To evaluate their frequency, duration and severity, drought indices, such as the standardized precipitation index (SPI) [15], the soil moisture drought index (SMDI) and the standardized runoff index (SRI) are the most widely used methods [15; 16; 17]. Many studies based on different drought indices have been conducted in China for recent decades. For instance, the propagation of meteorological drought to hydrological drought under the influence of human activities in the Hai River basin of the North China was simulated by [PERSON] et al. [18] based on the SPI and SRI. [PERSON] et al. [19] combined a multi-dimensional Copula function hydrological approach with the SRI to assess hydrological drought risk in arid inland basins of China. Results showed that the study area was dominated by mild to moderate hydrological droughts in 1961-2018. [PERSON] et al. [20] also investigated the transition mechanism from meteorological drought to hydrological drought for major basins of China from 1961 to 2014 using the SPEI and SRI. However, these studies usually forced on a single or a few basins and it is difficult to reveal the patterns of hydrological drought across China. Moreover, as the most basic data for drought assessment, river runoff is very difficult to obtain in mountainous or highland areas and international river basins in China [21], which restricts comparative analysis of hydrological drought in multiple basins at national scale. To overcome this obstacle, hydrological modelling is the most widely used method. However, for many areas, information on model inputs such as accurate precipitation and river morphology is often insufficient. High-resolution modelling usually relies on massive computational resources [22; 23], especially in the Qinghai-Tibet Plateau region, where the presence of glacial snow and frozen earth, as well as the lack of meteorological data and complex hydrological cycle mechanisms further constrain the application of hydrological models. Meanwhile, with the rapid development of remote sensing technology, real-time or near-real-time satellite data of high efficiency, wide observation range and large volume have been widely used in earth science research, including vegetation classification, land use/cover (LULC) planning, environmental pollution and disaster monitoring [24]. Remote sensing can provide another effective way to obtain basic information to simulate river runoff for drought assessment. To date, applications of using remote sensing spatial information in hydrological cycle research have recently gained popularity in hydrological research [25; 26; 27]. Compared with optical remote sensing, microwave remote sensing is basically not restricted by meteorological conditions and can observe and monitor surface changes day and night. In particular, satellite network equipped with microwave sensors can obtain daily global coverage data and minimize the interference of non-water features in runoff simulation, which has obvious advantages and can provide a new way to obtain data for drought studies in areas where data are scarce [28; 29]. Furthermore, brightness temperature (\(T_{b}\)), a fundamental parameter measured by the satellite microwave radiometer traveling upward from the Earth's surface to the satellite, is one of the core methods used in river runoff simulation based on passive microwave remote sensing [30; 31]. The radiation properties of the ground surface can be reflected by \(T_{b}\). For example, \(T_{b}\) of water bodies is often lower than other land features, which can be adopted for water body detection and runoff monitoring by methods such as threshold classification [32]. Many institutes, including the National Aeronautics and Space Administration (NASA), the National Snow and Ice Data Center (NSIDC) provide real-time data download service of \(T_{b}\) observations for free, whose time period can date back to 1979.