# 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 Evapotranspiration in Drought
While assessing 22 Sentinel-2 derived vegetation indices, it was observed that compared to other regions of the electromagnetic spectrum, the indices calculated from the red-edge regions are more suitable for predicting the K\({}_{\rm c}\)[127]. ArChQualK\({}_{\rm c}\) is an FAO-56K\({}_{\rm c}\) based approach implemented in the Geographical Information System (GIS) which facilitates Sentinel-2 in the spatial mapping of ET [128]. Considering the precision and needlessness of ground measurands, the forecasted data began to replace measured meteorological data [129] and was incorporated with Sentinel-2 VI for computing the ET [130]. Irrespective of its quality and predictability, the forecasted data are subjected to model errors. However, to minimize the uncertainty in the prediction of ET more research has to be conducted on treating forecasted meteorological data with VI information. Data synergy from a similar satellite series may ensure data continuity, apart from that, thermal sharpening of Sentinel-3 using Sentinel-2 optical information [131] for a field-level estimation of ET is a complex procedure. However, several methods are available to serve the purpose, such as PBIM [132], SADFAT [133], TsHARP, and Data Mining Sharpening (DMS) [134; 135]. Using Sentinel-2 information, the DSM method sharpened the Sentinel-3 thermal band to 20 m [136]. The proficiencies of sharpened thermal information in the computing field level ET were assessed with [PERSON]'s two-source energy balance model (TSEB-PT), mapping ET with internalized calibration (METRIC), and end-memberbased two-source approach (ESVEP). The TSEB-PT outperformed other methods with all-over accuracy and is suitable for varying land cover and climates [137]. The Priestly-Taylor equation is a modified form of the Penman equation, widely applied for estimating ET under water limiting conditions [138]. The Multi-Sensor Data Fusion approach (MSDF-ET) is a well-framed multi-data fusion approach, combined with the Priestly-Taylor equation to generate a robust time series of ET with a short interval from Sentinel-2, MODIS, and Landsat-8 [139]. Landsat-8 derived land surface temperature integrated with the TsHARP algorithm for downscaling the MODIS thermal band. The estimated ET from sharpened thermal information outperformed Landsat-8 output and shows a better correlation with ground-based measurements. R package 'agriwater' was powered by the Simple Algorithm For ET Retrieving (SAFER) algorithm. The SAFER algorithm, earlier known as PM2 (based on Penman-Monteith equation) [140], was applied in a large-scale estimation of ET from diverse land surface wetness conditions. SAFER works on agrometeorological data that neither requires anchor pixels nor the thermal bands nor can integrate with weather data [141]. Hence, integrating Sentinel-2 with agriwater enables a precise estimation of ET which is beneficial in drought studies. Further, the model is designed for a daily scale rather than hourly and is subjected to uncertainties related to the quality, validity, and calibration of data. It was observed that NDVI is more reliable in estimating the irrigation demand rather than the FAO-56 dual crop coefficient estimations [142] and is closely related to the ET fraction [143]. Hence, the ET fraction is calculated from the Landsat-8 and Sentinel-2 applied in the Simplified Surface Energy Balance model for estimating ET. Though this approach could generate 10 m ET data, the level of bias is directly proportional to the level of vegetation. Considering its wide applicability, the accuracy of the satellite-based ET estimation is directly associated with the accuracy of ground-based measuring methods [144]. Each method was developed to test the same phenomenon under different conditions and hypotheses using a variety of inputs derived from different mechanisms, hence it is difficult to point out the best single method [145]. Satellite-based ET retrieving methods are subjected to regional constraints such as varying climate and associated landscape changes.