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The Fawakheir-Attala gold mining zone in Egypt’s Central Eastern Desert features a diverse range of rock formations, including Precambrian crystalline rocks and Phanerozoic sedimentary formations. These formations encompass gneisses, metavolcanics, metasediments, a metagabbro-diorite complex, Dokhan volcanic rocks, and granitic rocks, alongside lower and upper Nubia sandstones. Faults and shear zones are pivotal in controlling gold mineralization within the area, indicative of substantial mineral wealth. This study aimed to map subsurface structural characteristics and investigate gold mineralization zones using aerial magnetic data and ASTER remote sensing, the latter of which played a crucial role in highlighting the surface exposure of alteration zones. Geological surveys combined with remote sensing techniques were employed to identify rock types and mineralization zones, while magnetic methods, including aeromagnetic surveys and ground-based studies, were used to reveal underlying structural properties and fault systems. Analysis of aeromagnetic data revealed a large mineralization zone running from the Fawakhir Gold mine through the Attala Gold mine in a NW–SE direction. Various structural trends and faults, including NW–SE, NE–SW, E–W, and N–S directions, were identified, strongly associated with hydrothermal alteration zones and gold mineralization. Shallow basement relief was observed in the eastern and central regions, contrasting with deeper formations and greater relief in the western section. Land magnetic surveys were utilized to identify new areas for gold mineralization, with geochemical analysis confirming gold content in quartz veins and host rocks. The integration of magnetic and remote sensing techniques effectively highlighted alteration zones indicative of potential mineralization, which could have a subsurface continuation, aiding in the identification of gold occurrences connected to faults, lineaments, and mineralization zones.

Soil erosion and groundwater resources are two fundamental global concerns intricately linked through various hydrological and morphometric processes. Morphometrics with soil erosion assessment is crucial for managing hydrological processes and implementing preventative strategies. Utilizing Geographical Information system and Remote Sensing techniques, morphometric, morphotectonic, and soil erosion susceptibility in the tectonically active Hunza-Nagar catchment were explored, spanning 1455.05 km 2 with elevations from 1763–7697 m above sea level. With this motive, linear, areal, and relief morphometric variables were investigated. Analysis of the linear aspects indicated the sub-dendritic drainage pattern with streams ordered from 1 to 4th order. The calculated parameters recorded huge variations, including stream length of 384.92 km, bifurcation ratio of 1.65, drainage density of 2.65 km/km 2 , drainage intensity of 0.25 km −1 , drainage texture of 0.49, stream frequency of 0.07 km −2 and form factor of 0.41, respectively. The circulatory ratio of 0.46 indicates structural influence, elongation ratio of 0.72 reflects moderate to steep slopes with low flood regimes, length of overland flow of 1.33 km shows high infiltration and shape index of 2.47 underscores a higher risk of soil erosion in the catchment. Soil erosion susceptibility analysis was conducted using the XGBoost model, renowned for its proficiency in predictive modeling and classification tasks. The model was trained and tested on a dataset comprising factors pertinent to soil erosion dynamics. Subsequently, the trained model was applied to assess soil erosion susceptibility across the study area. The final Susceptibility map was classified from low to very high susceptible zones. Confusion matrix and Receiving operative characteristic curve (ROC) were used to validate the model. These results offer crucial insights into geohydrological characteristics, supporting global conservation efforts in conservation of natural resources and soil practices.

Since its impoundment in 2003, more than 5,000 landslides have been identified in the Three Gorges Reservoir, and more than 600 slides have apparent activity, which causes significant damage and threats to residents and water infrastructure. Understanding the kinematic behavior and velocity characteristics, mechanisms, trigger factors, and dynamic models of landslides contribute to their instability evaluation and prevention. However, landslide stability analysis is challenging because of complex influencing factors and unclear structural features. The primary objectives of this study were to investigate the kinematic, mechanical, and dynamic characteristics of the Chuwangjing landslide and to identify the trigger factors. We applied multi-resource remote sensing techniques, including satellite Tri-Stereo, unmanned aerial vehicle (UAV) surveys, light detection and ranging (LiDAR) point clouds, and interferometric synthetic aperture radar (InSAR) techniques, to analyze morphological, kinematic, and dynamic features, combined with meteorological and hydrological data. The increased velocity during periods of intense rainfall and prolonged water function, particularly during periodic rapid drawdown periods at high water levels, indicates that deformation is primarily governed by these two factors. The composition of cracks and scrapes detected by LiDAR and satellite Tri-Stereo technology and the deformation distribution on the slope indicated a retrogressive model. We analyzed the landslide’s kinematic model and dynamic conditions by considering characteristics such as step-like deformation, influencing factors, and geological composition. Furthermore, by comparing the application effects of multi-remote sensing technology combinations in landslide analysis, this study proved the usefulness of an integrated method for landslide analysis and trending evaluation.

The aim of this study was to determine the quality and quantity composition of epiphytic algae of Phragmites australis in Um El-Naaj lake. Samples were collected from six Stations from November 2018 to October 2019. During this study, a remote sensing techniques was used to monitor the Community of epiphytic algae through storage and management data the geographic information system (GIS) program. The satellite images of the Landsat 8 satellite of the OLI which used to create visual images that facilitate the interpretation of the study area to identification and presence of these species and control their spread. The total number 306 species of epiphytic algae recorded during the study period were, belong to 61 genera. Seasonal variations in total number of species also noticed during the current study, Bacillariophyceae recorded 52.28%, with (160 species of 28 genera), Classes of Chlorophyceae and Cyanophycea recorded 12.67% (39 species of 20 genera 11) and 11.76% (36 species of 12 genera) respectively, while Classes of Fragilarphyceae recorded 14.37% ( 44species of 7genera), Coscinodisphyeceae recorded 13.59% (11 species, 7 genera). Then class of Euglenophyceae with 3.59%., Finally Chrysophyceae, Dinophyceae, and Rhodophyceae recorded 0.32% with 1 species of 1genera as they were present.

Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC) project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity losses Estimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacity Potential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters