# 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  
Evapotranspiration (ET) is defined as the sum of the evaporation and transpiration from the vegetation. Advances in remote sensing technologies enable precise estimation of ET from global to field level under varying topographical conditions. This section focuses only on the ET from the land surface, observational methods, major techniques, and algorithms applied for estimating the ET using Sentinel-2 data and fusion methods. ET is one of the major components in the hydrologic budget and is considered equally with the precipitation in drought studies. In other words, drought can be defined as the imbalance between evaporation and precipitation, the rate of ET decides the intensity and the duration of drought. As the rate of ET has exceeded the precipitation level due toclimate change-driven land surface temperature [108], the upcoming decades may record frequent and extreme droughts. Hence, regional and seasonal variations in ET are closely monitored to estimate the trend and amplitude of drought [109; 110]. Advancements in satellite remote sensing techniques have provided new dimensions to the spatial and temporal estimation of ET [111; 112]. The two prevalent approaches in which Sentinel-2 is used for estimating ET are the indirect and data fusion method. In the indirect method, the required biophysical parameters for computing the FAO-56 Penman-Montecito (FAO-56 PM) are derived from the Sentinel-2 using vegetation indices (VI). In the data fusion method, Sentinel-2 synergy with other satellite data (particularly thermal band) contemplates the latent heat flux using surface energy balance models [113; 114]. Thermal disaggregated data fusion methods overcome the thermal band limitation of the Sentinel-2 [115]. The Penman-Monteith (FAO-56 PM) method is considered a standard protocol for calculating ET [116; 117]. However, data availability [118] and the quality of input parameters [119] control its applicability to an extent and result in overestimation. The peculiarities of a Sentinel-2 driven VI in ET estimation is its ability to provide accurate vegetation phenology characteristics and a large number of available red-infrared region-based indices, including its red-edge band [120]. Sentinel-2 derived vegetation indices applied in the FAO-56(PM) improved the ET estimation. Sentinel-2 derived LAI and surface albedo coupled with the daily weather information was applied in the Penman-Monteith equation for calculating the field level potential crop evapotranspiration [121] in the Mediterranean region. The estimated result shows a better correlation with the actual crop evapotranspiration derived from the calibrated soil water balance model [122]. Hence, the satellite-derived biophysical variables became major constituents in the FAO-56(PM)-based computation of ET. Biophysical retrieval methods influence the level of accuracy. In a comparative analysis, LAI and the canopy chlorophyll content were retrieved from the Sentinel-2 using major vegetation indices, the CLAIR model, and the artificial neural network (ANN), and their statistical significance was recorded with the in situ data. The crop evapotranspiration generated from the ANN-based LAI and canopy chlorophyll content outperformed other methods with zero saturation [123]. The soil resistivity tomography records the soil wetness and distribution pattern within the irrigation interval. Sentinel-2 derived VI coupled with electrical resistivity tomography derived from surface geophysics information was applied in the dual crop coefficient (K\({}_{\rm c}\)) FAO-56 approach for the estimation of crop evapotranspiration from an underwater stress condition [124]. The crop coefficient (K\({}_{\rm c}\)), the major component of ET has been widely assessed from the satellite-based vegetation indices [125; 126].