Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services.

This study is the first attempt to characterize the quality status of El Fahs aquifer by combining graphical tools, multivariate statistical techniques and traditional geostatistical methods. Water samples are collected from thirty-six observation wells during April 2016 to characterize the physicochemical properties of the aquifer. Subsequently, these samples are partitioned into three hydrochemically distinct water classes (i.e., C1, C2, and C3) using the K-means clustering method. Principal Component Analysis is used to reduce the dimensionality of the dataset prior performing the clustering computations, resulting in clusters of higher quality than the non-reduced case in terms of Silhouette coefficient. Piper diagram is used to display the chemical composition of the samples, revealing the dominant role of Mg–Ca–Cl water type for all three classes, whereas Sodium and Sulfate were found to be the second most important cations and anions respectively. Indicator kriging (IK) is used to identify the probability of occurrence of the hydrochemical classes beyond the sampling locations. It is found that Class 1, associated with fresh groundwater component, is most probable to occur at the central part of the plain, mainly due to the presence of a dense hydrological network, whereas Classes 2 (agricultural activities) and 3 (dissolution of evaporate geological formations) are expected to occur at the southern and northern regions respectively. IK also identified the regions associated with high levels of uncertainty, mostly occurring in a large portion of the northern area due to the absence of available hydrochemical information. The results showed that integration of graphical methods, multivariate statistical techniques and geostatistical modeling, is an efficient approach for characterizing the hydrochemical status of the aquifer system, to spatially optimize the groundwater monitoring well networks and quantify the uncertainty levels of the water classes in a systematic way.

Managed aquifer recharge (MAR) is essential for enhancing groundwater storage and ensuring long-term water sustainability, particularly in semiarid regions

This study addresses the challenges of urban flood risk assessment using a holistic flood risk assessment methodology for the catchment area of the Garyllis River (Cyprus). This advanced methodology integrates hydraulic modeling, earth observation and geospatial information analysis, as well as socioeconomic data for risk-informed emergency planning. Two-dimensional hydraulic simulations have been conducted under three different return period scenarios (50, 100 and 1000 years). For the worst-case scenario, the highest water depth reached 6.2 m, most significantly affecting the urban regions in the southern part of the basin. Flood hazard was assessed and combined with both demographic vulnerability indicators (population density and age distribution) and economic exposure data (land economic value). To quantify flood risk, a risk matrix was developed in which hazard, vulnerability and exposure values were synthesized and classified into five risk categories. The results revealed that the highest risk levels are associated with the urban areas, since these areas are characterized by flat terrains, low surface permeability, dense population, and high land economic value. The resulting flood risk maps were used to identify safe emergency shelters and design optimal evacuation routes in the existing road network with adequate traffic capacity. The selection of emergency shelters was focused on public buildings located in zero-to-low risk zones, prioritizing accessibility for vulnerable and at-risk people. In this context, this study delivers a replicable decision-support framework that links hydraulic modeling and Earth Observation with actionable civil protection-oriented strategies, thus enhancing urban flood resilience and disaster preparedness.