1. Introduction

2. Materials and Methods

Table 1. Indicative EO Applications per thematic area.

Table 2. Population (Po), area (A), per capita Gross Domestic Product (GDP.P.C), and number of author and co-author affiliations (A.A) per country, as percentage of the total number of affiliations and author affiliations per capita (A.A.P.C) for EMMENA countries (measured in ppm).

3. Results and Discussion

3.3. EO Research per Country and per Thematic Area in the EMMENA Region

Big Earth Data is the thematic area with the highest contribution (27.1 %) of EO research done in the EMMENA region (Figure 3). Surprisingly, while most of the EMMENA region countries are surrounded by sea, Marine Safety and Security has the lowest contribution of EO research done in the EMMENA (0.9%). Also, EMMENA region is characterized by long history and Cultural richness [8], but EO applications on Cultural Heritage have a low contribution to the total number of studies (1.4%).

A total of 495 citation topics were identified in Web of Science, of which the first 10 account for 46% of the total number of publications. These topics include Normalized Difference Vegetation Index (NDVI) (9%), groundwater (6%), evapotranspiration (6%), landslides (6%), fuzzy sets (4%), ecosystem services (4%), Aerosols (3%), Geostatistics (3%), soil erosion (3%) and tectonics (2%).

Most publications for EO applications in the EMMENA region used Landsat and Sentinel data, as identified by the keyword search on the publications’ title, keywords and abstract (Table 2). Sentinel-2 data (60.8%) and Sentinel-1 data (35.4%) are the most widely used among the Sentinel missions. Similarly, Landsat 8 (83.2%) and Landsat 7 (10.0%) are the most widely used data sources among the Landsat missions.

The detailed analysis of the EO research done according to the top ten highly cited articles per thematic area will be described in the following subsections.

Table 2. Percentages of satellite missions per appearance in the title, keywords and abstract of EMMENA publications.

Table 2. Percentages of satellite missions per appearance in the title, keywords and abstract of EMMENA publications.

Satellite mission	Title	Keywords	Abstract
Landsat	24.6	28.2	29.8
Sentinel	30.2	25.9	22.6
MODIS	12.6	17.5	14.1
ASTER	9.1	7.9	6.5
GRACE	3.3	3.9	3.4
TRMM	2.4	2.6	4.1
SPOT	1.7	1.2	1.3
WorldView	1.5	1.4	1.4
PlanetScope	1.5	0.7	0.6
Other	12.9	10.7	16.2

3.3.1. Atmosphere

Four out of the 10 highest cited articles in the thematic area of atmosphere focused on the impact of Coronavirus on air quality through EO. Spaceborne NO₂ column observations from two high-resolution instruments, Tropospheric Monitoring Instrument (TROPOMI, Sentinel-5 Precursor) and Ozone Monitoring Instrument (OMI), were used to assess the impact of COVID-19 outbreak on NO₂ pollution on five different regions (China, South Korea, western Europe, USA and Iran) [24]. The results showed 20-40% decrease in NO₂ columns over China, Europe, South Korea, and the United Sates between January and April 2020, due to the lock-down measures implemented by the governments. A likely explanation for the absence of NO₂ decrease in Iran is that complete lockdowns were not enforced in Iran. The authors emphasized that further studies are needed to evaluate the effects of the temporary lockdowns on global air quality and climate, and the gradual return to pre lockdown periods. On the contrary, the study of Broomandi et al. [25] showed that the COVID-19 lockdown positively affected Iran's air quality, as evident by the reduction in the levels of CO, NO₂, SO₂, and PM₁₀. Similar trends of NO₂ reduction during the lockdown were observed for other EMMENA countries, such as Egypt and Iraq [26,27].

The remaining studies on the Atmosphere thematic area focus on precipitation estimates [28,29], improvement of CO2 anthropogenic emission measurements [30], climate change [31,32] and dust storms[33].

Reuter et al. [30] developed a co-location method with the simultaneous use of CO₂ (OCO-2) and NO₂ (Sentinel-5) data for the identification and quantification of anthropogenic emission plume signals. The authors highlighted that existing satellites present high flux uncertainties which are expected to be reduced by the planned European Copernicus anthropogenic CO₂ monitoring mission (CO2M), due to higher spatial resolution and better imaging capabilities.

The two articles which focus on the improvement of precipitation estimates used a combination of modelling applications, multiple data sources (European Centre for Medium-Range Weather Forecasts-ECMWF, Tropical Precipitation Measuring Mission-TRMM), and improved algorithms [28,29].- These studies underlined that future studies should account for uncertainties related to the spatial variability, over regions with complex topography.

The study of Namdari et al. [33] examined the impacts of climate fluctuations on dust storm activity over the Middle East. They used Aerosol Optical Depth, temperature, and precipitation data between 2000 and 2015, obtained from MODIS Collection, Tropical Rainfall Measuring Mission (TRMM; https://pmm.nasa.gov/index.php), Global Historical Climate Network (GHCN; http://www.esrl.noaa.gov/psd/data/gridded/data.ghcncams.html) of the NCDC/NOAA , ECMWF (http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc) and from the Iran Meteorological Organization (IMO). Their results indicate that long-term reduction in rainfall has promoted lower soil moisture and vegetative cover, leading to more intense dust emissions. Furthermore, short term variations in temperature in hot periods exacerbate the influence on the dust storm genesis.

Long-term trends between vegetation and climate showed apparent Climate change effects in Iran [31]. Using 33-year records of NDVI (calculated with data from the Advanced Very High Resolution Radiometer (AVHRR) satellite) and climate variables Lamchin et al. [31] found that the vegetation greenness trend consistently decreased (range=−0.47 to−0.08) in eastern Iran. Similarly, projected Land Use Changes for Saudi Arabia between 2014 and 2100 showed reductions in vegetation due to climate change and anthropogenic activities [32].

3.3.2. Water

The top 10 highly cited articles in the water thematic area focused on mapping groundwater potential and groundwater quality [34,35,36,37,38,39,40,41,42,43]. According to Arabameri et al. [43], due to the constant technical advancements in the field of groundwater potential mapping, there is no current agreement or reference on which approach is more suitable to predict high groundwater potential. However, all studies have in common the use of GIS software and geostatistical methods, including different models and algorithms. The studies used various sources of data (e.g., ALOS PALSAR, ASTER, Landsat, ground-based) related to water quantity and quality, land use/ land cover, soil and geology, digital elevation, morphological (e.g., slope, aspect) and hydrological data (e.g., river network). The choice of the input data depends on the site-specific characteristics of each region. All these studies highlighted that the results will be useful for better water and land management practices and policies.

3.3.3. Agriculture

The EO applications in the thematic area of Agriculture covers a wide range of topics such as evapotranspiration (ET) estimation [44,45], land suitability for agriculture [46,47,48,49], irrigation mapping [50], agricultural land loss [51] and crop monitoring [52,53]. The studies on evapotranspiration were conducted with ET models based on MODIS and Landsat satellite data, in Iran. The authors emphasized the need for high quality in situ data in future studies, to improve the model’s performance. Land suitability studies were conducted both with GIS and multi-criteria decision-making analysis for maize (Iran), soybean (Iran), rapeseed (Iran), barley (Iran) and jojoba (UAE) crops. The studies reported that their findings can help in improving the management of crop production and that future studies should expand on different crops and soil types.

In their study on soil moisture and irrigation mapping in Tunisia, Bousbih et al [50] presented a new technique based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). Their approach is based on the inversion of the Water Cloud Model, using radar data and NDVI data derived from optical images. Their results showed an RMSE of 4.3% and a bias of 1.9% between the satellite and observed data.

Crop growth dynamic observations from satellites are often limited by the coarse spatial and temporal resolutions. To address this issue Nguyen et al.[53] proposed the use of data from different ongoing satellite missions Sentinel-2 (ESA) and Landsat 7/8 (NASA) through harmonization techniques. They found that improved topographic correction models are needed to achieve higher performance of the harmonized products in mountainous areas. Sadeh et al. [52] developed a new method to fuse a time series of images sourced from two different satellite constellations (Sentinel-2, Planetscope) which combines the advantages of each satellite (high temporal, spatial and spectral resolution). This new technique was applied to create spectrally consistent daily images of wheat LAI at a 3 m resolution which were then compared with in-situ wheat LAI measurements from Australia and Israel. Their approach successfully estimated LAI (R² = 0.94) throughout the growing season for LAI < 3.

Urban expansion has critical consequences for agricultural production in the EMMENA region. Radwan et al. [51] analyzed 24 LULC maps from the European Space Agency-Climate Change Initiative (ESA-CCI) land cover viewer (http://maps. elie.ucl.ac.be/CCI/viewer/) and found that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost due to urban expansion from 1992 to 2015 at an average rate of 3108 ha year^-1. Also, 206,100 hectares of bare land was converted to agricultural land at an average rate of 8588 ha year^-1. According to the authors, more sustainable land-use strategies are needed to minimize the threats to agricultural sustainability and food security.

3.3.4. Land

Most of the studies in land thematic areas focus on erosion processes [54,55,56,57,58,59] where various satellite data (e.g. ALOS-PALSAR, Landsat, ASTER G-DEM, Sentinel) ground data, statistical approaches and GIS techniques are applied to create erosion susceptibility maps. However, the selection of data, approaches and techniques depends on the data availability and the site-specific characteristics of each study. All these studies reported high accuracy of the maps and that their results will help towards better land use planning and risk management.

The remaining four studies focused on land use/land cover (LULC) monitoring [60,61], forest biomass estimation [62] and on soil properties mapping [63]. Mansour et al. [61] applied advanced GIS techniques, spatial modelling and utilization of satellite images (Landsat 5/8) to simulate LULC changes and to predict the urban expansion in the mountainous cities of Oman, for the years 2028 and 2038. Their predictions showed 50% increase in the built-up area in 2038; on the contrary, vegetation will decrease by 36.5%. Ghorbanian et al. [60] developed an object-based land cover classification methodology using the Google Earth Engine in order to improve the land cover map of Iran. They used Sentinel-1 and Sentinel-2 imagery data for the fusion of SAR and multi-spectral data to increase the land cover classification accuracy. The land cover map for Iran (2017) had an overall accuracy of 95.6%.

Zeraatpisheh et al. [63] used the Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Perpendicular Vegetation Index (PVI), Clay Index (CI), Landsat image band 4 (B4) and Soil Adjusted Vegetation Index (SAVI), derived from the Landsat enhanced thematic mapper, in order to predict and compare the spatial distribution of soil organic carbon (SOC), calcium carbonate equivalent (CCE) and clay content by using different machine learning techniques (Cubist, Random Forest , Regression Tree and a Multiple Linear Regression) in Iran. Their results showed that the highest and the lowest prediction accuracies were obtained for clay and SOC using Random Forest and Random Tree models, respectively. Similarly, machine learning techniques were applied by Vafaei et al. [62] in order to improve the forest above ground biomass estimation. They generated three datasets including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). They found that above ground biomass models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset.

3.3.5. Disaster Risk Reduction

Disaster risk reduction is the thematic area with the highest average number of citations per thematic area for the top 10 cited papers (Figure 4). The focus of these papers is on landslides [64,65,66,67,68,69] and on floods [70,71,72,73]. All these papers have in common the use of Machine learning, Artificial Neural Networks or both techniques.

According to Merghadi et al. [65] tree-based ensemble algorithms achieve excellent results compared to other machine learning algorithms for landslide susceptibility mapping. Specifically, the Random Forest algorithm offers robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training the model. Pourghasemi and Rahmadi [69] reported similar results for the Random Forest algorithm on their review study on landslide susceptibility. According to Reichenbach et al. [64] for the preparation of landslide susceptibility assessment there are 9 steps that need to be considered, which include obtaining relevant landslide and thematic information, selecting appropriate mapping unit and model, evaluating model fitting and predictive performance, estimating model uncertainty and ranking model quality and designing the landslide protocol. Future studies should consider these steps and focus on the development of landslide early warning systems at different geographical scales.

Remote sensing data on land cover, morphology and hydrology are essential both for landslide and flood research. According to Ahmadlou et al. [70], flood occurrence is mainly affected by plan curvature, followed by altitude, geology, distance to river, slope and soil, land use, rainfall and slope aspect. A range of models and machine learning techniques have been applied in order to improve the flood susceptibility mapping [72,73]. However, future studies should focus on the development of flood forecasting and warning systems which is essential in order to minimize the flood hazards [71].

3.3.6. Cultural Heritage

EO on Culture Heritage thematic includes detection, monitoring and management of archaeological sites [74,75,76,77], looting detection [78,79], mapping and reconstruction [80,81,82] and environmental risk monitoring [83].

The studies on the detection, monitoring and management employed various satellite data (Corona, Landsat, Spot, Quickbird, and Sentinel-2A, COSMO-SkyMed) and reported that there is a need for further development of machine and deep learning techniques in order to automate feature extraction and pattern detection in very high resolution (VHR) images [74,76]. Also, the combined use of LiDAR, aerial photographs, historical records and satellite images will further enhance the investigation of ancient sites [80]. By screening long Sentinel-2 time series for Aleppo and Apamea, Tapete and Cigna [74] showed that changes of textural properties and surface reflectance can be associated to events relevant for conservation. Tapete and Cigna [75] also suggested the use of COSMO-SkyMed data for condition assessment of archaeological heritage and landscape disturbance due to its capabilities for very high spatial resolution images and for site revisit of up to one day. Stott et al. [82] applied airborne laser scanning (ALS) remote sensing and photogrammetric techniques, including enhancing local topographic contrast for archaeological interpretation, residual relief modelling, sky view factor, and local dominance, and they were able to accurately map known archaeological features and to detect a wide variety of previously unrecorded potential features. A major issue for the preservation of archaeological areas appears to be urban and agriculture sprawling [74,77,82,83].

Lasaponara and Masini [79] introduced an automatic method for archaeological looting feature extraction approach (ALFEA) which is based on the looting enhancement using spatial autocorrelation, unsupervised classification, and segmentation. For their analysis they used images available from Google Earth. Their results from the evaluation procedure showed satisfactory performance from two analysed test cases with a rate of success higher than 90%. According to the review study by Tapete and Cigna [78], a variety of satellite data were used for the detection of archaeological looting, such as HR multispectral imagery from Sentinel-2, VHR X-band SAR data from the COSMO-SkyMed, VHR panchromatic and multispectral WorldView-2 imagery and VHR optical data from GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3; 89% of the studies on looting detection exploited optical images and the majority of these studies used VHR images (less than 1 m resolution). According to the authors, the use of open high resolution (HR) data (e.g., Sentinel-2 and Landsat) is still at the early stage. Currently, there is lack of common practices in the domain of looting detection from space, thus there is a need to move toward sharing and harmonization of methodologies.

3.3.7. Energy

The top 10 highly cited articles in the Energy thematic area focus on the spatial assessment of the energy potential and on the location selection for energy plants [84,85,86,87,88,89,90,91,92,93]. Seven of these studies focus on solar energy (Photovoltaics, Concentrating Solar Power), two focus on offshore wind energy potential and one study focuses on bioethanol.

In their study on the spatial assessment of solar energy potential at global scale, Pravalie et al. [91], analyzed the solar radiation distribution and intensity based on global horizontal irradiation (GHI) and direct normal irradiation (DNI) data. They found that most of the countries located in the EMMENA region (Saharan countries and countries located in the Arabian Peninsula) present superb (maximum) solar potential, thus there are opportunities for developing solar energy at national or international (regional) level. However, there are additional factors that can affect solar power generation in this region, such as dust particles and dust storms, high air temperatures, insufficient financial resources and political instability or war conflicts.

The remaining studies have in common the combined use of GIS and Analytical Hierarchical Process Methods, except the study of Firozjaei et al. [93] in which they employed Ordered Weighted Averaging analysis for the solar energy evaluation in Iran. The assessment of the energy potential and the location of energy plants includes a range of criteria such as climatic (e.g. solar radiation, sunshine, land surface temperature), morphological (e.g. slope and aspect), locational (e.g. distance from cities, roads and electricity grid), land cover and hydrological (e.g. distance from dams and groundwater) [84,85,86,87,92,93]. The criteria for the assessment of the offshore wind energy potential include among others wind characteristics, water depth, soil substrate and distance from the shoreline, military areas, parks, submerged cables, shipping routes and fishing areas [89,90]. Finally, the criteria for Bioethanol facility location selection include social (e.g. policies, work force, societal impact, quality of life), environmental (e.g. ecologically sensitive areas) and economic (e.g. investment, maintenance and operation costs) criteria [88].

3.3.8. Marine Safety and Security

Marine safety and security is the thematic area with the lowest average number of citations per thematic area for the top 10 cited papers (Figure 4). Studies include oil spill detection and assessment [94,95,96,97,98,99], toxicity assessment of heavy metals [100], environmental pollution of coastal zones [101], detection of floating plastic litter [102], characterization of natural hydrocarbon seepage [103] and relative sea-level rise and potential submersion risk [104].

Research on oil spill detection and assessment was conducted with various satellite data including Sentinel-1, Planetscope, Landsat 5 and Landsat 8. Chaturverdi et al. [97] and El-Magd et al. [99] used SAR data from Sentinel-1 satellites to detect and map oil spills in the Al Khafji area (Saudi Arabia) and in the coastal waters of Egypt. Their results demonstrated the capability of utilizing Sentinel-1 for oil spill detection and mapping. Arslan [96] analyzed Sentinel-1 data to detect an oil spill caused by a ship on 18 December 2016 off the coast of Ildır Bay (Izmir, Turkey). Furthermore, Landsat 8 data were used for validation purposes by estimating brightness temperatures to observe oil spill changes in temperature on the sea surface. Park et al. [95] combined very high resolution satellite images (Planetscope) with an Artificial Neural Network technique to detect oil spill accidents occurred on 10 August 2017 near the Ras Al Zour area (Kuwait). The accuracy and kappa coefficient of the oil classification map obtained from the optical image were 82.01 % and 72.42 %, respectively. Future studies should focus on satellite data of higher temporal resolution, to verify the results with in-situ or satellite SAR data and to develop better algorithms for rejecting low-probability pixels and dust. Finally, there is a need to develop a near-real-time warning and alarm system for oil spill cases as a service, which can help to identify the ship or platform that is liable for the pollution [99].

Ivanov et al. [103] studied the hydrocarbon seep characteristics and their relation to the local hydrocarbon field or potential hydrocarbon reservoirs in the South Caspian sea (Iran). They analyzed SAR images (Sentinel-1) together with bathymetry, geological and geophysical data with GIS. Their results showed that the oil slicks have natural origin, related to the bottom seepage phenomenon, and associated with an existing hydrocarbon system.

Fatehian et al. [101] designed and implemented an integrated Volunteered Geographic Information (VGI)-based system based on coastal pollution management concepts and public participation in order to monitor coastal pollution in Nowshahr port city (Mazandaran Province, Iran). Tourists, residents, and other present individuals were asked to report observable pollutants at their location; 86% of the reports referred to the accumulation of garbage, while 10% and 4% of the remaining reports were related to the wastewater pollution and oil contaminants, respectively. According to the authors, future work should focus on the integration of Spatial Decision Support Systems (SDSSs) in the existing tool to improve coastal pollution management.

El-Alfy et al. [100] used Landsat OLI images, synchronized with the sampling time of water and sediment samples to assess the toxicity of heavy metals and organochlorine pesticides in freshwater and marine environments in Rosetta area (Egypt). They found that drainage canals, cultivations and urbanized zones were the major sources of contamination in the studied area. They recommended biological treatment processes such as using naturally grown plants like Phragmites australis in the contaminated sites.

Themistocleous et al. [102] examined the potential of detection of the floating plastic litter by introducing the Plastic Index (PI) and Reversed Normalized Difference Vegetation Index (RNDVI), based on the Sentinel-2 B08 (842 nm) and B04 (665 nm) bands. They found that Sentinel-2 satellite images were effective in identifying plastic clusters in the sea, through the high reflectance of solar radiation at NIR wavelengths. They highlighted that future research should focus on the use of Sentinel-1 SAR images for the identification of plastic litter in the seas.

Antonioli et al. [104] studied the relative sea-level rise and potential submersion risk for 2100 on 16 coastal plains of the Mediterranean Sea (France, Spain, Tunisia, Cyprus) by analyzing LIDAR data and different climate projection scenarios. They estimated a potential loss of land for the above areas between about 148 km² for the IPCC-RCP8.5 scenario and 192 km² for the Rahmstorf scenario, impacting a coastline length of about 400 km. However, the low resolution (25 m ×25 m) of the Digital Terrain Model map of Cyprus prevented any further detailed coastal hazard assessment.

3.3.9. Big Earth Data

The Big Earth Data thematic area includes technologies and development of methods and algorithms which are applied in the other thematic areas. Thus, it is considered as a horizontal thematic area. Nine out of the ten most highly cited articles which include Big Earth data applications were already described in subsection 3.1.5 and the focus of these papers is on the use of Machine learning and Artificial Neural Networks on landslide and flood studies. The remaining article focuses on a novel machine learning-based approach for the risk assessment of nitrate groundwater contamination [34]. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence and then an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map with reliable results. The majority of the research done on Big Earth Data in the EMMENA region is applied.

3.3.10. Other

This study also identified EO studies in the EMMENA region which are not included in any of the above thematic areas. These studies include animal species detection, identification and monitoring [105,106,107], sociodemographic characteristics [108,109], spatial analysis of cancer [110,111], optimal siting of electric vehicle charging stations [112], evaluation of bike-share stations [113], paleoecology [114] and examining the walking accessibility, willingness, and travel conditions of residents [115].

Figure 4. Average number of citations per thematic area (top 10 cited papers).

Figure 4. Average number of citations per thematic area (top 10 cited papers).

4. Overview and Conclusions

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