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This study presents a comprehensive analysis of mineralization exploration in the Egyptian Eastern Desert (ED), one of the most sought-after areas for those interested in mining industry, by integrating Landsat-9 images and geophysical magnetic data. Employing advanced techniques like Principal Component (PC) analysis, Minimum Noise Fraction (MNf) transform, and Band-Ratio (B-Ratio), the research focuses on mapping lithological units, hydrothermal alteration regions, and structural elements. Composite images derived from specific PC, and MNf bands, and B-Ratio exhibit superior lithological unit identification. The findings emphasize that there are significant variations in the types of rocks extend from the southern to the northern parts of the ED. Hydrothermal alteration mapping, guided by B-Ratio results, aids qualitative lithological discrimination. A novel false color composite image optimizes Landsat-9 B-Ratios, enhancing rock unit discrimination. Correlation analyses reveal associations between mineralization types and major lithological units, while exploration of the magnetic anomaly map highlights its role in correlating mineralization sites. Structural features, analyzed through Center for Exploration-Targeting Grid-Analysis (CET-GA) and Center for Exploration-Targeting Porphyry-Analysis (CET-GA) with Tilt Derivative of RTP (TDR) techniques, contribute to a robust association between regions with medium to high structural density and porphyry intrusions and mineralization. The study significantly supports the advanced exploration geoscience, providing insights into the geological structures and dynamics governing mineralization in the Egyptian ED.

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2 = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption.

Assessing the dynamics and patterns of Land Use and Land Cover (LULC) and its transformation is an important practice of urban planners and environmentalists for a variety of applications, including land management, urban climate modeling, and sustainability of any urban region. Monitoring changes in LULC using geospatial techniques can help to identify areas at risk for indefensible land use, low-grade environment, and especially for sustainable urban planning. This study aims to analyze the changing pattern, dynamics, and alteration of LULC using Google Earth Engine (GEE) and Machine Learning Applications for the years 1991, 2001, 2011, and 2022 in the Varanasi City Development Region (VCDR). The LULC classification was divided into seven classes using random forest classification, and Landsat-5(TM) and 9(OLI-2) satellite data were used. Saga GIS has been utilized for the detection of LULC change during the 1991-2022 period. For validation of classification results, accuracy assessment was estimated using error matrices and through user, producer, and overall accuracy estimation. The Kappa statistics were applied for the reliability of the accuracy assessment result. As a result, the built-up area increased by 507.8 percent, and other classes like agricultural, barren, fallow land, and vegetation cover rapidly declined and altered into concrete areas over the period. Water bodies and river sand classes have been slightly converted into different classes. The finding explains that 114.8 km2 of fertile agricultural land, 14.81 km2 barren land, and 12.93 km2 of vegetation cover transformed into impervious surface, which is unsustainable and causes various problems like food scarcity, environmental degradation, and low quality of urban life. This study can be a useful guide for urban planners, academicians, and policymakers by providing a scientific background for sustainable urban planning and management of VCDR and other cities as well.

This study addresses the long-standing challenge of accurately distinguishing ophiolitic rock units in the structurally complex terrains South Eastern Desert of Egypt’s. By integrating multispectral satellite data from Sentinel 2 and Landsat 9 (OLI 2) with substantial field investigation and petrographic analyses, this research proposes an enhanced methodological framework for precise lithological mapping of the Wadi Ghadir ophiolitic complex. Advanced enhanced digital image processing methods for False Color Composites (FCC), Band Ratios (BR), Minimum Noise Fraction (MNF), Principal Component Analysis (PCA), and Maximum Likelihood (ML) classification were systematically applied and evaluated. A newly proposed band ratio combination of Landsat-9 (6/7, 6/5, 6/3) proved highly effective in differentiating serpentinite, talc-carbonate, metagabbro, sheeted dykes, and pillow lavas, while PCA and MNF transforms improved separation of granitic, metasedimentary, and volcanic units. The resulting 1:25,000 scale geological map demonstrates the innovation of integrating complementary satellite sensors with ground validation to overcome spectral confusion and mapping limitations typical of Neoproterozoic ophiolitic terranes. This integrated workflow enhances geological interpretation accuracy and provides a cost effective, reproducible approach for regional mineral exploration and mapping in similar environments.

This study aims to explore the presence and distribution of gold deposits in the Gabal Abu Karahish area by identifying hydrothermal alteration zones associated with favorable geological settings. The objective is to assess gold potential through an integrated remote sensing and geochemical approach. Multispectral satellite data from ASTER and Landsat-9, combined with radiometric data and field geology, were utilized to delineate alteration zones indicative of mineralization. ASTER band ratios (7/6, 4/6, and 9/8) and Landsat-9 false color composites were processed to enhance lithological discrimination and detect hydrothermal alterations. Automated lineament extraction was also performed to evaluate structural controls on mineralization. Several alteration zones of argillic, phyllic, and propylitic types were identified and are spatially associated with alteration minerals such as chlorite, calcite, kaolinite, sericite, and iron oxides. Scanning electron microscopy (SEM) analysis of ten representative samples from alteration zones and quartz veins in metavolcanic and ultramafic rocks confirmed the presence of gold in all samples, with concentrations ranging from 0.23 to 0.83 g per 50 g of rock powder. These findings highlight key zones for further gold exploration. Geologically, the area is composed of calc-alkaline metavolcanic rocks, Dokhan volcanic rocks, serpentinites, talc carbonates, hornblende gabbros, tonalite, granodiorite, and younger granite intrusions. The lithological diversity and structural features, including listwanite ridges and overthrust contacts, further support the area’s mineral potential.

The Wadi Shait–Wadi Gemal district, within the Hafafit metamorphic dome of Egypt’s South Eastern Desert, represents a structurally complex segment of the Arabian Nubian Shield where garnet-bearing rocks are difficult to distinguish using conventional mapping. This study addresses this issue by integrating multi-sensor remote sensing (Landsat-9 and ASTER) with detailed field and petrographic investigations. Advanced image processing techniques, including false color composites (FCC), principal component analysis (PCA), minimum noise fraction (MNF), and proposed band ratios (BRs), were applied to enhance lithological discrimination and improve the detection of garnetiferous units. Field and petrographic analyses confirmed three principal garnet-bearing lithologies: garnet–muscovite–biotite schists, psammitic gneisses, and pegmatites. Garnet porphyroblasts were observed within quartz-mica matrices, reflecting a complex metamorphic history involving multiple deformation phases. The integration of remote sensing with petrological data allowed the production of a refined geological map and the precise delineation of garnet-enriched zones. This combined approach proved effective in resolving lithological complexities and significantly improves the understanding of garnet distribution in Precambrian metamorphic terranes.

The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of the machine learning algorithms XGBoost, SVM, and RF using data from the Sentinel-2 and Landsat-9 satellites. The study assesses the influence of the significant spectral bands and vegetation indices on the accuracy of the AGB estimate. The results presented in the paper indicate that Sentinel-2 data were more effective than Landsat-9 data. This is mainly because it had higher spatial and spectral resolution, which enabled the model vegetation gradients and structural attributes more accurately. The XGBoost model performed the best with an R 2 of 0.82 and RMSE of 0.73 Mg/ha with Sentinel-2 and R 2 of 0.80 and RMSE of 0.71 Mg/ha with Landsat-9. In the current study, SVM also showed a substantial accuracy with an R 2 of 0.79 and RMSE of 0.73 Mg/ha for Sentinel-2 and R 2 of 0.76 and RMSE of 0.80 Mg/ha for Landsat-9. For Sentinel-2, the random forest achieved an R 2 of 0.74 and an RMSE of 0.93 Mg/ha, and Landsat 9 yielded an R 2 of 0.72 and an RMSE of 0.88 Mg/ha. Thus, using variable importance analysis, the results showed that vegetation indices and spectral bands have higher importance in predicting AGB. As expected from their application in biomass research, these predictors consistently emerged as highly significant across models and datasets. This study demonstrates the potential of integrating machine learning with remote sensing data to achieve accurate and efficient biomass assessment.

Precise identification and spatial analysis of land salinity in China’s Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran’s I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6–8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high–high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low–low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies.