# 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 Vegetation in Drought
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Band No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} } & \multicolumn{2}{c}{**Sentinel-2 A**} & \multicolumn{2}{c}{**Sentinel-2 B**} & \multirow{2}{*}{**SNR @ Lref**} \\ \cline{3-3} \cline{5-6}  & & **Central** & **Band WIDTH** & & **Central** & **Band Width (nm)** & \\ \hline B8 & 10 & 832.8 & 106 & 832.9 & 106 & 174 \\ \hline B8a & 20 & 864.7 & 21 & 864.0 & 22 & 72 \\ \hline B9 & 60 & 945.1 & 20 & 442.2 & 21 & 114 \\ \hline B10 & 60 & 1373.5 & 31 & 943.2 & 21 & 50 \\ \hline B11 & 20 & 1613.7 & 91 & 1610.4 & 94 & 100 \\ \hline B12 & 20 & 2202.4 & 175 & 2185.7 & 185 & 100 \\ \hline \end{tabular} Source ([[https://sentinel.esa.int/web/sentinel/user-guides/Sentinel-2](https://sentinel.esa.int/web/sentinel/user-guides/Sentinel-2) msi/resolutions/radiometric]([https://sentinel.esa.int/web/sentinel/user-guides/Sentinel-2](https://sentinel.esa.int/web/sentinel/user-guides/Sentinel-2) msi/resolutions/radiometric), accessed date: 10 November 2020).  
\end{table} 
Table 1: \(Cont.\)Index, Anthocyanin Reflectance Index, Red-Green Ratio, Normalized Difference Infrared Index, Short Wave Infrared Ratio, Plant Senescence Reflectance Index, and Soil Adjusted Total Vegetation Index was assessed in extracting the grassland and savanna vegetation states of the North American transect. Hyperion hyperspectral data were used to simulate Sentinel-2, and MODIS data and the Visible Infrared Imaging Radiometer Suite were used for the field validation [70]. Drylands are one of the most climatologically vulnerable areas, which are characterized by varying rainfall conditions with a high level of evapotranspiration. Hence it is essential to monitor and estimate the spatial-temporal vegetation characteristics in the dryland ecosystem. The potentiality of HLS has been assessed in delineating the dryland phenology characteristics. In a novel approach, spectral information was derived from the Sentinel-2 MSI (Multispectral Instrument) and Landsat OLI (Operational Land Imager) using the regression tree model. In several cases, the information derived from the data fusion methods outperformed the single sensor information derived from MODIS and Landsat [71]. Vegetation phenology has a vital role in analyzing the impact of climate change and drought analysis. Since the inception of Sentinel-2, there has been a substantial increase in research towards this direction [72]. For a regional and field level, a high-spatial time series of information is required for continuously monitoring the seasonal variations in drought analysis. The vegetation temperature condition index (VTCI) is a proven index used for estimating drought [73].VTCI derived from Sentinel-2 and MODIS fusion using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) correlated with cumulative precipitation to assess the drought conditions in Guanzhong Plain of China [74]. Sentinel-2 derived NDVI, fraction canopy cover, leaf area index, canopy water content used to disaggregate MODIS land surface temperature [75]. Vegetation response to drought estimated through Spatio-temporal analysis of NDVI, changes in NDVI values before and during drought period used to quantify the intensity of drought. Following Figure 2 represents the variations of Sentinel-2 derived NDVI before and during the drought in Novi Sad and the neighboring region. The image on the left represents March 2017 and the on the right represents July 2017. Figure 2: Sentinel-2 derived NDVI before and during the drought.