## 1 Introduction  
Drought is the most costly disaster that can affect natural habitats, ecosystems, agricultural systems, and urban water supplies [1; 2]. As a result, many techniques for monitoring drought conditions have been developed. The complex drought phenomenon can be simplified into a drought index, which is a single number incorporating a large amount of water supply and water demand data [3]. A variety of drought indices have been developed [1]. Many were derived using station-based measurements of temperature and precipitation, such as the Palmer drought severity index (PDSI), the moisture anomaly index (Z-index) [4], and the standardized precipitation index (SPI) [5]. These drought indices can effectively evaluate drought conditions around meteorological stations, but the lack of continuous spatial coverage limits the ability to characterize and monitor the detailed spatial pattern of drought conditions on a regional scale, especially in areas with few meteorological stations or a high degree of spatial variability [2]. Remote-sensing technology has made it possible to monitor soil moisture and the condition of vegetation across large areas. In locations with a limited number of sampling gauges, remote sensing data may be the only available information source for drought monitoring [6; 7]. Satellite-based drought indices such as the normalized difference vegetation index (NDVI)-based vegetation condition index (VCI) [8] have been widely used for detecting the onset of drought and measuring the intensity, duration, and impact of drought globally [8; 9; 10; 11; 12; 13; 14; 15]. The obvious advantage of VCI is that it can be easily computed owing to the fact that it does not require station observation data, and as a satellite-based drought product it can provide near real-time data over the globe at a relatively high spatial resolution [16]. The utility of the NDVI-derived VCI for monitoring drought conditions has been studied in various regions around the world and has been shown to be strongly correlated with agricultural production in regions of South America, Africa, Asia, North America, and Europe, particularly during the critical periods of crop growth [17]. The VCI based on NDVI has also been compared with vegetation density, biomass, and field reflectance measurements in Kazakhstan and has proven to be a good indicator of the impact of weather on vegetation conditions and health [18]. However, other studies have shown that the NDVI-derived VCI should be used with caution. When the NDVI-derived VCI and other satellite-derived drought indices were compared to station-based drought indices over the desert and desert scope regions of Mongolia, they showed little agreement [19]. A similar situation was observed in India, where the NDVI-derived VCI alone was shown to be insufficient for drought monitoring [14]. The NDVI-derived VCI was also tested in different counties in Texas, U.S.; the correlations between the NDVI-derived VCI and _in situ_ drought indices varied greatly among counties [20]. The aim of most studies has been to establish drought indices with simple input data, and only a simple calculation [21]. [PERSON] _et al._[17] established VCI and temperature condition index (TCI) with a simple and efficient formula. Later, the widely used precipitation condition index (PCI) and soil moisture condition index (SMCI) which were derived from the Tropical Rainfall Measuring Mission (TRMM) data and ARMS-E data respectively, were also established based on the same formula [2]. For vegetation index, most of the studies focused on the VCI derived from NDVI, whereas few studies have considered drought monitoring using other easily calculated vegetation indices. The vegetation index based on the universal pattern decomposition method (VIUPD), which was established by [PERSON] _et al._[22], has several advantages over other vegetation indices. Like NDVI, VIUPD is also based on satellite-based data and can provide near real-time data over the globe at a relatively high spatial resolution.