## 1 Introduction  
Droughts, along with floods, are some of the most common and inevitable natural disasters faced by human beings [1, 2, 3, 4]. Therefore, many researchers have been trying to monitor and predict droughts accurately, and the development of drought monitoring techniques based on satellite remote sensing (RS) data (as a representative method) has garnered special interest in recent years [5, 6, 7, 8, 9]. The onset and magnitude of drought in the region is still a challenge for researchers because of a lack of ground meteorological observatories [4]. However, satellite-based RS data partially solve the problem by providing information in a fast and cost-effective way. The advantage of RS-based monitoring using satellite data is that it is possible to monitor droughts in large areas and ungauged basins, and we can utilize multiple satellite imagery data to have accurate results; therefore, monitoring drought by using satellites has proven to be an efficient and reliable tool [6, 10, 11, 12, 13]. There are four kinds of droughts in the academic sense: meteorological, agricultural, hydrological droughts, and their socioeconomic impacts [2, 14, 15, 16]. A meteorological drought is caused by a deficit through the shortage of rainfall and is mainly a short-term drought event [6]. An agricultural drought is determined based on the vitality of vegetation and the pattern of quantitative changes in soil moisture; it indicates short or medium-term drought situations [6, 13]. A hydrological drought is commonly a mid or long-term drought condition; this drought identification is made based on a shortage of water resourcesrequired by human-environmental systems, such as river discharge, efficient water levels of dams, and reservoir storage [17]. A socioeconomic drought consists of a wide range that takes meteorological, agricultural, and hydrological droughts into account and is characterized by the temporal and spatial processes of water demand and supply [18]. A variety of drought indicators that help prevent disasters and reduce and allocate water resources have been developed to quantify different drought conditions, such as severity, duration, and frequency [3; 5; 17; 19; 20; 21; 22; 23; 24; 25]. The standardized precipitation index (SPI; [19]) and the Palmer drought severity index (PDSI; [26]) are the most commonly used meteorological drought indices. The SPI standardization concept was also applied to other drought indices, such as the standardized runoff index (SRI; [20]) and standardized soil moisture index (SSI [22]). Because RS technology provides an alternative approach for analyzing drought events across a wide range of regions, many studies have introduced RS-based drought indices [5; 7; 23; 24; 27]. [PERSON] and [PERSON] [27] proposed the microwave integrated drought index (MIDI) to monitor meteorological drought over semi-arid regions and the continental United States of America. [PERSON] et al. [23] calculated the normalized differences vegetation index (NDVI) and land surface temperature (LST) data to monitor the effects of drought on vegetation in real-time. [PERSON] et al. [24] analyzed Korea's drought conditions through a comparative analysis of the existing drought indices (SPI and PDSI) based on a satellite image-based drought index from 2004 to 2013. It was confirmed that the results of the evaporative stress index (ESI), and the energy-based water definition index (EWDI) showed high applicability for severe drought situations since 2010. [PERSON] et al. [5] selected three widely used satellite drought indices as indicators suitable for drought monitoring in northeastern China and investigated the spatiotemporal patterns and trends of rainfall and drought; the indices were normalized monthly precipitation anomaly percentage (NPA), vegetation health index (VHI), and normalized vegetation supply water index (NVSWI). [PERSON] et al.