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The post-tectonic granites and pegmatites of the Abu Rusheid-Sikait area, in the South Eastern Desert of Egypt, represent highly mineralized plutons within the Arabian Nubian Shield (ANS). This study integrates remote sensing datasets, field work, petrographic, and geochemical analyses to investigate these plutons. Machine learning algorithms (MLAs), including Support Vector Machine (SVM) and Random Forest (RF), applied to Minimum Noise Fraction (MNF)-enhanced PRISMA data, successfully discriminated lithological units with an overall accuracy of up to 89%. Spectral analysis identified four main hydrothermal alteration zones (phyllic, argillic, propylitic, and ferrugination), which were validated by field and laboratory data. Geochemically, the post-collisional granites show geochemical characteristics of highly evolved peraluminous A-type granites generated in a within-plate environment similar to other A-type granites in the ANS. All the studied granites display negative Eu/Eu* (0.02–0.19) anomalies, reflecting plagioclase fractionation. The geochemical similarities between the pegmatites and the surrounding granites support their genetic relation. Zinnwaldite-muscovite and garnet-muscovite granites, along with associated pegmatites, are highly enriched in rare earth and high field strength elements (e.g., ΣREEs up to 1310 ppm, Zr up to 4477 ppm, Nb up to 1500 ppm, Ta up to 216 ppm, U up to 411 ppm). This mineralization occurs as both disseminated accessory minerals within host granitoids and concentrated along structurally controlled zones that are affected by the Nugrus shear zone, faults (NW-SE, N-S, and NE-SW), and fractures, as confirmed from automatic surface structure lineament extraction maps, field work, and petrography. The enrichment is primarily controlled by extreme magmatic fractionation, which produced primary rare metal-bearing minerals (zircon, columbite, xenotime, monazite). This magmatic signature was subsequently overprinted by hydrothermal alteration, which redistributed and further concentrated metals along structurally controlled pathways concentrated metals along brittle fracture zones formed by the late-stage reactivation of the Najd Fault System, forming secondary minerals like kasolite and galena. Ferrugination is considered the main alteration related to uranium and REEs remobilization and concentration in specific locations, sometimes alongside iron oxides (hematite, goethite). This work presents an integrated model that links magmatic processes, structural controls, and hydrothermal alteration, providing a valuable framework for rare metal exploration in the ANS.

The El-Bahariya depression in the Western Desert of Egypt is well-known for its iron ore deposits, with mineralization recorded in five well-known locations. This study is the first integration of hyperspectral PRISMA and aeromagnetic analysis for the Bahariya iron ore deposits. Automated lithological mapping of the study area was performed using machine learning algorithms (MLA) such as Random Forest (RF) and Support Vector Machine (SVM), applied to stacked (ASTER + Sentinel-2) data, achieving an overall accuracy of up to 91.73%, Kappa accuracy of 89.99%, and F1-score of 93.98%. Hyperspectral PRISMA data analysis identified diagnostic absorption features (1.95–2.3 μm) associated with hematite, goethite, and limonite. Advanced spectral methods, including Pixel Purity Index (PPI) and Spectral Angle Mapper (SAM), successfully discriminated between hematite and limonite concentrations, as validated by field surveys, an ASD spectroradiometer, and USGS laboratory spectra. Results revealed ferruginous sandstone distributions, iron-rich zones, and previously undetected high-potential mineralization targets. Complementary high-resolution aeromagnetic data, processed using edge detection filters and CET techniques (CET-GA, CET-PA), resolved structural controls on mineralization. Major NE-SW and NW-SE trending lineaments, alongside minor N-S and E-W structures, were identified as fluid conduits for hydrothermal iron oxide emplacement. Euler deconvolution constrained magnetic sources to shallow depths (< 2 km), aligning with surface-derived anomalies. High magnetic susceptibility zones correlated strongly with remote sensing-identified iron-rich areas, including known mines, and highlighted unexplored anomalies in the northwestern and central regions. Geochemically, we identified economic ore-grade ironstones distinct (FeO t = 26–46 wt%, MnO < 0.11 wt%, P₂O₅ 0.45–0.84 wt%) from non-economic Mn-rich carbonate lenses (MnO 4.85–7.76 wt%) and barren siliceous rocks. The integration of spectral and magnetic datasets confirmed the spatial coherence of mineralization signals, along with the detection of new high-potential zones for iron mineralization, demonstrating the synergistic utility of these datasets in defining exploration targets. The proposed methods were highly effective in mapping iron oxide deposits within the four major zones: Nasser, Gabal-Ghurabi, El-Gadidah, and El-Harrah.

The Gabal El-Faraid Group, in the Egyptian Southeastern Desert, comprises Neoproterozoic lithologies and structurally controlled mineralization zones that require an integrated exploration approach. This study integrates multi-source remote sensing data with detailed field observations, structural analysis, and petrographic investigations to improve lithological differentiation and delineate mineralization-related structures. Results indicate a potential structural-control spatial association among shear zones, hydrothermal alteration zones, and mineralization, suggesting their potential role in controlling mineralization. Support Vector Machine (SVM) and Random Forest (RF) algorithms have been used to best delineate lithological rock units of the study area. Accurate lithological mapping was achieved using an SVM applied to combined Sentinel-2 and ALOS PALSAR datasets, yielding an overall accuracy of 92.67%, a Kappa coefficient of 0.8928, and an F1-score of 88.64%. Six spectral mineral indices, like argillic, clay, ferrous silicates, ferrugination, hydroxyl, and phyllic alterations, have been emphasized using the hyperspectral PRISMA dataset. Exposed rocks comprise metavolcanics, metagabbro-diorite, tonalite-granodiorite, monzogranite, syenogranite, pegmatite, and late basic dikes, representing a multiphase magmatic sequence spanning arc-related to post-collisional stages. Structural investigations indicate a polyphase deformation history comprising four major events (D1-D4). The earliest phase (D1) is characterized by penetrative foliation, stretching lineation, and low-angle thrusting associated with nappe transport during intra-oceanic arc accretion. D2 reflects large-scale folding and regional shortening related to arc-to-continent collision. The D3 phase records transpressional strike-slip shearing, reactivating earlier structures and facilitating magma ascent, whereas D4 is marked by brittle faulting and jointing linked to post-orogenic uplift and extensional tectonics. Lineament analysis from PALSAR data highlights dominant NW-SE and NE-SW trends that closely correspond to field-measured fabrics and shear zones. Integration of field and remote sensing datasets demonstrates that late-stage deformation (D3-D4) exerted strong structural control over pegmatite emplacement, quartz veining, and hydrothermal alteration, with a main set trending WNW to NW and a less prominent set with pronounced ENE and NNW trends, emphasizing the role of inherited Pan-African structures in localizing mineralization. High-priority prospective zones in the studied granites were derived from integrated datasets, which were systematically combined in ArcGIS using a fuzzy-logic overlay. The detected zones are labeled as very high, high, moderate, low, and very low based on their potential for rare-metal mineralization. The integrated datasets can be applied to similar areas in the Eastern Desert of Egypt and across the entire Arabian-Nubian Shield (ANS).

The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data.

Nearshore bathymetric data are essential for assessing coastal hazards, studying benthic habitats and for coastal engineering. Traditional bathymetry mapping techniques of ship-sounding and airborne LiDAR are laborious, expensive and not always efficient. Multispectral and hyperspectral remote sensing, in combination with machine learning techniques, are gaining interest. Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutional neural networks. The U-Net, trained on 49 Sentinel-2 images, and the 2D-3D CNN, trained on PRISMA imagery, had a Mean Absolute Error (MAE) of approximately 1 m for depths up to 20 m and were superior to band ratio models by ~40%. Problems with underprediction remain for turbid waters. Sentinel-2 showed higher performance than PRISMA up to 20 m (~18% lower MAE), attributed to training with a larger number of images and employing an ensemble prediction, while PRISMA outperformed Sentinel-2 for depths between 25 m and 30 m (~19% lower MAE). Sentinel-2 imagery is recommended over PRISMA imagery for estimating shallow bathymetry given its similar performance, much higher image availability and easier handling. Future studies are recommended to train neural networks with images from various regions to increase generalization and method portability. Models are preferably trained by area-segregated splits to ensure independence between the training and testing set. Using a random train test split for bathymetry is not recommended due to spatial autocorrelation of sea depth, resulting in data leakage. This study demonstrates the high potential of machine learning models for assessing the bathymetry of optically shallow waters using optical satellite imagery.

The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.

In-situ water depth measurement is a time-consuming and expensive process for large-scale and frequent monitoring. This underscores the need for alternative methods, such as those based on satellite imagery, which can offer a more efficient and cost-effective solution. This research explores the use of PRISMA hyperspectral images for bathymetry in Nayband Bay, South Iran. The proposed method, SSIP_PSO, includes image pan-sharpening, spectral and spatial information preservation, Particle Swarm Optimization for band ratio selection, and enhanced geometric correction. Among fusion methods tested, the Gram-Schmidt Transform proved most effective for pan-sharpening. The optimal band ratio, b4/b19, improved bathymetry accuracy, reducing root mean square error from 2.291 to 1.716. SSIP_PSO outperformed traditional methods and demonstrated the importance of preserving spatial information in bathymetry.

Accurate prediction and mapping of soil properties are essential in environmentally sustainable land use. Traditional approaches are laborious and costly, however. This article demonstrates the use of Prismatic Imaging Sensor (PRISMA) with a cost-effective, rapid, and environmental-friendly approach of forecasting and mapping the soil pH, electrical conductivity (EC), and calcium carbonate (CaCO 3 ) of New Delta region in Egypt’s Western Desert based on hyperspectral data and machine learning (ML) techniques. The study integrates soil wet chemistry data, hyperspectral reflectance, multivariate regression, and ML models to improve soil property estimation. PRISMA hyperspectral imagery was acquired, processed, and classified to map land use and land cover (LULC). Seventy-four representative bare soil profiles were collected and their pH, EC, and CaCO 3 content were determined. Hyperspectral reflectance data for the samples were reaped, and noisy spectral bands were eliminated to improve data quality and prediction accuracy. Partial least squares regression (PLSR), random forest (RF), multivariate adaptive regression splines (MARS), and support vector regression (SVR) were the different ML models attempted. Spectral band selection was improved by the implementation of competitive adaptive reweighted sampling (CARS); and multiple linear regression (MLR) was used for the development of prediction equations to map soil properties over PRISMA image. Soil condition was found highly variable, ranging from slightly to extremely alkaline pH, non-calcareous to extremely calcareous in CaCO 3 content, and non-saline to highly saline soils. Of the models, PLSR provided the best fit estimates for surface soil pH (R² = 0.1186, RMSE = 3.115, RPD = 0.7902), surface soil EC (R² = 0.2281, RMSE = 1.196 dS/m, RPD = 1.132), surface CaCO 3 content (R² = 0.5984, RMSE = 2.73%, RPD = 1.817), subsurface soil EC (R² = 0.2557, RMSE = 1.7481 dS/m, RPD = 1.124), and subsurface CaCO 3 content (R² = 0.6092, RMSE = 2.32%, RPD = 1.779). RF yielded the highest performance for predicting subsurface soil pH (R² = 0.1517, RMSE = 2.876, RPD = 0.1777). Identification of relevant spectral bands, calibration of the prediction models, and their use in PRISMA imagery resulted in high-resolution maps of soil parameters. These findings are extremely helpful to improve land reclamation planning and the effectiveness of their application.

The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R 2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R 2  > 0.90.

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.