# 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 Soil Moisture in Drought  
As a key variable in regulating the Earth water cycle, surface soil moisture (SSM) has a significant role in detecting, monitoring, and predicting drought [146; 147; 148; 149; 150; 151]. Soil moisture characteristics are directly associated with the weather conditions and geographical characteristics of an area [152; 153]. Since it is crucial for plant growth, it is often used as an important parameter for detecting drought-induced vegetation stress [154; 155; 156; 49]. The traditional method of soil moisture retrieval, such as the gravimetric method [157], was applied to a specific location, mostly on the small spatial resolution [158]. The importance of analyzing this parameter on a regional scale for more complex research ushered in a new era in soil moisture estimations using remote sensing techniques. During the last decade, drought monitoring by using satellite images drastically increased [159; 34; 160]. The higher spectral and temporal resolution of satellite images is required for more precise and accurate results. Nowadays, with the availability of Sentinel 2 images, these requirements are achieved. As a drought indicator, soil moisture can be obtained from land surface model simulations or satellite estimates. The most commonly used model is the OPtical TRAPezoid Model (OPTRAM) which has been used for estimating soil moisture requiring only the optical remote sensing data. The estimation of the soil moisture is based on shortwave infrared transformed reflectance (STR)-NDVI trapezoidal space [161; 162; 163; 146]. [PERSON] et al. [161] showed that in comparing estimated SSM from OPTRAM Sentinel-2 and TOTRAM Landsat-8, scientifically acceptable and comparable predictive accuracy was found. However, as the OPTRAM model does not require thermal data, it is advanced and applicable to use with optical data such as Sentinel-2 images. To improve derived results from this model, considering different sites of application, this study proposed to use the SWIR band in combination with the NIR band, as the SWIR reflectance is sensitive not just to the leaf water content but to the leaf's internal structure [164]. [PERSON],. et al. [146] applied two different model variations based on linear and nonlinear parameters validated with in situ soil water content measurements. This study showed that the nonlinear model gives a better correlation between in situ and estimated soil water content values and allows the OPTRAM model to be used for effective SSM mapping. [PERSON] et al. [163] implemented a modified OPTRAM model by introducing nonlinear edges to the VI-STR space. The MOPTRAM was used for estimating SSM and root zone soil moisture. With the validation of the OPTRAM and MOPTRAM models with ground truth volumetric soil moisture data of a maize field, this study concludes that the MOPTRAM gave accurate results when compared with those achieved from the OPTRAM model and the accuracy of surface soil moisture estimates in bare soil was better than the root zone soil moisture in relatively full vegetation cover. For different model-based approaches, retrieving SSM data requires a fusion of the data. The fusion of Sentinel-1 and Sentinel-2 satellite data showed successful applications and accurate results whether in global or field-based SMM estimations [165; 166; 167; 168; 169]. There are several different machine learning or empirical-based approaches for this purpose: the Water cloud model [149; 168; 170; 171], change detection method [65; 169; 172], ANN [149], cost function [173], and support vector regression [174]. A combination of the Water cloud model (WCM) and the integral equation model (IEM) or advanced IEM (AIEM) are often used to estimate SSM with high accuracy in different vegetated areas [170; 175]. Comparing with OPTRAM, the Water cloud model requires both SAR and optical data to provide the possibility to derive SSM data.