The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all water resources in Siwa Oasis and analyzed for physical (pH, T°C, EC, and TDS) chemical (SO₄ ^2-, HCO₃ ^-, NO₃ ^-, Cl^-, CO₃ ^2-, SiO₂, Mg²⁺, Na⁺, Ca²⁺, and K⁺), and trace metals (AL, Fe, Sr, Ba, B, and Mn). A digital elevation model supported by machine learning was used to predict the change in the land cover (surface lake area, soil salinity, and water logging) and its effect on water quality deterioration. The groundwater circulation and interaction between the deep aquifer (NSSA) and shallow aquifer (TCA) were detected from the pressure-depth profile of 27 production wells penetrating NSSA. The chemical facies evolution in the aquifer systems were (Ca-Mg-HCO₃) in the first stage (freshwater of NSSA) and changed to (Na-Cl) type in the last stage (brackish water of TCA and springs). Support vector machine successfully predicted the rapid increase of the hypersaline lake area from 22.6 km² to 60.6 km² within 30 years, which deteriorated a large part of the cultivated land, reflecting the environmental risk of over-extraction of water for irrigation of agricultural land by flooding technique and lack of suitable drainage network. The waterlogging in the study was due to a reduction in the infiltration rate (low permeability) of the soil and quaternary aquifer. The cause of this issue could be a complete saturation of agricultural water with chrysotile, calcite, talc, dolomite, gibbsite, chlorite, Ca-montmorillonite, illite, hematite, kaolinite and K-mica (saturation index >1), giving the chance of these minerals to precipitate in the pore spaces of the soil and decrease the infiltration rate. The NSSA is appropriate for irrigation, whereas TCA is inappropriate due to potential salinity and magnesium risks. The best way to manage water resources in Siwa Oasis could be to use underground drip irrigation and combine water with TCA and NSSA.

Environmental degradation is reducing crop productivity in many regions of Egypt. Moreover, unsustainable surface water drainage contributes to salinized soil conditions, which negatively impact crops. Egypt is seeking solutions to mitigate the problem of surface water drawdown and its consequences by exploring renewable and sustainable sources of energy. Geothermal energy and the desalination of saline water represent the only solutions to overcoming the fresh water shortage in agricultural industry and to providing sustainable fresh water and electricity to villages and the Bedouin livelihood. In Egypt, the Siwa Oasis contains a cluster of thermal springs, making the area an ideal location for geothermal exploration. Some of these thermal springs are characterized by high surface temperatures reaching 20 °C to 40 °C, and the bottom-hole temperatures (BHT) range from 21 °C to 121.7 °C. Pre-Cambrian basement rocks are usually more than 440 m deep, ranging from 440 m to 4724.4 m deep. It is this feature that makes the Siwa Oasis locality sufficient for geothermal power production and industrial processes. This study utilized both the Horner and the Gulf of Mexico correction methods to determine the formation temperatures from BHT data acquired from 27 deep oil wells. The present study revealed a geothermal gradient ranging from 18 to 42 °C/km, a heat flux of 24.7–111.3 mW/m², and a thermal conductivity of 1.3–2.65 W/m/k. The derived geothermal, geophysical, and geological layers were combined together with space data and the topographic layer to map relevant physiographic variables including land surface elevation, depth to basement, lineament density, land surface temperature, and geologic rock units. The ten produced variables were integrated in GIS to model the geothermal potential map (GTP) for the Siwa Oasis region. According to the model, both the eastern side and north and northeastern portions of the study region contain high and very high geothermal potential energy. Combining bottom-hole temperature measurements with satellite remote sensing and geospatial analysis can considerably enhance geothermal prospecting in Egypt and other East African areas that have geologically and tectonically similar settings. In addition to identifying sustainable resources needed for food production, this research has implications for renewable energy resources as well.

The Gulf of Suez area is one of the most favorable regions in Egypt for geothermal exploitation since it hosts an evident cluster of superficial thermal springs. Some of these thermal springs include Hammam Musa, Hammam Faraun, Sudr, Ayn Musa and Ain Sokhna, which are characterized by high temperatures ranging between 35 °C to 86.66 °C. It is this feature that makes the Gulf of Suez locality sufficient for geothermal power production. Corrected bottom hole temperature (BHT) data from 197 oil wells situated onshore and offshore of the Gulf of Suez were utilized for the present research. The results indicated that the study area has a geothermal gradient ranging from 24.9 to 86.66 °C/km, a heat flow ranging from 31 to 127.2 m W k⁻¹, a thermal conductivity of 2.6–3.2 W m⁻¹ k⁻¹, and an amplitude temperature varying from 49.48 °C to 157.8 °C. The derived geothermal and geological layers were used together with the remote sensing thermal infrared and topographic data, to map relevant physiographic variables including surface elevation, fractures density, drainage density, nighttime land surface temperature and major lithological units. The nine produced variables were integrated in GIS to model the geothermal potential map (GTP) for the Gulf of Suez region. The model identifies the northeastern and the southwestern areas as equally two sites for high geothermal potential. Findings of this study demonstrate that integration of well logging and space data with the adopted geospatial techniques is a practical method for geothermal prospecting in similarly geologic and tectonic setting in Egypt and East Africa.

Arid coastal regions are threatened by land desertification, which poses a serious threat to desert ecosystems, urban areas, and sustainability on a local as well as global scale. The present study aims to map desertification and the degree of its severity over the Jazan province on the western coast of Saudi Arabia. This investigation was conducted through the integration of remote sensing data (2001 and 2020) and statistical techniques. A scatter diagram, Karl Pearson correlation coefficient, and significance p-value test were performed on various spectral indices and tasseled cap transformation (TCT) derivative matrices to determine the strong significant relation of the spectral indices combination. Based on these analyses, the desertification degree index (DDI) was developed using a NDVI–TCG combination. The desertification grades were mapped and categorized into five classes, namely, non-desertification, low, moderate, severe, and extreme desertification. The results indicated that the spatial distribution of desertification grades declined from west to east during the period from 2001 to 2020. The degree of desertification improved during the study period since there was a significant reduction in extremely serious desertification land by 15.5% and an increase in weak desertification land by 7.8%. The dynamic changes in the DDI classes in the Jazan province mainly involve transformation from extremely serious to serious, serious to moderate, and moderate to weak, with areas of 2268.1 km², 1518.5 km², and 1062.5 km², respectively. Generally, over the 19-year period, the restoration of vegetated areas accounted for 41.99% of the total study area, while desertification degradation land represented 15.57% of the total area of the Jazan province.

The Nile River has played a central role in Egypt’s historical and cultural development, shaping ancient civilizations and settlement patterns. However, its course has changed dynamically over millennia, leaving behind buried channels and geomorphological features that are critical for reconstructing past hydrological landscapes. This study utilized Sentinel-2 satellite imagery within Google Earth Engine to develop a remote sensing method for analyzing spectral and temporal variations in vegetation as indicators of paleofluvial landforms and past river activity. The approach, applied to create ten seasonal representations, enhanced the detection of moisture-driven vegetation patterns. Here, the Moisture-Gradient Enhanced Vegetation Index (MGEVI) was developed to identify stable vegetated landforms and differentiate persistent moisture conditions from seasonal variations. Through this method, former river channels, river islands, and channel belts were identified, revealing patterns of past river activities. The results suggest a late anabranching phase of the Nile, characterized by the gradual stabilization of fluvial features in response to evolving hydrological conditions. A comparison between fluvial features identified through remote sensing and those mapped from TanDEM-X radar elevation data and historical maps revealed strong agreement, affirming the reliability of the remote sensing approach developed by this study. Evidence from sediment core analyses, stratigraphic correlation, and high-precision RTK field surveys further corroborated the existence of ancient, buried channels and islands within the study area. The study highlights the utility of multi-temporal satellite imagery analysis for reconstructing hydrological evolution and assessing past settlement suitability. Specifically, an inferred paleochannel near the Dendera Temple Complex suggests a possible hydrological connection between a former course of the Nile River and this archaeological site. These findings underscore the potential of remote sensing for large-scale geoarchaeological studies, offering scalable methodologies for identifying ancient river networks and supporting cultural heritage conservation in arid regions.