## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.3 Agriculture
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, [PERSON] et al. [84] 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 production and yield prediction using remotely sensed data are generally scarce in the EMMENA region due to the inter-annual variation in meteorological factors combined with anthropogenic factors (such as war conflicts) [88]. In the study on barley yield prediction in Iran, [PERSON] [89] combined vegetation indices (NDVI and EVI) derived from Sentinel-2 data, barley yield data, and four algorithms, including a backpropagation neural network, decision tree, K-nearest neighbor, and Gaussian process regression. The latter algorithm had the highest performance (\(r^{2}\) = 0.84) and estimated yield with a root mean square error of 737 kg ha\({}^{-1}\) and mean absolute error of 650 kg ha\({}^{-1}\). [PERSON] et al. [88] developed an empirical regression-based model to forecast winter wheat and barley production in Iraq, based on MODIS satellite data and in situ crop data. They found that the highest accuracy (R\({}^{2}\) = 0.70) is achieved when the vegetation index (VI) is at the maximum. Crop growth dynamic observations from satellites are often limited by the coarse spatial and temporal resolutions. To address this issue, [PERSON] et al. [87] proposed the use of data from different ongoing satellite missions of 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. [PERSON] et al. [86] 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 (R\({}^{2}\) = 0.94) throughout the growing season for LAI < 3. [PERSON] et al. [90] examined the potential of automatic mapping of canola (_Brassica napus_ L.) using time series of Sentinel-2 images. They found that multiplication of the near-infrared (NIR) band by the sum of red and green bands is an efficient index to identify canola during the flowering stage. The kappa and overall accuracy (OA) were more than 0.75 and 88%, respectively. Furthermore, [PERSON] et al. [91] examined the relationship between stem water potential and four vegetation indices (GNDVI, NDVI, EVI, and SAVI) in Mediterranean vineyards (Israel) through the use of high spatial resolution Planetscope images. The authors highlighted that SAVI displayed a slightly better correlation with stem water potential than the other three indices, likely due to its capacity for reducing soil effects. Urban expansion has critical consequences for agricultural production in the EMMENA region. [PERSON] et al. [85] 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/](http://maps.elie.ucl.ac.be/CCI/viewer/)]([http://maps.elie.ucl.ac.be/CCI/viewer/](http://maps.elie.ucl.ac.be/CCI/viewer/)), accessed on 13 March 2023) 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\({}^{-1}\).