## 1 Introduction  
Drought is a complex process involving soil, vegetation, the atmosphere, and other factors. The decrease in precipitation and the increase in air temperature will often cause a rise in surface evapotranspiration and lead to drought occurrence and intensification, which will bring significant uncertainty to drought monitoring [1]. In the context of global climate change, drought, as one of the longest and most destructive extreme climate events, has been a concern by scholars worldwide for a long time. The occurrence of large-scale drought events tends to weaken carbon deposition in terrestrial ecosystems and exacerbate the conflict between food demand and biofuel production [2]. Moreover, the occurrence of extreme drought events, even in areas with well-irrigated facilities, can also have a significant impact on food production and the social economy [3]. It is reported that by the end of the 21 st century, the global annual average air temperature is expected to rise by 0.3-4.8 \({}^{\circ}\)C, and the probability of drought occurrence will also increase under future climate scenarios, which will pose a significant risk to vegetation growth [4]; therefore, it is crucial to study the spatial-temporal evolution characteristics of drought events and evaluate their impacts quantitatively. With the rapid development of satellite remote sensing technology, it is now possible to observe drought characteristics at regional and global scales continuously, especially in areas with limited field observation data, which also provides a new way to monitor and assess the impact of large-scale drought events on surface vegetation [5]. The vegetation index obtained from remote sensing data is a quantitative indicator of vegetation greenness, which can indirectly reflect vegetation's physiological state. Presently, traditional vegetation indexes (VIs, such as normalized differential vegetation index (NDVI) and enhanced vegetation index (EVI)) have been widely used to monitor the vegetation's physiological state and evaluate the effect of water stress on vegetation productivity. Although traditional VIs can represent the physiological state of vegetation, they can only reflect the vegetation's pigment content change, which is not directly related to the actual photosynthesis of plants, let alone the evolution of their instantaneous photosynthetic rate [6]; therefore, the response of traditional VIs to the situation of vegetation water loss is slow, cannot reflect the dynamic response process of vegetation to various water stresses immediately and has an evident lag in capturing vegetation drought information, which highly restricts its applicability in early drought monitoring [7; 8]. Solar-induced chlorophyll fluorescence (SIF) is a spectral signal (650-800 nm) emitted by the photosynthetic center of chlorophyll molecules in the form of fluorescence under solar illumination conditions, with two peaks in the red band (about 690 nm) and near-infrared band (about 740 nm) [9]. Solar radiation absorbed by vegetation can be used for photosynthesis, heat dissipation, and re-release of fluorescence. Of those, chlorophyll fluorescence is the energy re-stimulated by vegetation photosynthesis after absorbing light, so it can directly reflect the dynamic changes of a plant's actual photosynthesis [10]. As a direct probe of the plant's actual photosynthesis, compared with traditional VIs, SIF can achieve rapid response and early warning when plants are subjected to environmental stress (such as water stress, heat stress, etc.) [6; 7; 8; 11]. In recent years, global SIF remote sensing datasets have been successfully retrieved from SCIAMACHY, GOSAT, GOME-2, OCO-2, TROPOMI, and other space-borne sensors, making it possible to study plant photosynthesis on a large scale and providing new data sources for regional vegetation growth and environmental stress monitoring [9; 12].