The downstream space sector in Italy is confronted with distinctive challenges, marked by a fragmented landscape, inadequate stakeholder interactions, underdeveloped business planning, and a critical need for improved industrial and financial acumen. In response to these challenges, the Italian Space Agency has taken a central role in addressing these issues through the establishment of the Downstream and Integrated Application Unit. The primary objective of this unit is to elevate the competitiveness of national Small and Medium Enterprises (SMEs), industries, and academia operating in the space sector. Within this strategic framework, the PRISMA SCIENZA program emerges as a pivotal initiative, aiming to harness the scientific data generated by the PRISMA mission for the improvement of the Italian Community. Specifically, the program is designed to facilitate the complete exploitation of mission data while concurrently fostering the advancement of Italian expertise in the hyperspectral remote sensing sector.By operating within this comprehensive framework, the PRISMA SCIENZA program contributes to the overarching goal of addressing challenges within the Italian downstream space sector. It not only serves as a mechanism for maximizing the utilization of mission data but also plays a vital role in promoting the growth and proficiency of the Italian space industry, aligning with broader strategic objectives set forth by the Italian Space Agency.Aim of this paper is to analyze the effects the PRISMA SCIENZA program brought to the Italian industrial and scientific communities.

Temporal changes in soil moisture (SM) and green vegetation affecting the spectral reflectance can heavily reduce the accuracy of topsoil properties estimation from satellite imaging. To minimize these effects on the soil organic carbon (SOC), sand, silt and clay estimations, an external parameter orthogonalization (EPO) model developed using laboratory based measured spectra was tested on PRISMA hyperspectral satellite data. The estimation of soil properties was performed using different machine learning algorithms. The results show that as compared to the uncorrected spectra, removing the effects of both green vegetation and SM (EPOSM+GV) from the reflectance spectra leads to 18%, 13%, 10%, and 24% improvement in the R2for clay, silt, sand and SOC retrieval, respectively. The Gaussian Process Regression (GPR) algorithm provides the best results for all of the soil properties with an RMSE of 9.5%, 14.2%, 6.9% and 0.68% for clay, silt, sand and SOC retrievals, respectively.

The scarcity of a sufficiently large and representative hyperspectral image dataset is a substantial obstacle to the effective development of algorithms for remote sensing applications. Hyperspectral images can provide rich spectral information for various tasks, such as land cover classification, vegetation monitoring, and environmental assessment. However, the limited availability of diverse and well-annotated hyperspectral datasets hinders the development and optimization of these models in this domain. For this purpose, the generation of synthetic hyperspectral images has emerged as a pivotal area of research.This paper aims to introduce a preliminary analysis of various AI-based methodologies specifically crafted to generate synthetic PRISMA hyperspectral images derived from Sentinel-2 data. By exploring innovative approaches, this study aims to develop novel techniques for creating synthetic datasets, providing valuable insights into the potential of synthetic hyperspectral imagery for algorithm training and evaluation in the absence of extensive real-world hyperspectral datasets.

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.

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.

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.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an ASI (Italian Space Agency) mission based on a technology demonstrator project, aimed at the in space qualification of an innovative hyperspectral payload and at the development of new Earth Observation products and applications.In the upcoming Earth Observation European scenario PRISMA is expected to be a good opportunity for science and users community to access hyperspectral data, both to develop new applications products and to explore technological innovative contribution of hyperspectral data to earth observation, in the perspective of a future hyperspectral operational mission.This paper will report the status of the PRISMA mission after the launch was on 22 March 2019 (VEGA launcher) and will present the commissioning phase and preliminary mission exploitation plan.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an Italian Satellite Earth Observation hyperspectral mission led by the Italian Space Agency (ASI) and planned for the launch in 2018. The payload is based on a high spectral resolution imaging spectrometer operating in the VNIR/SWIR (0.4-2.5 μm) optically integrated with a medium resolution Panchromatic camera (0.4-0.7 μm). The PRISMA Ground Segment includes the Mission Control Centre (MCC) and the Satellite Control Centre (SCC) both located at the Fucino station and the Instrument Data Handling System (IDHS) located at the ASI Space Geodesy Center in Matera. The IDHS is devoted to process the payload data downloaded using the X-Band antenna of the CNM (National Multimission Center). The IDHS data processing function generates Level 0, Level 1 and Level 2 products archived and distributed by the CNM. This paper defines the detailed PRISMA product types, specifying the content and process of the products generation.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is one the most important investments of Italian Space Agency (ASI) in the field of Optical Remote Sensing for Earth Observation. The PRISMA Space Segment consists of a single spacecraft embarking a state-of-the-art hyperspectral/panchromatic payload using pushbroom scanning technique. The PRISMA Ground Segment inlcudes the Fucino facilities for satellite/mission control and Matera CNM (Multimission National Center) systems, mainly devoted to data acquisition, products archive/delivery and user management. The IDHS facilty processes the payload data downloaded using the CNM X-Band antenna. The launch is scheduled in 2018 (VEGA Launcher) for a five years operational lifetime. This paper reports an overview of the mission and program development.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an Italian Earth Observation hyperspectral mission, fully funded by the Italian Space Agency (ASI - Agenzia Spaziale Italiana) and scheduled for launch in 2018. The PRISMA system will be composed of a Space Segment, consisting in a single satellite, embarking a state-of-the-art hyperspectral and panchromatic payload, a Launch Segment in charge of placing the satellite into the appropriate orbit and a Ground Segment geographically distributed in Italy between Fucino and Matera, devoted to satellite/mission control, user management and product delivery. An overview of the main characteristics and current status of the PRISMA mission is provided, with a focus on the space and ground segments.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an innovative Italian Earth Observation mission using a Hyperspectral/Panchromatic instrument based on a pushbroom scanning technique. The PRISMA mission development is completely funded by ASI (Italian Space Agency) and includes the system development program and the related applications and research activities. The system program is in the development phase (ECSS standard C/D phase) in the framework of a contract signed between ASI and an Italian Industries Consortium, including also the system on orbit commissioning. The launch is planned for the beginning of 2018. Thanks to the Low Earth, Sun Synchronous orbit placement, PRISMA will acquire up to 200.000 km2of daily Panchromatic/hyperspectral images within an Area of interest bounded by 180°W÷180°E - 70°S÷70°N, supporting many earth observation applications with relevant data. The Italian Science Community has been involved both in supporting the system development program, mainly for Calibration and Validation activities and algorithm developments, and in promoting research and applications based on Panchromatic/Hyperspectral remote sensed data.

The exploitation of the high revisit time (8-16 days) by the COSMO-SkyMed®(CSK®) satellites is an important opportunity for agricultural mapping. This study aims at evaluating CSK®potentiality to classify different crop types, with CSK®multi-temporal images collected over the agricultural site of Marchfeld, in Austria. Two different time series of CSK®HIMAGE SAR scenes, at 3m resolution, 9 at HH and 9 at VH polarization were taken during the vegetation season (from April to October 2014). CSK®data were processed and analyzed to investigate crop signatures from CSK®backscattering coefficient of five crop types, namely carrot, corn, potato, soybean and sugarbeet. In situ field observations were conducted during the SAR data acquisition. CKS®data were overlaid with crop fields ground truth. A Support Vector Machine (SVM) classification method has been applied. The classification results yield very promising overall classification accuracies using the combination of HH and VH polarization.

This study aims at presenting the results of a correlation analysis between the COSMO-SkyMed X-band backscattering coefficients (σ0) at VV and VH polarization and the DEIMOS-based Normalized Differential Vegetation Index (NDVI), carried out over carrot and pea fields. The analysis shows a significant higher correlation at VH (R=0.70 and R=0.65 resp.) than at VV polarization (R=0.15and R=0.32 resp.) for both the crop species analyzed. The results seem to suggest the possibility of using SAR X-Band VH data for crop status monitoring, at least for carrot and pea fields.

The COSMO-SkyMed®X-band SAR satellites constellation, due to its capability to acquire dense temporal data series at very high to high ground resolution and in co and cross-polarizations, is a promising system for crops monitoring and management. Its use, in combination with other SAR and optical systems in a virtual constellation, can result in a very effective tool for agricultural practice monitoring, management and control. This work intends to show a preliminary analysis of a dataset of COSMO-SkyMed®products acquired at HH, VV and VH polarizations over the Marchfeld agricultural area in Austria. The analysis has been carried out at a regional scale by taking into consideration the temporal behavior of two different clusters of vegetation macro-classes distinct for their different Normalized Differential Vegetation Index (NDVI) temporal signatures. Preliminary results show a significant correlation of the NDVI values with the COSMO-SkyMed®HH backscattering coefficients for both the clusters of classes as well as a significant one with VV backscattering coefficients for NDVI values lower than 0.7.