## 1 Introduction  
Drought is one of the most destructive natural disasters, and there has been wide concern about it for a long time [1; 2; 3]. In recent decades, seasonal drought has become more frequent and intensified under the background of global warming [2; 3; 4]. In particular, drought has become widespread and has long-lasting impacts on crops, such as sugarcane [5; 6]. Sugarcane needs to consume a large amount of water during critical growth periods, such as the tillering, elongation, and maturity periods, but drought occursfrequently during these periods [7]. Drought, thus, tends to seriously affect the normal growth of sugarcane during the critical growth periods, which ultimately causes serious sugarcane yield losses [8]. Therefore, timely and effective drought monitoring is essential for sugarcane management and national sugar safety. Ground experimental research can only reveal the drought condition in a small region near the observation site, but cannot monitor drought at a large spatial scale. Remote sensing technology has the characteristics of fast data acquisition, short cycle, and low cost [9; 10; 11; 12]. It can obtain long-term, large-scale, spatially continuous observation data with a fine spatial resolution [12]. Traditional remote-sensing-based vegetation indices (VIs), such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), reflect the greening rate of crops [13]. VIs can track the spatiotemporal characteristics of persistent drought, and are widely used to evaluate drought-related vegetation conditions [14; 15; 16]. For most types of vegetation, VIs generally show a downwards trend under the impacts of drought stress [13; 17; 18]. The greenness indices can only reflect changes in canopy structure, but the canopy structure remains unchanged during the early stage of drought [19; 20; 21]. Previous studies have suggested that the response of crop VIs to drought stress has a considerably long lag time [22]. For example, the response of NDVI to water stress typically lags from 10 days to 2 months [23]. Therefore, VIs are not able to capture the early response of crops to drought. The recently developed solar-induced chlorophyll fluorescence (SIF), which serves as a direct probe of photosynthesis, provides an earlier and more direct method for accurately diagnosing changes in vegetation functions [1; 24; 25; 26]. SIF is the red and far-red light (650-800 nm) emitted by chlorophyll, a pigment after light absorption for a few nanoseconds [21; 25]. SIF is different from traditional reflectance-based VIs. It contains information on the biochemical, physiological, and metabolic functions of plants, and the amount of absorbed photosynthetically active radiation (APAR) [27; 28]. Vegetation photosystems consume light from three pathways: photochemical reactions, thermal dissipation, and chlorophyll fluorescence emission [28; 29; 30]. Environmental stress affects the ability of plants to absorb APAR through photochemical reactions [31], thereby changing the fraction of absorbed energy that flows along these three pathways. Thus, SIF is related to photosynthesis and environmental stress [32; 33]. Previous studies have suggested that the photosynthesis of many crops shows a quick negative response during the early stage of drought stress [1], so SIF may have great potential for capturing the early response of sugarcane to drought stress. Atmospheric satellite sensors from several missions, including SCIAMACHY [34; 35], GOME-2 [35; 36; 37], GOSAT [38; 39; 40], OCO-2 [41; 42], and TROPOMI [43; 44; 45], have been used to observe the SIF of terrestrial vegetation. The chlorophyll fluorescence signal was mainly derived from the fluorescence emission of vegetation chlorophyll, while soil and cloud layers did not produce fluorescence signals [21; 46]. Thus, satellite SIF products are less contaminated by cloud and soil background compared with traditional satellite-based VIs [47].