Hyperspectral imagery plays a vital role in precision agriculture, forestry, environment, and geological applications. Over the past decade, extensive research has been carried out in the field of hyperspectral remote sensing. First introduced by the Italian Space Agency ASI in 2019, space-borne PRISMA hyperspectral imagery (PHSI) is taking the hyperspectral remote sensing research community into the next era due to its unprecedented spectral resolution of ≤12 nm. Given these abundant free data and high spatial resolution, it is crucial to provide remote sensing researchers with information about the critical attributes of PRISMA imagery, making it the most viable solution for various land and water applications. Hence, in the present study, a SWOT analysis was performed for PHSI using recent case studies to exploit the potential of PHSI for different remote sensing applications, such as snow, soil, water, natural gas, and vegetation. From this analysis, it was found that the higher reflectance spectra of PHSI, which have comprehensive coverage, have greater potential to extract vegetation biophysical parameters compared to other applications. Though the possible use of these data was demonstrated in a few other applications, such as the identification of methane gases and soil mineral mapping, the data may not be suitable for continuous monitoring due to their limited acquisition, long revisiting times, noisy bands, atmospheric interferences, and computationally heavy processing, particularly when executing machine learning models. The potential applications of PHSI include large-scale and efficient mapping, transferring technology, and fusion with other remote sensing data, whereas the lifetime of satellites and the need for interdisciplinary personnel pose challenges. Furthermore, some strategies to overcome the aforementioned weaknesses and threats are described in our conclusions.

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input.

Mediterranean forests are gravely affected by wildfires, and despite the increased prevention effort of competent authorities in the past few decades, the yearly number of fires and the consequent damage has not decreased significantly. To this end, a number of dynamical methods have been developed in order to produce short-term hazard indices, such as the Fire Probability Index and the Fire Weather Index. The possibility to estimate the fire hazard is based on the observation that there is a relationship between the characteristics of the vegetation (i.e., the fuel), in terms of abundance and moisture content, and the probability of fire insurgence. The density, type, and moisture content of the vegetation are modeled using custom fuel maps, developed using the latest Corine Land Cover, and using a number of indices such as the NDVI (Normalized Difference Vegetation Index), Global Vegetation Moisture Index (GVMI), and the evapotranspiration, derived from daily satellite imagery. This paper shows how the algorithm for the calculation of the Fire Potential Index (FPI) was improved by taking into account the effect of wind speed, topography, and local solar illumination through a simple temperature correction, preserving the straightforward structure of the FPI algorithm. The results were validated on the Italian region of Sardinia using official wildfire records provided by the regional administration.

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT.

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.

The elaboration of a methodology for accurately assessing the potentialities of blue renewable energy sources is a key challenge among the current energy sustainability strategies all over the world. Consequentially, many researchers are currently working to improve the accuracy of marine renewable assessment methods. Nowadays, remote sensing (RSs) satellites are used to observe the environment in many fields and applications. These could also be used to identify regions of interest for future energy converter installations and to accurately identify areas with interesting potentials. Therefore, researchers can dramatically reduce the possibility of significant error. In this paper, a comprehensive SWOT (strengths, weaknesses, opportunities and threats) analysis is elaborated to assess RS satellite potentialities for offshore wind (OW) estimation. Sicily and Sardinia—the two biggest Italian islands with the highest potential for offshore wind energy generation—were selected as pilot areas. Since there is a lack of measuring instruments, such as cup anemometers and buoys in these areas (mainly due to their high economic costs), an accurate analysis was carried out to assess the marine energy potential from offshore wind. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome this limitation. Undoubtedly, with the advent of new technologies for measuring renewable energy sources (RESs), there could be a significant energy transition in this area that requires a proper orientation of plans to examine the factors influencing these new technologies that can negatively affect most of the available potential. Satellite technology for identifying suitable areas of wind power plants could be a powerful tool that is constantly increasing in its applications but requires good planning to apply it in various projects. Proper planning is only possible with a better understanding of satellite capabilities and different methods for measuring available wind resources. To this end, a better understanding in interdisciplinary fields with the exchange of updated information between different sectors of development, such as universities and companies, will be most effective. In this context, by reviewing the available satellite technologies, the ability of this tool to measure the marine renewable energies (MREs) sector in large and small areas is considered. Secondly, an attempt is made to identify the strengths and weaknesses of using these types of tools and techniques that can help in various projects. Lastly, specific scenarios related to the application of such systems in existing and new developments are reviewed and discussed.