## 1 Introduction  
Net primary productivity (NPP) represents the fixation and conversion efficiency of photosynthetic products in plants, determines the net carbon input to terrestrial ecosystems, and is a key process in the carbon cycle [1]. With ongoing global climate change, the frequency and severity of droughs are increasing, leading to significant losses in terrestrial vegetation's NPP [2, 3, 4]. Moreover, it also amplifies the uncertainties associated with carbon uptake and storage in terrestrial vegetation ecosystems [5, 6]. Therefore, the real-time monitoring of drought-induced dynamic changes in vegetation NPP is an urgent issue that needs to be addressed. Currently, regional- and global-scale NPP data are primarily obtained through mathematical model estimation and inversion. Predictive models for NPP estimation, such as the Carnegie-Ames-Stanford approach (CASA) and global production efficiency model (GLO-PEM), have been widely applied [7, 8, 9, 10]. Furthermore, research has been conducted to utilize the normalized difference vegetation index (NDVI) for the estimation of NPP [11, 12]. However, NPP derived from these models is uncertain and has limitations. First, fully quantifying the parameters that influence NPP changes during the estimation and inversion processes is difficult, leading to differences in the actual NPP. Second, spatial heterogeneity factors, such as vegetation coverage and water use efficiency, can introduce uncertainty into data inversion and estimation processes [13]. In addition, NPP derived from models relies on historical observation data and cannot satisfy research requirements for real-time NPP monitoring [14]. Solar-induced chlorophyll fluorescence (SIF), as a fluorescence signal of vegetation photosynthesis, exhibits great potential in monitoring drought-induced stress on vegetation [15; 16]. Compared with traditional vegetation indices such as NDVI and enhanced vegetation index (EVI), SIF has the advantages of faster and more sensitive reflection of changes in vegetation physiological parameters. It can effectively characterize the impact of environmental stress on vegetation [17; 18]. SIF shows tremendous potential in monitoring the effects of high temperature on crop stress and yield [19], the resistance and resilience of large-scale vegetation to drought [20], and provides a new perspective for studying the impact of drought on vegetation carbon uptake [21; 22]. Furthermore, SIF has shown tremendous potential in estimating and monitoring vegetation productivity in real time [23; 24; 25]. Current research has demonstrated a close linear relationship between SIF and gross primary productivity (GPP) [26]. Combining SIF with proxy data on biological characteristics can appropriately capture vegetation dynamics and interannual variations under various climatic conditions [27; 28]. Meanwhile, NPP is commonly considered as the portion of GPP that remains after subtracting vegetation respiration and exhibits a stable and close linear relationship with GPP [29; 30]. The superiority of SIF in GPP-related studies together with the close relationship between GPP and NPP indicate that SIF also holds potential for characterizing and monitoring dynamic changes in NPP [31]. However, no studies have examined the potential relationships between SIF and drought-induced dynamic changes in NPP. As research on SIF in vegetation productivity continues to advance [32; 33], new opportunities arise for real-time monitoring of NPP dynamics [29; 34; 35]. Therefore, we established a novel ratio index, SIF/NPP, which links SIF with NPP for the first time. This index quantifies the relationship between SIF and NPP on an 8-day time scale. By analyzing the changes in the SIF/NPP ratio, we aim to investigate the relationship between SIF and NPP, validate the feasibility of using SIF to monitor NPP, and discuss the potential of SIF in monitoring the dynamic changes in NPP induced by drought. Drought plays a crucial role as a significant factor causing loss in NPP.