# 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
Inland water bodies were detected from the downscaled Sentinel-2 SWIR band using Intensity, Hue, Saturation (IHS) transformation, Principal Component Analysis, the Brovey, and the Gram-Schmidt pan sharpening methods. The modified NDWI produced from the IHS and Brovey methods outperformed other approaches and accurately detected the water and non-water areas, respectively [198]. Similarly [199], short-wavelength infrared bands of Sentinel-2 and Landsat were pan-sharpened using PCA, IHS, GS, and NDWII methods. The HIS method outperformed other methods and accurate modification of NDWI was produced from this approach. In recent years, drought monitoring has become very important for people and the environment. Now, thanks to satellite missions with a higher spectral and temporal resolution, such as Sentinel-2, the obtained results are much more accurate. With a temporal resolution of 5 days and a spatial resolution of 10 m, Sentinel-2 enables the efficient detection of surface waters and wetlands [200; 201; 202; 203; 204; 205; 206; 207]. The most common methods for mapping wetlands and surface waters that use Sentinel-2 mission data, or use Sentinel-1 and Sentinel-2 mission data fusion, are unsupervised classification [208; 209; 210], supervised classification [209; 211; 47], change detection using vegetation indices [212; 213], object-based classification [209; 214; 47], index-based classification [209], tile-based image thresholding [215], OTSU algorithm [200; 203; 216], and the rule-based super pixel (RBSP) approach [217]. In addition to these approaches, machine learning algorithms are being used more and more often. The most commonly used ML algorithm is the Random Forest classification [218; 219; 220], SVM [220; 221]. On the Sentinel-2 image, [PERSON] et al. 2018 used the one-class classifier Bias support vector machines (BSVM) to identify and map wetlands as an unsupervised classification algorithm. Such a model, which is primarily based on images from the Sentinel-2 mission, provides high performance for wetland mapping, in cases when single-date images and a small amount of field data are used [210]. A multi-time analysis of images was obtained from Sentinel-2 satellites, using the unsupervised K-means classification method and the MNDVI water index with the same image size in each process and year. In this way, a percentage of the class belonging to the flooded area was obtained. Comparing the results, he concluded that there are large oscillations in the water level of the wetlands, which can be attributed to drought or excessive rains. [PERSON] et al. [47; 209] uses three different approaches to determine the boundaries of wetlands and they are pixel-based, object-based, and index-based classifications. When a pixel-based classification is used as an approach in an unsupervised classification, the pixels are grouped based on the pixel reflection properties, while for supervised classification, selected samples are used for each defined class. The results show that better results are obtained with supervised classification, but are not good enough. Another approach that [PERSON] et al. 2017, 2018, 2018a [47; 209; 214] use is object-based which certainly gives better results, especially when using the fusion of Sentinel-1 and Sentinel-2. The results obtained by a fusion of radar and optical data showed an overall classification accuracy of about 90% for wetland mapping. The third approach based on the classification of the index provides the possibility of separating the amount of vegetation and water, but not the detection of wetland boundaries. Therefore [PERSON] et al. 2017 [209] propose a new approach that uses both object-based and index-based classifications, which as a result, gives a kappa value of 0.95 and as such is very suitable for mapping and change detection in wetlands. [PERSON] et al. [211] explore the applicability of pixels based on supervised classification, using Bagged Tree, Subspace KNN and SVM.