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 project "EO AFRICA EXPLORERS—PRISMA 4 AFRICA", funded by the ESA, aims at combining products derived from hyperspectral (e.g., PRISMA, EnMAP, DESIS) and thermal data (e.g., ECOSTRESS, Landsat) to detect vegetation anomalies and identify whether they may be related to biotic or abiotic stress factors. In this context, water stress as a main abiotic stress was considered. At this aim we investigated the possibility to exploit PRISMA combined with the ECOSTRESS data to derive evapotranspiration (ET). Even though LST products are available and appear of good quality, the lack of ancillary data prevents ET products to be generated timely. 30-m ET map was produced by combining of ECOSTRESS and PRISMA data and used for water stress estimation. The resulting products proved to be sufficiently accurate to describe the crop water stress at the field scale.

Land cover, or the biophysical cover of the earth's surface, plays an essential role in climate and environmental dynamics. Processes involving land cover change, are among the factors that most threaten the ecosystems sustainability and services. The objective of the work is to explore the potential of the PRISMA multi-temporal hyperspectral imagery in generating new EO products to complement/improve the products provided by Copernicus' Land Monitoring Service for the analysis and monitoring of complex and fragile ecosystems such as the coastal Metaponto (Southern Italy) by estimating of the land biological and economic productivity loss and land degradation vulnerability. Preliminary results showed that an improvement in ecosystem mapping is supported by the use of Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) and a hybrid approach to define the vegetation trait, leads to significant improvement in the damage assessment and land degradation assessment.

Soil salinization and calcification in arid lands facilitate the desertification process, reduce the nutrient availability for plants, and disturb ecological equilibrium. The achievement of the perspectives of spatial variation in electrical conductivity (EC) and calcium carbonate equivalent (CCE) and monitoring based on imaging spectroscopy sensors ensures sustainable and reliable management over large areas. In this study, to evaluate the capability of the PRISMA imagery to predict spatial variations of EC and CCE using machine learning (ML) regression algorithms, 252 soil samples from the Sirjan region in Iran, which is mainly under pistachio cultivation, were used. The highest accuracy for CCE estimation using PRISMA imagery was acquired by the Gaussian Process Regression (GPR) algorithm (R2=0.75, RMSE=4.09), and for EC, it was acquired by the partial least squares regression (PLSR) algorithm (R2=0.64, RMSE=44.8). Moreover, results demonstrate that gypsum abundance is the main confounding parameter impacting the capability of PRISMA for EC estimation.

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.