## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.3 Agriculture  
The EO applications in the thematic area of agriculture cover a wide range of topics such as evapotranspiration (ET) estimation [73; 74; 75; 76], land suitability for agriculture [77; 78; 79; 80; 81; 82; 83], irrigation mapping [84], agricultural land loss [85], crop monitoring [86; 87], crop production and yield prediction [88; 89], crop mapping [90], stem water potential monitoring [91], and crop water footprint estimation [92]. Accurate assessment of water use in agriculture is of critical importance due to the changing climate and increasing water scarcity. Thus, modeling crop water use is a powerful decision tool for agricultural policy decisionmakers to improve water use efficiency [92]. In their study on crop water footprint estimation, [PERSON] et al. [92] modeled the green and blue water footprints of maize by using an artificial neural network (ANN) in major maize-producing sites in Egypt, between 2006 and 2016. Meteorological data were collected from the Climatic Research Unit Time Series and the Japanese 55-year Reanalysis (JRA-55) in NetCDF formats. Their model achieved high accuracy with deviations between the actual and predicted water footprints ranging from \(-2.6\) to \(6.6\%\) and from \(-2.4\) to \(3.2\%\) for the blue and green water footprints, respectively. Representative and accurate evapotranspiration maps are needed to achieve the planning and management of sustainable water use in water-scarce regions, such as the EM-MENA region [76]. [PERSON] et al. [74] compared four different evapotranspiration methods, including ET estimates from reanalysis data (NCEP, ERA-Interim, ERA5), MODIS ET datasets, Landsat-based ET models (METRIC, SEBS), and in situ measurements, from 2009 to 2014, in southern Iran. The authors reported 4.6% overestimation and 11.4% underestimation of ET with the METRIC and SEBS models, respectively. [PERSON] et al. [73] reported that actual evapotranspiration in daily time steps predicted based on the SEBAL method is underestimated for barley, cucumber, chickpea, and alfalfa and overestimated for wheat, potato, onion, lentil, bean, and maize and is close to the calculated values of SWB for tomato, colza, and sugar beet. Furthermore, [PERSON] and [PERSON] [75] showed good efficiency of the SEBAL model for estimating the actual ET of pistachio. [PERSON] and [PERSON] [76] computed the actual evapotranspiration (AET) over the central region of Saudi Arabia from 1950-2013, based on meteorological data and on crop coefficients (Kc) which were modeled as a function of a 16-day time-series MODIS normalized difference vegetation index (NDVI). They reported that annual AET estimated by the soil water balance model was between 9 and 11% higher than the modeled AET. The studies 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 wheat (Turkey), Citrus (Turkey, Morocco), legumes (Morocco), maize \begin{table}
\begin{tabular}{c c c c c} \hline
**N** & **Ref.** & **Focus** & **Country** & **Methods** & **Satellites** \\ \hline
6 & [55] & GP & Iran & ML & Landsat ETM+ \\
7 & [56] & GP & Iran & AI & Landsat 7, ASTER \\
8 & [57] & GP & Iran & ML & Landsat 8 \\
9 & [54] & GP & Iran & ML & Landsat ETM+, Sentinel-2, ASTER, ALOS \\
10 & [72] & GP & Iran & AI/ML & Landsat \\
11 & [66] & GQ & Egypt & & N/A \\
12 & [63] & GQ & Iran & & N/A \\
13 & [64] & GQ & Iran & & N/A \\
14 & [67] & GQ & Iran & & N/A \\
15 & [68] & GQ & Iran & ML & N/A \\
16 & [65] & GQ & Iran & & N/A \\
17 & [60] & GQ & Iran & & N/A \\
18 & [70] & QIW & Iran & & ASCAT, SMOS, AMSR2 JAXA \\
19 & [71] & ET & Iran & & Landsat 5/8 \\
20 & [69] & DP & Iran & AHP & N/A \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._(Iran), soybean (Iran), rapeseed (Iran), barley (Iran), olives (Morocco), and jojoba (UAE) crops.