## 1 Introduction  
Droughs are one of the most extreme and least understood climatic events in many regions of the world that has been responsible for significant socioeconomic and ecological consequences [1]. Climate change increases the global frequency and severity of droughs, thus exacerbating yield uncertainty and posing several risks to agriculture [2]. Consequently, there is an urgent need for obtaining a better understanding of drought detection. Historically, drought monitoring approaches have evolved from in situ station-based measurements (e.g., the Palmer Drought Severity Index (PDSI)) toward the continuous observations and monitoring of key drought-related variables at regional and global scales using remote sensing [3]. Among them, vegetation indices (VIs) have been widely used for estimating drought severity across space and serve as good indicators to monitor drought [4]. However, it is noted that greenness-based VIs are incapable of capturing dynamic changes of drought responses in shorter timescales, since these indices relate to canopy greenness directly and potentially decouple from photosynthetic functioning [5]. Solar-induced fluorescence (SIF) is an optical signal (650-850 nm) emitted from plant chlorophyll and has been used as an indicator of the actual functional state of plant photosynthesis. Recent research has demonstrated that SIF is promising for estimating gross primary production (GPP) [6], monitoring water stress [7], tracking phenological changes [8], predicting crop yield [9], and estimating leaf nitrogen content [10], among other uses. With respect to stress detection, various studies have explored the impacts of droughts on the red (e.g., F685) or far red (e.g., F760) SIF observed by the ground, airborne, and satellite platforms. For example, [PERSON] et al. [11] conducted the field experiments to acquire ratios between the 757 nm (out) and 760 nm (in) bands and revealed the good relationship between fluorescence and droughts at the canopy scale. The far-red SIF was more sensitive to VPD [7] and the short-term standard precipitation index (SPI) than the Normalized Difference Vegetation Index (NDVI) [12]. More importantly, the site-observed F760 was significantly correlated with the root zone soil moisture (SM) [13], and it outperformed the VIs for early drought detection in a short time lag, especially over a closed canopy [14]. However, the top-of-canopy SIF (SIFtoc) is only a fraction of the emitted SIF photons due to the absorption and scattering effects of SIF emission in the canopy scale, thus influencing its relationship with GPP and showing the confounding effects of the leaf optical properties and canopy structures on SIF [15]. Therefore, many efforts have been made to reduce the influence of normalizing the solar zenith angle (SZA) of SIF observations or downscaling SIF from the canopy to leaf level. Some studies [16; 17; 18] have used the bidirectional reflectance distribution function model to correct the angular effect of SIF and demonstrated a stronger relationship between SIF and GPP. Alternatively, the importance of downscaling SIF from the canopy to leaf level has attracted increasing attention recently. Generally, at the canopy scale, the amount of SIFtoc is frequently defined as  
\[\text{SIF}_{\text{toc}}=\text{PAR-fPAR-\Phi_{\text{F}}\cdot}\text{f}_{\text{ esc}} \tag{1}\]  
where PAR represents the photosynthetically active radiation received, fPAR is the fraction of PAR absorbed by vegetation, \(\Phi_{\text{F}}\) is the fluorescence efficiency, and \(\text{f}_{\text{esc}}\) is the fluorescence escape ratio (i.e., the probability of a photon escaping from the canopy in the direction of the sensor). Based on SIFtoc and \(\text{f}_{\text{esc}}\), the term of SIFtot (total canopy SIF emission) can be defined as  
\[\text{SIF}_{\text{toc}}=\text{SIF}_{\text{tot}}\cdot\text{f}_{\text{esc}} \tag{2}\]  
The core of downscaling SIF from the canopy to leaf level is to estimate \(\text{f}_{\text{esc}}\).