# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Land Use and Land Cover Change in Drought  
Drought is one of the major land surface altering agents, its intensity and rate of land surface conversion varies and depends on several factors such as anthropological activities, climatological conditions, and geographical characteristics. Spatio-temporal analysis of land use and land cover will depict the drought-driven land surface changes. With the higher spatial resolution revisiting capabilities, Sentinel-2 can efficiently monitor the changes in land use and land cover [100]. Compared to single sensor information, the land use and land cover estimated from the multi-sensor (Sentinel-1 and 2) using the random forest wrapper approach increased the overall accuracy in the cloud-prone region [101]. Foremost classifiers such as the Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) were compared in classifying land use and land cover from Sentinel-2. It was observed that when the training class is greater than 0.25% of the total study area, the three classifiers recorded a similar and overall higher accuracy (>93.85%). Among other bands, the peculiarities of the Sentinel-2 red-edge band were evaluated in land use and land cover analysis [102]. The random forest, stochastic gradient boosting, and support vector machine methods were applied to HLS data for delineating the land use and land cover changes. The research concluded that the Sentinel-2 red-edge band-derived classification outperformed the results produced from Landsat-8 as well as other bands of Sentinel-2 (5% and 4% respectively) [103]. For a better understanding of drought-driven land cover changes, [PERSON] et al. [104] produced 10 m land cover and tree cover data from combining Sentinel-2 with Google Earth information and field data. The reliability of the Google Earth information was assessed with field data and Sentinel-2 treated with a supervised classification algorithm including the minimum distance, Mahalanobis distance, neural network, and support vector machine algorithm. The SVM outperformed other algorithms with an overall accuracy of 81.33% and a kappa index of 0.76. Sentinel-2 data was computed in the Google Earth engine platform for classifying the fallow land from one of the world's largest dryland regions, the Sahel [105]. Initial reference data produced from the Knowledge Analysis based unsupervised classification techniques used as an input in the random forest classifier for producing the second level enhanced reference data. Finally, the enhanced reference data used in the random classifier was applied to map the 10 m fallow land and cropland of the Sahel region. Spatiotemporal analysis of biocrust and vascular plant cover over the rainfall gradient region of the Negev Desert was assessed using the Sentinel-2. The continuum removal method was applied to the hyperspectral data acquired from the headwall spectrometer to estimate the chlorophyll content information derived from Sentinel-2 [106]. Two indices were proposed, namely the biocrust greenness index and maximum vegetation development index. The proposed indices had satisfactory performance over the northern part of the Negev and poor performance in the south due to a shortage of precipitation and heavy dryness. Pixel R-CNN, a novel method based on recurrent and convolutional neural networks designed to estimate the land cover and crop classification from the Sentinel-2 data. The Pixel R-CNN results show considerable improvement with an overall accuracy of 96.5%, compared with the support vector machine, random forest, Kernel SVM, and gradient boosting machine models [107].