## 1 Introduction
The intensity of SIF is related to the photosynthetic capacity, thereby providing a direct indication of the dynamic variations in actual photosynthesis within plants. The correlation between SIF and gross primary productivity (GPP) was found to potentially exceed that between NDVI and GPP [34]. Furthermore, the almost linear relationship between SIF and GPP allows the utilization of SIF as a proxy indicator for GPP at the ecosystem level [35; 36]. Therefore, SIF can be effectively used as a \"probe\" of the vegetation physiological state and can reflect rapid changes in the photosynthesis capacity of terrestrial vegetation and related stress effects [3; 33; 37]. The availability of high-quality SIF products, including contiguous SIF (CSIF) and global orbiting carbon observatory-2 (OCO-2) SIF (GOSIF) datasets [36; 38], facilitated the utilization of SIF as a powerful tool for directly monitoring environmental stress, photosynthetic capacity, and productivity characteristics within regional ecosystems [39; 40; 41; 37]. For example, [PERSON], et al. [42] used GOSIF to explore the characteristics of vegetation dynamics in Africa and found that the SIF values of most vegetation areas showed an increasing trend during 2000-2017. [PERSON], et al. [43] investigated the characteristics of peak photosynthesis timing (PPT) in boreal ecosystems using CSIF and GOSIF datasets and concluded that PPT trended earlier over the past two decades due to increasing temperature. Several studies focused on the impacts of traditional slow-evolving droughs on ecological indicators (e.g., GPP and SIF) [40; 44; 45; 46]. For example, [PERSON], [PERSON] and [PERSON] [40] analyzed the characteristics of GOSIF and drought stress in the Yellow River basin in China and found that GOSIF can effectively monitor the response of vegetation growth to drought conditions in this region. [PERSON], et al. [47] found an increasing impact of warm droughs on Northern Hemisphere ecosystems using the standardized precipitation evapotranspiration index (SPEI) method and GPP data. There were also studies on the effects of flash droughs on ecosystems [37; 48]. [PERSON], [PERSON], [PERSON], and [PERSON] [8] investigated the ecosystem response to flash droughs in India based on SM and GPP data, noting that GPP was able to respond to 95% of flash droughs, with high response frequency occurring mainly in the Ganga basin and southern India. [PERSON] and [PERSON] [17] concluded that the impacts of flash droughs on global ecosystems are spatially diverse, and that flash droughs in arid areas exert greater stress on vegetation than those in humid areas. However, there is limited research on the impacts of flash droughs on ecosystems based on SIF data, especially in agricultural areas. The productivity of croplands is required for food security. In recent decades, the patterns of croplands and food production underwent significant alternations due to climate change and human activities [49; 50]. Southern China not only has a high level of economic development, but also a large area of traditional agricultural areas [51]. Despite the relatively abundant annual precipitation in the agricultural areas of southern China, this region experiences significant fluctuation in short-term precipitation. Combined with the high-water demand of crops, this predisposition increases the likelihood of flash droughs, potentially having a substantial impact on agricultural production. For example, southern China experienced a devastating flash drought event during the late summer of 2019, resulting in huge losses to crops and associated economies [16; 52]. Therefore, this study assessed the response characteristics of vegetation photosynthesis capacity to flash droughs in the main agricultural areas of southern China using high-resolution SM and SIF datasets. The objectives of this study encompassed three crucial aspects. Firstly, we aimed to investigate the patterns of meteorological, SM, and SIF variables in the major agricultural regions of southern China from 2001 to 2020.