## 1 Introduction  
Drought is a frequent and potentially devastating natural phenomenon that directly impacts social and economic development, industrial and agricultural production, urban and rural water supply, people's lives, and the ecological environment [1, 2, 3, 4]. Its prolonged and destructive nature has long been a subject of scholarly concern [5]. Drought has recently emerged as a worldwide concern [6]. Drought refers to a condition where the supply of water fails to meet the demand [7]. Currently, drought is commonly categorized as meteorological, hydrological, agricultural,and socioeconomic droughts [8]. Meteorological drought pertains to abnormal water scarcity resulting from an imbalance between precipitation and evaporation; agricultural drought relates to abnormal water scarcity caused by an imbalance between soil moisture and crop water requirements; hydrological drought signifies abnormal water shortage due to an imbalance between precipitation and groundwater availability; socioeconomic drought denotes an uneven supply and demand of water resources within natural and human socioeconomic systems [2; 9; 10]. Among these aforementioned types of droughts, agricultural drought presently stands as the most prevalent form of this phenomenon today [11]. Agricultural drought poses a direct threat to crop production [12]. The research on drought monitoring commenced in the late 1960s, and conventional methods for monitoring drought utilized data from stations, such as precipitation, relative soil moisture, temperature, and air pressure, to assess parameters like the onset, extent, and severity of agricultural drought [13; 14]. The site-based observation method primarily characterizes drought based on observed precipitation and other factors at specific locations [15]. The most representative methods and indices among them are the Standardized Precipitation Index (SPI) [16], Standardized Precipitation Evapotranspiration Index (SPEI) [17], and Palmer Drought Severity Index (PDSI) [18]. The monitoring method based on station data can provide long-term observational data, which are more conducive to the sustained and continuous observation of drought [19]. However, limitations arise in station-based monitoring due to the sparse distribution of stations in certain regions [20]. With the continuous progress and advancement of remote sensing technology, an increasing number of vegetation indices are being employed for drought monitoring [21], such as the Normalized Differential Vegetation Index (NDVI) [22; 23], Vegetation Condition Index (VCI) [24], Temperature Condition Index (TCI) [25], Standardized Soil Moisture Index (SSI) [26], Soil Moisture Condition Index (SMCI) [27], Normalized Difference Water Index (NDWI) [28] and Normalized Multi-band Drought Index (NMDI) [29]. These indices directly reflect terrestrial plant greenness and crop growth status [30]. However, these vegetation indices may introduce a lag effect [2]. In recent years, an increasing number of scholars have been directing their attention towards utilizing multi-source remote sensing data for the establishment of drought indices, such as The Vegetation Health Index (VHI) [25], the Scaled Drought Condition Index (SDCI) [31], the Microwave Integrated Drought Index (MIDI), the Synthesized Drought Index (SDI), the Optimized Meteorological Drought Index (OMDI) [32], the Optimized Vegetation Drought Index (OVDI) [32], etc. The accuracy of monitoring has been somewhat enhanced by these drought-monitoring indices. Solar-induced chlorophyll fluorescence (SIF) can reflect changes in photosynthesis [33]. It can characterize the response of plants to leaf and canopy water content [34; 35], and is closely related to the photosynthesis of vegetation [33; 36]. Furthermore, SIF has been shown to correlate strongly with gross primary productivity (GPP) and drought [37; 38]. SIF can serve as a valuable tool for characterizing plant responses to leaf and canopy water content, given its close association with vegetation photosynthesis [39].