## 1 Introduction  
One of the worst and most prevalent natural catastrophes in the world is droughs. Over 35% of the world's land surface is composed of arid and semiarid areas. More than 60 nations and areas throughout the world are at risk of drought, which has the Qinghai-Tibet Plateau (QTP) yearly economic impact of USD 6 to 8 billion, a cost that is significantly greater than that of other meteorological disasters [1; 2]. Therefore, it is crucial for human civilization to accurately monitor the incidence of droughts. Drought is mainly divided into four types, namely meteorological drought, agricultural drought, hydrological drought and socio-economic drought [3]. The first occurrence of a lack of rainfall that lasts for several months and reaches a specific threshold is meteorological drought. [4]. With the continuous evaporation of water and the continuous reduction in water resources, the shortage of soil water supply gradually leads to agricultural drought. Hydrological drought [5] results from river flows and [5] water storage in water bodies that are below long-term averages. Due to water shortages, there is an excessive demand for commercial goods, which has detrimental social, economic and environmental impacts [6; 7]. Social and economic drought is the term used to describe the anomalous phenomena of a water scarcity brought on by an imbalance between the natural systems for precipitation, surface and subsurface water distribution, and human social water consumption and drainage [8]. In the early stage, traditional drought monitoring is mainly performed to conduct statistical analysis on the monitoring data of ground meteorological stations or hydrological stations, calculate the corresponding index and finally determine the drought index suitable for the region, to clarify the time, scope and degree of drought. Three of the most common drought indexes, including Palmer Drought Severity Index (PDSI) [9], SPI [10] and SPEI [11], based on one or more hydroclimatic factors, are widely used in meteorological drought monitoring. SPEI, combined with the characteristics of PDSI and SPI, can describe the impact of temperature change on drought assessment [12]. The assessment of agricultural drought is based on the lack of SM during the plant growth season. SM-based agricultural drought index mainly includes the Palmer Z index [13], CMI [6], Normalized Soil Moisture [14] and Soil Water Deficit Index (SWDI) [15]. In addition to indices based on soil moisture, vegetation-based indices have also been proposed to describe agricultural drought, such as Vegetation Condition Index (VCI), Normalized Difference Vegetation Index (NDVI) or GPP [16; 17; 18]. Droughs are generally affected by a variety of factors, such as precipitation, temperature and soil moisture. Many researchers have realized that using multiple variables or parameters can improve the accuracy of dryness assessment [19]. The common comprehensive remote sensing dryness index includes monitoring land surface temperature (LST), vegetation, precipitation, evapotranspiration and soil moisture [20; 21]. In addition to these dryness indices, with the Temperature Vegetation Drought Index (TVDI) [22], it was found that the spatial relationship between NDVI and Ts was triangular when the range of vegetation cover change was large and the soil moisture change ranged from lack to sufficient. With the progress of related research, other normalized indicators, such as the Soil Moisture Condition Index (SMCI) [23] and Precipitation Condition Index (PCI) [24], were introduced to the comprehensive remote sensing dryness indices. The present paper assesses how to fairly balance each indicator in the composite index, including the Scaled Drought Condition Index (SDCI) and Synthesized Drought Index (SDI) [25; 26]. The principal component analysis of SDI was calculated, and the first principal component was emphasized, while other useful drought information was ignored. Thus, the correlation between the two indices decreases [27].