# 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 Surface Water and Wetland Analysis in Drought
The Bagged Tree classifier proved to be the best classifier for wetland detection with an accuracy of about 84%. One of the simplest approaches in mapping surface waters and wetlands is change detection using vegetation indices. [PERSON] et al. [212] and [PERSON] et al. [213] in their works calculate indices such as NDWI, Modified Normalized Difference Water Index Land Surface Water Index (LSWI + 5), and Modified Normalized Difference Index (MNDVI), on which they apply an appropriate threshold based on which they detect areas of interest. Such an approach has proven to be very useful for monitoring surface water and wetlands. Based on the threshold, the most used algorithm is the OTSU algorithm, which is used to determine the optimal value of the threshold for mapping the phenomenon from certain vegetation indices. [PERSON] et al. [200] calculated NDWI and MNDVI, and the results show that the use of MNDVI is more useful than the NDWI index. These same indices were also used in a study by [PERSON] et al. [216] and the results showed that NDVI gave better results and represented water bodies better. In addition to the aforementioned indices, [PERSON] et al. [217] introduced the automated water extraction index (AWEI). In this case, NDWI generated poor results, and there were no significant changes in accuracy between the other two indices. Another way of threshold-based detection is the tile-based image thresholding that [PERSON] et al. [215] applied in their work. The algorithm is based on a multi-time classification approach and showed an accuracy of over 92% for the proposed location. On that occasion, he calculated the following indices: NDWI, Modified Normalized Difference Water Index (MNDWI), NDVI, Normalized Difference Moisture Index (NDMI), Normalized Multi-band Drought Index (NMDI), Angle Based Drought Index 1 (ABDI1), Angle Based Drought 2 (ABDI2), Normalized Difference NIR-SWIR2 (ND\({}_{0812}\)), Normalized Difference GREEN-SWIR2 (ND\({}_{0312}\)), and Normalized Difference SWIR1-SWIR2 (ND\({}_{1112}\)). [PERSON] et al. 2020 [217] proposed a new approach to rules-based automatic superpixel (RBSP). The RBSP method uses combined spectral indices and superpixel techniques to determine surface water volume. In this study, the AWEI index was adopted and the results showed that this approach has the potential to track surface water changes. Today, machine learning tools are increasingly used to process multi-temporal images. The most commonly used algorithms are the Random Forest (RF) and Support vector machine (SVM) [218; 219; 220]. Data fusion of Sentinel-1 and Sentinel-2 and the results show an accuracy of over 80%. [PERSON] et al. 2018, compare these two algorithms and the results show that RF is better than SVM, but not to a large extent. [PERSON] et al. [221] also used the SVM algorithm in their study as a way to detect water surfaces, and the results show an accuracy of about 95%. From these examples, the fusion of Sentinel-1 and Sentinel-2 satellite data has a successful application, especially when ML algorithms are implemented over this data. Sentinel-2 satellite missions have been successfully used to detect and map the impacts of drought on water surfaces and wetlands. The following Table 2 summarized all mentioned methods and corresponding references in this article for extracting the drought information from the climatological ecological variables using Sentinel-2 satellite.