## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.4 Land
The authors reported high classification accuracies and showed that the total forest cover over the 30-year period remained stable. [PERSON] et al. [103] developed the dry built-up index (DBI) and dry bare-soil index (DBSI) to map built-up and bare areas in dry climates with the use of Landsat 8 images. The results from the city of Erbil (Iraq) showed an overall classification accuracy of 93% (\(\kappa\) = 0.86) and 92% (\(\kappa\) = 0.84) for DBI and DBSI, respectively. Rapid and extensive urbanization over the past few decades has led to changes in the microclimate of urban environments [104]. Urban regions experience warmer temperatures by absorbing a greater amount of solar radiation than their natural rural surroundings, a phenomenon well known as urban heat island (UHI). [PERSON] et al. [104] used satellite images of Landsat 7 ETM+ (1999 and 2006) and Landsat 8 (2013 and 2016) to retrieve the Earth surface temperature of Isfahan (Iran). Their results showed an increase in the minimum temperature of 2016 compared to 1999 and that the heat island ratio followed a rising trend, moving from 0.16 (1999) to 0.3 (2016). Similarly, the study of [PERSON] et al. [105] showed that surface urban heat island intensity in the city of Babol (Iran) increased significantly (24%) during the period from 1985 to 2017. [PERSON] et al. [106] found a temperature difference between urban high building density areas and other terrestrial areas, ranging from 0.4 to 2.5\(\,{}^{\circ}\)C.  
Knowledge about the distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices [107; 108]. [PERSON] et al. [109] developed a method for retrieving surface soil moisture at a high spatiotemporal resolution, by combining Sentinel-1 (S1) microwave and Landsat 7/8 thermal data. The authors reported a root mean square difference between satellite and in situ soil moisture of 0.03 m\({}^{3}\)m\({}^{-3}\). [PERSON] et al. [107] 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. [PERSON] et al. [110] showed that the use of dynamic (multi-temporal) satellite data fusion, rather than static, can greatly improve soil modeling and mapping. Accurate measurements of aboveground forest biomass are crucial for estimating forest carbon stocks and for regional, sustainable land use planning. [PERSON] et al. [111] applied machine learning techniques in order to improve the forest aboveground 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 aboveground 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.