## 1 Introduction  
Agricultural drought poses a direct threat to both crop growth and global food security [1, 2]. Within the context of global climate change, drought events are projected to occur with increasing frequency, leading to increased water stress in crops [3]. Therefore, real-time crop water stress monitoring is crucial for achieving precision irrigation and food security. Remote sensing is an essential tool for monitoring the impact of large-scale droughs on agricultural ecosystems [4]. Monitoring the impact of drought on crops typically involves the use of remote sensing observations related to vegetation structure or physiology. Among available indices, those related to the vegetation structure, such as the normalized difference vegetation index (NDVI) [5], reflect key characteristics, including the leaf area index (LAI) and overall vegetation coverage [6]. Therefore, these indices are effective for monitoring declines in vegetation greenness caused by drought [7]. Water deficits lead to stomatal closure and affect leaf physiology. These physiological changes can also be detected via remote sensing techniques [8, 9]. For example, the solar-induced chlorophyll fluorescence (SIF) is a reliable indicator for monitoring photosynthetic activity in crops [10]. On the basis of remote sensing observations related to vegetation structure or physiology, the statistical comparison approach has commonly been adopted to evaluate drought levels. This method involves the detection of anomalies caused by water deficits. Remote sensing measurements over a specific period when crops are potentially stressed are usually compared with multiyear historical statistical values for the same period to assess vegetation water stress during that period. For example, the vegetation condition index (VCI), built upon the NDVI, is highly correlated with monthly water stress changes [11]. However, owing to the impact of historical stress, i.e., the stress experienced by vegetation prior to the measurement period, the chlorophyll content and LAI may not always recover to the normal levels observed in previous years, even after adequate soil moisture becomes available during the measurement period. As a result, the VCI may still fall below the values associated with historically healthy vegetation. When the statistical comparison approach is adopted, historical stress can lead to uncertainty in accurately assessing the water stress status of vegetation during the current measurement period. The diurnal variations in remote sensing observations related to vegetation physiology and structure hold promise for eliminating the influence of historical stress, thus enabling accurate daily water stress monitoring [12]. Since the SIF is more sensitive to water stress changes than are indices such as the NDVI and enhanced vegetation index (EVI) [13, 14, 15], recent research on the use of diurnal variations to monitor vegetation daily water stress levels has focused primarily on the SIF and its derived parameters. For example, [PERSON] et al. measured daily variations in the water status via the diurnal characteristics of the far-red SIF [16]. The observed canopy SIF contains integrated information related to the photosynthetically active radiation (PAR), the fraction of PAR absorbed by vegetation (fPAR), the canopy fluorescence emission efficiency (\(\Phi\)F\({}_{\text{canopy}}\)), and the escape probability (f\({}_{\text{esc}}\)), which describes the proportion of light that is not reabsorbed by leaves within the canopy. The observed canopy SIF can be expressed as follows:  
\[\text{SIF}=\text{PAR}\times\text{fPAR}\times\Phi\text{F}_{\text{canopy}} \times\text{f}_{\text{esc}} \tag{1}\]  
where the \(\Phi\)F\({}_{\text{canopy}}\) is related to the vegetation physiology, whereas the f\({}_{\text{esc}}\) and fPAR are associated with the canopy structure [17]. Notably, the variation in PAR is not related to water stress.