effectively process real-life PRISMA data. The evaluation of our model’s performance utilizes PRISMA L2D data, encompassing simulated low-resolution data and real-life data. We employ quantitative metrics and visual examination to assess the results. We also highlight the importance of choosing right PRISMA data processing level for the pansharpening process. The proposed pansharpening model successfully enhances PRISMA data for practical applications, contributing to the advancement of Earth observation techniques.

In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented. Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events. The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.

In this study we present a novel approach dedicated to the retrieval of volcanic ash parameters by means of data acquired from the Sea and Land Surface Temperature Radiometer on board of the Copernicus Sentinel-3. In this framework, we developed a procedure combining Radiative Transfer Model simulations and Neural Network for estimating three volcanic ash parameters such as aerosol optical depth, effective radius and ash mass. The Radiative Transfer Model simulations have been considered for producing synthetic training sets, which have been used in the training phase of the Neural Networks development. In particular, nine latitude's belts have been identified for training several Neural Networks ensuring the global coverage of the method. The approach has been tested by comparing the results of the trained NN with the ones obtained by applied the state-of-art Look Up Table and the Volcanic Plume Retrieval procedures. The results of the methodologies applied on Raikoke, 2019 eruption demonstrated the feasibility of the proposed approach by registering values of the correlation coefficient between all the three methods ranging between the 65% and the 94%.

Machine learning can be considered as a very important area within artificial intelligence and it is characterized by algorithms and techniques that learn by examples. In the last decade, mainly due to the improvements obtained in the field of high performance computing, such as the enhanced exploitation of cloud technology and of graphics processing units (GPU), machine learning models have gained considerable progress as far as remote sensing and Earth Observation (EO) applications are concerned. However, the need of huge quantities of data necessary for the training phase, may be still a limiting factor especially in problems addressing the quantitative estimation of geo-physical parameters. In this paper, we report about the design and the development of a new platform capable of meeting the requirements of scientists and researchers who are attracted by the use of machine learning but meet difficulties in the generation of reliable data sets. The platforms relies on the implementation of radiative transfer models, plus a bunch of appropriate functionalities, in order that simulated data can be added to those available by ground-truth campaigns.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUTp(Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be beneficial to air traffic security and to predict any correlated impact on the environment; for example, the possibility of acid rain events. Within the presented work, we exploited Sentinel-5p radiance data (Level 1 b) to detect and retrieve SO2 volcanic emissions through a neural network based algorithmthat produces rapid SO2 vertical column estimates. The dataset used for training the net was composed of 13 TROPOMI Level 2 “Offline” SO2 data collected during the Etna Volcano eruption that occurred in 2018 from 22 December to 1 January. Experimental results are very encouraging and open to the perspective ofmake available a new and stable product for monitoring atmospheric SO2 clouds on a global scale based on Sentinel-5p acquisitions.

This study aims at analysing local government efficiency using EO data to focus on the construction of new buildings in burned areas. We detected all forests and pastures of Sardinia (Sardinia) that witnessed at least a one-hectare wildfire, for biennium 2005-2006, using Landsat-TM data. We then monitored all the burned areas for a period of at least 10 years following the wildfire, up until 2016, by using Landsat products and VHR imagery provided by the Sardinian Region. The idea was to verify compliance with the Italian Framework Law on forest fires, which prohibits the construction of any structure or infrastructure aimed at civil settlements on woods and pastures affected by a fire for the next ten years. We retrieved as many as 748 burned areas, over which 148 violations were detected. These data have been used to carry out an econometric pilot analysis. Results suggest that, in general, the emergence of violations is inversely related to wildfire dimensions and that, on average, law-breaking occurs within the first five years.