Over the last decades, remote sensing techniques have contributed to supporting cultural heritage studies and management, including archaeological sites as well as their territorial context and geographical surroundings. This paper aims to investigate the capabilities and limitations of the new hyperspectral sensor PRISMA (Precursore IperSpettrale della Missione Applicativa) by the Italian Space Agency (ASI), still little applied to archaeological studies. The PRISMA sensor was tested on Italian terrestrial (Alba Fucens, Massa D’Albe, L’Aquila) and marine (Sinuessa, Mondragone, Caserta) archaeological sites. A comparison between PRISMA hyperspectral imagery and the well-known Sentinel-2 Multi-Spectral Instrument (MSI) was performed in order to better understand features and outputs useful to investigate the aforementioned areas. At first, bad bands analysis and noise removal were performed, in order to delete the numerically corrupted bands. Principal component analysis (PCA) was carried out to highlight invisible details in the original image; then, spectral signatures of representative areas were extracted and compared to Sentinel-2 data. At last, a classification analysis (ML and SAM) was performed both on PRISMA and Sentinel-2 imagery. The results showed a full agreement between Sentinel and PRISMA data, enhancing the capability of PRISMA in extrapolating more spectral information and providing a better reliability in the extraction of the features.

<p>As global warming continues to be one of the greatest threats to Earth environment, the detection and monitoring of natural and anthropogenic emissions of greenhouse gases holds a critical role as the first step of any danger reduction policy. New generation spaceborne hyperspectral instruments cover large portions of the Earth while maintaining a high enough spectral and spatial resolution to investigate the contribution of single molecular species and accurately localize their emission source. The Matched Filter method is used to search enhanced concentrations of methane in the atmospheric column. PRISMA, ASI’s newest hyperspectral sensor, data are analysed. Both strong and weak CH<sub>4</sub> emissions, in multiple scenarios, are investigated. It is demonstrated that PRISMA data allow also the identification of methane non-punctual sources when the land gas emission is very high. An estimated flux in the order of 4000 kg/h is found for a case study considering a landfill in India.</p>

<p>Cloud identification from satellites is considerably challenging in polar environments due to the similar radiative properties of surface and ice clouds, making it difficult to detect and distinguish cloud features. CIC (Cloud Identification and Classification) is a machine learning algorithm adopted as the official software in the ESA Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) (Palchetti et al., 2020) End2End simulator (FE2ES). CIC is based on Principal Component Analysis and performs cloud detection and multi-scene classification. It is adaptable to every type of sensor and is particularly suitable when a small number of elements are available for the Training Set. Assessment studies have already been conducted to evaluate the performances of the algorithm in multiple conditions. In Maestri et al. (2019), CIC was applied to simulated radiance all over the globe, while Magurno et al. (2020) used the algorithm to analyze airborne interferometric spectra. Finally, in Cossich et al. (2021) the algorithm was tested on downwelling radiances collected at Dome-C in Antarctica. In this work, CIC is applied to high spectrally resolved data taken from ground and, for the first time, from satellites. Ground-based data are collected by the REFIR-PAD sensor (Di Natale et al., 2020), covering the far and mid-infrared part of the spectrum. Collocated satellite data are measured by IASI (Infrared Atmospheric Sounding Interferometer) which collects upwelling radiance between 3.4 and 15.5 μm. The period under study spans from 2012 to 2022. CIC results applied to ground-measured spectra are compared to IASI’s L2 classification products. Large discrepancies between the two classifications are observed, indicating an overestimation of the cloud occurrence in case of IASI. A verification is obtained using collocated ground-based LIDAR measurements, which are available for subsets of the collocated radiances. Finally, the CIC algorithm is trained with a subset of IASI data collocated with REFIR-PAD and LIDAR measurements. The training set is defined also with the help of the Advanced Very High Resolution Radiometer (AVHRR) on board of MetOp satellites. The AVHRR has 1 km resolution (at the nadir) and its collocated measurements are used to evaluate the scene homogeneity in the satellite field of view. Statistical analyses are then performed on IASI spectra using the CIC classification. Results indicate a much better agreement with ground-based data, improving the cloud occurrence provided in IASI L2 products.</p>