# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 4 Summary and Conclusions  
Drought is the most destructive and expensive natural disaster with a complex nature and pervasive impacts. For many reasons including high spatial, spectral, and temporal resolution, Sentinel-2 has a greater potential in assessing drought, compared to other contemporary mission products (e.g., Landsat-8). However, researchers have to depend on other satellite data for analyzing drought conditions before the commencement of Sentinel-2. That could also be a reason behind there being less research on the direct applicability of Sentinel-2 on drought. Another limitation observed in the Sentinel-2 application was the rate of uncertainties in data fusion and downscaling methods, though it was less in comparison to other sensors. Like other optical sensors in certain conditions, Sentinel-2 accuracy is also subjected to the accuracy of the on-ground measurement instruments. With the unique characteristics of the red-edge band and short revisiting nature, Sentinel-2 can efficiently record the abnormal variation in plant's phenological and physiological conditions. The red-edge band is the most important band of the Sentinel-2 with high precision in observing the vegetation response to drought, compared to conventional methods such as NDVI. Higher spatial resolution is a prerequisite in drought studies, Sentinel-2 offers peerless 10 m resolution in land surface monitoring in comparison to other freely available satellite images such as Landsat and MODIS. The data fusion method has overcome the thermal band limitation of the Sentinel-2 in estimating the drought factors such as soil moisture and ET using the surface energy balance model. In several cases, ET estimated from Sentinel-2 fused information such as HLS outperformed Landsat and MODIS outputs. At the same time, Sentinel-2 derived biophysical parameters coupled with climatological data used in ET, soil moisture estimation using FAO-56 PM, OPTRAM, and SAFER-based agriwater methods. It was observed that estimating soil moisture from Sentinel-1 and 2 data fusion maintains a higher level of accuracy compared to the fusing of Sentinel-2 with Landsat and MODIS. Without adequate research in this direction, it is difficult to compare the precision and efficiency of Sentinel-2 derived drought factors using direct and fusion methods because in most cases these methods are developed for either testing a particular hypothesis or an area of interest. The integration of machine learning and deep learning algorithms increased the overall accuracy of Sentinel-2 derived information on surface water and wetland, vegetation, land use, and land cover classification. Data continuity of the Sentinel-2 is another important aspect in drought studies. The upcoming improved Sentinel-2C and Sentinel-2D to cover the overall 20-year mission will be another advantage. Finally, Sentinel-2 can efficiently \begin{table}
\begin{tabular}{c c} \hline
**Aspect** & **Method** & **References** \\ \hline \multirow{6}{*}{Remote Sensing of Surface Water and Wetland Analysis in Drought} & ML algorithm-Random Forest da & [28; 219; 220] \\ \cline{2-3}  & Unsupervised Classification & [47; 208; 210; 211] \\ \cline{2-3}  & Supervised Classification & [47; 209; 211] \\ \cline{2-3}  & Object-based Classification & [47; 209; 214] \\ \cline{2-3}  & Gram-Schmidt pan sharpening methods & [198] \\ \cline{2-3}  & OTSU algorithm & [200; 203; 216] \\ \cline{2-3}  & Index-based Classification & [209] \\ \cline{2-3}  & Change Detection using Vegetation Indices & [212; 213] \\ \cline{2-3}  & Tile-based Image Thresholding & [215] \\ \cline{2-3}  & Rule-based Super Pixel (RBSP) approach & [217] \\ \cline{2-3}  & ML algorithm-Support Vector Machine (SVM) & [220; 221] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._monitor and delineate the slow pace of drought, which could improve drought monitoring, forecasting, and developing efficient mitigation procedures. Conceptualization, D.V., Writing and Editing D.V., M.R., S.S., Review, V.C.