Agricultural drought is one of the most critical effects of climate change. This work proposes a machine learning based approach for agricultural drought monitoring that integrates seven standard remote sensing indices computed from Sentinel-2 multispectral imagery, agricultural drought damage percentage assessed in situ and six meteo-climatic variables, including Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index. We applied the approach to 117 agricultural fields in Italy, using a multinomial logistic regression model to classify the fields into zero-risk, medium-risk, and high-risk drought damage classes. The overall performances of the proposed classification model, summarized by an F1 score equal to 0.61, are not particularly encouraging as the model struggles to distinguish between medium-risk damage and high-risk damage classes. Nonetheless, the model shows promising results in identifying fields with zero drought damage and could be applied to reduce the time and cost of in situ measurements by excluding fields with no damage from the ground data collection.

The planetary crisis regarding water resources means that new methods are needed to monitor large areas of water basins that are threatened by chemical and natural pollutants, together with climate change. With the aim to monitor the pollution status of some Sites of National Interest (SIN) which represent very large contaminated Italian areas classified as dangerous, we are applying new or already well established algorithms to optical satellite images. In particular, a recently introduced oil spill detection algorithm [1] was able to, consistently and reliably, confirm the presence of oil in five polluted lake waters analysed. The combination of this algorithm with metagenomic analysis of the spill areas detected by the satellite allowed to identify drivers of the microbial response to oil pollution. Based on ortholog abundances, metabolic pathway reconstruction carried out in Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) software highlighted the degradative capacity of these microorganisms. These microorganisms could be suitable candidates for treatment of crude oil, aromatic hydrocarbons and the desulfurization of persistent petroleum substances like dibenzothiophene. Building upon this recent algorithmic development, the SatellOmic project funded by the Italian Space Agency (ASI) focuses on the pre-operational application of such integrated approach combining satellite sensing and metagenomics analyses for real-time monitoring of water bodies threatened by oil spills, as well as for the design of recovery strategies based on the use of valuable hydrocarbonoclastic microorganisms.

The GEORES project is funded by the Italian Space Agency (ASI) and aims to develop a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, based on the synergistic use of the most advanced Earth Observation (EO) technologies, Artificial Intelligence (AI) and eXplainable AI (XAI). GEORES is organized into four main modules to support management of the main risks associated with land degradation: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data, calculation models and algorithms are integrated to identify "hot-spots" of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators ("geo-analytics") calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and Gargano Promontory, Apulia Region, southern Italy, and foresees the engagement of final users (i.e. Regional Civil Protection and Municipality of Bari).

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.

This paper provides an in-depth exploration of the Crop Module within the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, specifically addressing challenges in precision agriculture. The study unfolds in the "Fortore" irrigation district (Southern Italy), focusing in particular on the 6/B district. The Crop Module, rooted in AquaCrop crop model architecture, emerges as a pivotal component in simulating and predicting crop growth, development, and water dynamics. It operates across leaf development, crop growth and productivity, and water balance levels, ensuring adaptability to daily temperature variations for real-time simulations. In interaction with the Soil Water Balance Module (SWB) and leveraging insights from satellite imagery, the Crop Module undergoes meticulous calibration and validation. The expected outcomes encompass increased precision in irrigation scheduling, early anticipation of water demand, and improved seasonal forecasting. This comprehensive approach positions stakeholders for informed decision-making, fostering sustainability and efficiency in agricultural practices.

This paper reports on a Spatial Decision Support System (SDSS) for the early, medium, and short-term forecast of irrigation needs in a semi-arid Mediterranean environment. The SDSS is developed in the context of the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project supported by the Italian Space Agency (ASI). THETIS integrates hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for efficient planning of irrigation resources. The study describes initial results concerning the irrigated area of the Apulian Tavoliere (AT) served by the Reclamation Consortium of the Capitanata, Foggia, Italy.

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.

The Italian Space Agency (ASI) and the Indian Space Research Organisation (ISRO) established the joint Earth Observation Working Group (EOWG) that is currently focusing on hyperspectral (HYP) activity. Eleven projects are investigating the use of ASI’s PRISMA data for agriculture, land cover classification, mineral and soil mapping, Martian analogues, urban lakes, biodiversity and calibration. The paper provides an overview and first results after one year and half since the EOWG HYP was launched.

The "Multi-mission and multi-frequency SAR" program (2021-2023) of the Italian Space Agency supported the national community to develop algorithms combining SAR data collected in C, X and L-bands. Main achievements in terms of SAR data exploitation, integration and data fusion, and new products prototyping are presented with regard to agriculture, natural hazards, urban areas, cryosphere, sea and coast. Perspectives are finally outlined with regard to future SAR missions and towards downstream applications.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is a demonstrative spaceborne mission, fully deployed by the Italian Space Agency (ASI). To support the calibration/validation activities of the PRISMA hyperspectral mission, ASI and the National Research Council (CNR) started in 2019 the PRISCAV project (Scientific CAL/VAL of PRISMA mission). The main objective of PRISCAV is the comprehensive characterization of the performances of the PRISMA payload in orbit in different operational scenarios and the verification of the durability in time of the performances. To this end, PRISCAV created a network of 12 instrumented sites showing different land-use and surface settings (Snow; Sea; Inland and Coastal Water; Forest and Cropland) to obtain independent and traceable in-situ and airborne Fiducial Reference Measurements (FRM) simultaneous to PRISMA acquisitions in order to assess the required performance of sensor, data products, and processors at the different levels (i.e. Top-of-Atmosphere Level 1 Radiances and Bottom-of-Atmosphere Level 2 Reflectance standard products). Moreover, on some of these sites, simultaneous PRISMA and airborne AVIRISNG acquisitions were made coupling remote sensing with in-situ observations to support new mission development and in particular the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). Recent updates on CAL/VAL activities and on AVIRSNG campaigns are presented in this contribution.

PRISMA, in orbit since March the 22nd 2019, opened the user access two years ago, in spring 2020. Recently we break-out the one-thousand threshold of user accounts, which witness the high worldwide interest into this innovative mission. This paper shortly describes the results achieved in this period of full operational usage.

The recent launches of Earth Observation (EO) satellites have made numerous SAR images available to dynamically monitor the ocean with improved spatial resolution at shorter revisit time. Focusing on automatic target detection of ships with the specific aim of improving our Maritime Domain Awareness (MDA), this work assesses the capabilities of multi-frequency/multi-mission spaceborne Synthetic Aperture Radar (SAR) data. Specifically, Sentinel-1 (C-band), COSMO-SkyMed (X-band), and SAOCOM (L-band) missions have been considered in this analysis. The aim of the paper is to present an efficient approach for interpreting images acquired within small time gaps, ensuring fisheries and pollution control, anti-piracy actions, and surveillance over coastal/protected regions.

In continuity with the investments made on SAR technologies in the last two decades by the Italian Space Agency (ASI), the “Multi-mission and multi-frequency SAR” program currently supports ten R&D projects aiming to design, develop and test innovative algorithms for exploitation of multi-mission/multi-frequency SAR data. Perspectives of engineering and pre-operational development are demonstrated with regard to various application domains (i.e. agriculture, urban areas, natural hazards, cryosphere, sea and coast). Access to COSMO-SkyMed and SAOCOM images is facilitated by ASI to allow the research consortia to achieve an effective multi-frequency SAR data integration. The program represents a foundational step in the ASI's roadmap towards “scientific downstream applications”, i.e. applications enabled by the exploitation of mature and validated algorithms that have been originally developed to answer scientific questions and/or retrieve geophysical parameters, and have been brought to the stage that they can generate products that address specific user needs beyond scientific and academic purposes only.

PRISMA, in orbit since March the 22nd 2019, opened the user access in spring 2020. After one year, many hundreds of users have gained the capability to program new acquisitions or download image products from the online archive containing more than 67k datasets, under a quasi-open & free data policy and licensing scheme. During this time frame and despite the COVID-19 related difficulties, PRISMA performed normal operations delivering not only nominal verified quality data to users but establishing collaborations with other space agencies, in order to enable and support synergies with other hyperspectral missions. This paper describes the mission and the results achieved in this first period of full operational usage.

PRISMA is an Italian EO hyperspectral Mission conceived as a Pre-operational and technology demonstrator. PRISMA is in orbit since March the 22nd2019, has completed the Commissioning Phase in January 2020 and is currently in the operational phase. User registration to access PRISMA mission products has been opened on 21thof May 2020. Users can request new acquisitions on own AoI and/or archived products based on data acquired within the background mission. According to the PRISMA data policy, a wide use of products is allowed in order to validate the technology, maximize the return on investment and support the development of skills in an innovative sector. This paper reports an overview of the mission performances, scientific initiatives and first month user access results.

The European Commission has identified the enhancement of integrated border management as one of the most important areas of interest. The 7th Framework Programme for research and development has been used since 2008 to improve the performance of the European Border Surveillance System (EUROSUR) and to test technical capabilities to curtail the flow of illegal immigrants reaching European coasts. In this sense, several initiatives have been fostered, CLOSEYE being one of them. In this paper a comprehensive description of the CLOSEYE project will be provided, highlighting the preliminary results obtained in this context by the synergic use of the two main Italian assets providing services in Satellite Earth Observation and Communications: COSMO-SkyMed constellation and Athena-Fidus satellite.

The April 25, 2015 M7.8 Gorkha earthquake caused more than 8,000 fatalities and widespread building damage in central Nepal. Four days after the earthquake, the Italian Space Agency's (ASI's) COSMO-SkyMed Synthetic Aperture Radar (SAR) satellite acquired data over Kathmandu area. Nine days after the earthquake, the Japan Aerospace Exploration Agency's (JAXA's) ALOS-2 SAR satellite covered larger area. Using these radar observations, we rapidly produced damage proxy maps derived from temporal changes in Interferometric SAR (InSAR) coherence. These maps were qualitatively validated through comparison with independent damage analyses by National Geospatial-Intelligence Agency (NGA) and the UNITAR's (United Nations Institute for Training and Research's) Operational Satellite Applications Programme (UNOSAT), and based on our own visual inspection of DigitalGlobe's WorldView optical pre- vs. post-event imagery. Our maps were quickly released to responding agencies and the public, and used for damage assessment, determining inspection/imaging priorities, and reconnaissance fieldwork.

COSMO-SkyMed is an Italian Dual-Use (Civilian and Defence) Earth Observation Space System composed of four satellites with an X-Band Synthetic Aperture Radar (SAR). It is conceived for global environmental monitoring, scientific and commercial purposes and strategic applications (Defence and National Security). During the last years, thanks to a continuous monitoring of user feedbacks, some aspects of the interface between COSMO-SkyMed System and external users have been identified to be improved. New services and new tools are intended to be introduced in order to make the interface more efficient. In this paper an overview of these new services will be shown, mainly concern with the Customer Service activities and the data utilization by civilian users.

In 2003, the Italian Space Agency (ASI), supported by the Ministry of Education, Universities and Research (MIUR) and the Ministry of Defense (It-MoD), commissioned and funded the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) mission, an Earth Observation Dual Use System (civil and military) for the monitoring of Resources, Natural Disasters and National Security. COSMO-SkyMed system, which represents the Italian biggest investment in the Earth Observation (EO) field, was realized by TAS-I (Thales Alenia Space Italy) as the industrial prime contractor for the Space Segment, and Telespazio as prime contractor for the Ground Segment, logistic and operations. For what concern the commercial distribution of COSMO-SkyMed products, ASI and Telespazio have constituted the e-GEOS company.