###### Abstract
Two of the most popular indicators for tracking meteorological droughs are the PDSI and the SPI. The PDSI is a drought index based on a water balance model that combines precipitation and evapotranspiration to capture the effects of global warming. The scPDSI has been improved to make it comparable across different climate zones [19]. In contrast to the SPI, which considers only precipitation, the SPEI considers both precipitation and potential evapotranspiration, capturing the impact of temperature rise on water demand [20]. However, these drought indices lack spatial continuity coverage and are insufficient for characterizing and monitoring the detailed spatial distribution of drought conditions, especially in areas where meteorological stations are sparse and unevenly distributed. Remote sensing technologies enable the monitoring of soil moisture and vegetation conditions over large areas, extend the methods of drought monitoring, compensate for the disadvantages of spatial discontinuities in site-based drought indices, and capture temporal and spatial changes in surface states on a large scale [1]. The normalized difference vegetation index (NDVI) [23], vegetation condition index (VCI) [24], temperature condition index (TCI) [25], soil moisture condition index (SMCI) [26], and vegetation health index (VHI) based on vegetation and temperature [27], are commonly used for agricultural drought monitoring with good effects [28]. However, these indices have limitations. The vegetation index is an excellent visual indicator for plants after drought stress, but there is a lag in how quickly it responds to soil and meteorological drought. Other meteorological disasters, pests, and diseases can also cause a decline in the vegetation indices [1]. Additionally, the loss of land surface temperature (LST) product data caused by cloud pollution [29] may compromise the capacity to continue conducting efficient drought monitoring. Soil moisture is one of the most important indicators of agricultural droughs. However, the response of various crops to drought are not sufficiently reflected by soil moisture indicators, which only reflect the root environmental conditions for crop growth. The revisit cycle and consideration of only the surface layer (0-5 cm) in the satellite inversion of soil moisture restricts its ability to monitor drought. The data assimilation technique combines the benefits of remote sensing monitoring and model simulation, which can improve the temporal and spatial resolution of the observed data [30]. Agricultural drought is a complex process that involves several factors that may promote crop growth, such as meteorological conditions, crop biology, and soil properties. However, the above indicatorsgenerally ignore the physical mechanisms along the soil-plant-atmosphere continuum (SPAC) [31]. Therefore, a comprehensive drought index (CDI) based on multi-source information must be developed to monitor agricultural drought based on meteorological drought, soil drought, and vegetation response to drought to overcome the limitation of a single drought index to monitor the complex process of drought stress and to fully capture the various drought-influencing factors. Because of its simple calculation, the CDI, which is created by integrating multi-source information such as meteorology, remote sensing, and data assimilation, and assigning values to different drought index weights using expert empirical weights [32], equal weights [33], correlation analysis [26], principal component analysis [34], and the entropy weighting method [31], is widely used for drought monitoring. In recent years, it has been suggested that monitoring plant photosynthesis from space using solar-induced chlorophyll fluorescence (SIF) is a promising technique. The SIF is regarded as an indicator of the functional state of plant photosynthesis. SIF has been demonstrated to be more representative of gross primary production (GPP) than vegetation index, with a higher sensitivity to environmental stress [35].