## 1 Introduction  
Drought is one of the most serious natural disasters with wide distribution, long duration, and recurrent frequency, and it has become an urgent issue worldwide [1, 2]. Drought severely affects food security, economic development, biological health, and even the course of civilizations [3, 4, 5]. Between 2000 and 2019, approximately 18% of the global population was affected by drought, leading to 16,000 deaths annually and economic lossesof 6.2 billion dollars per year [6]. Owing to droughts, annual crop production losses also reached 3 billion dollars in the past decade [7]. In China, the average drought-related crop yield losses exceed 30 million tons annually [8]. Climate change and human activity are expected to intensify drought conditions, potentially displacing 700 million people by 2030 and 216 million by 2050 [6]. Given these far-reaching influences, effective drought monitoring is essential for early warning and mitigation strategies. Drought manifests as an imbalance in the water cycle and is commonly categorized into meteorological, hydrological, agricultural, and socioeconomic types [9; 10; 11]. Precipitation (PRE), vapor pressure deficit (VPD), runoff, temperature, the vegetation spectrum, and other related factors have been employed to quantitatively monitor and assess droughts. Drought indices are vital for quantifying drought intensities, spatiotemporal characteristics, and evolutionary trends. To date, numerous drought indices have been established to monitor meteorological, hydrological, agricultural, and socioeconomic droughts, but each has notable limitations. Meteorological indices such as the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and standardized precipitation evapotranspiration index (SPEI) are widely used. However, they suffer from low spatial resolution due to reliance on station-based data, limiting their effectiveness in large or data-sparse regions [12; 13; 14; 15]. While the SPEI integrates both precipitation and evapotranspiration, its spatial resolution remains inadequate for comprehensive drought monitoring. Hydrological drought indices like the standardized runoff index (SRI) and the standardized streamflow index (SSI) focus on runoff/streamflow anomalies, but their accuracy depends on the quality of runoff data [16; 17]. Satellite-based indices, i.e., the GRACE drought severity index (GRACE-DSI), offer better spatial coverage but face challenges with temporal resolution and data gaps in certain regions [18]. Socioeconomic drought indices, including the Multivariate Standardized Reliability and Resilience Index (MSRRI), Socioeconomic Drought Index (SEDI) and standardized water supply and demand index (SWSDI), often struggle with socioeconomic data integration and are limited by data availability and complexity [19; 20; 21; 22]. Agricultural drought, closely linked to food security, is defined as a deficit in soil moisture (SM) caused by abnormal meteorological conditions, such as insufficient precipitation, reduced evapotranspiration, and increased land surface temperature (LST) [23]. These SM shortages further impact crop photosynthesis, growth, and yields [24]. Thus, monitoring SM is critical and can be achieved through in situ measurements [25]; satellite-based models (e.g., SMAP) [26]; optical, thermal, and microwave sensors [27]; and global navigation satellite systems (GNSSs) [28; 29]. Meteorological indicators, e.g., the PDSI, are also used to monitor agricultural drought, as they correlate with SM anomalies. Drought limits crop growth and yields and is characterized by low SM and a high VPD, which inhibits plant photosynthesis and growth by causing stomatal closure [30]. VPD, which represents the difference between saturated and actual vapor pressure, is influenced by both the atmospheric temperature and humidity and has gained popularity as an indicator for agricultural drought monitoring [31; 32].