## 2 Materials and Methods
### Data  
SMCI 1.0 (SM of China based on in situ data, version 1.0) utilized various factors (e.g., ERA5-land meteorological data, land cover, and soil data) to predict SM through the random forest method. The results of SMCI 1.0 were validated by 1648 local observation stations [55]. SMCI 1.0 can provide important supplementary data to the SM derived from model and remote sensing techniques for a more accurate meteorological, hydrological, and ecological process. SMCI 1.0 provided high spatio-temporal resolution of SM with 10 depth layers at 10 cm intervals. To facilitate analysis and understanding, we treated the SM of the SMCI 1.0 set at n cm as the average SM from n \(-\) 10 cm to n cm, e.g., the SM at 10 cm as the average SM from 0 to 10 cm, and the SM at 50 cm as the average SM from 40 to 50 cm. We mainly used the data from SMCI 1.0 (the top five soil layers, i.e., 0-50 cm) on a 9 km (0.1\({}^{\circ}\) spatial resolution) daily SM from 2001 to 2020 to identify flash droughs. We calculated the 0-30 cm and 0-50 cm SM (SMCI 1.0) using the soil layer thickness weighting methods. ERA5 is an enhanced iteration of ERA-Interim, integrating multiple data sources through a 4D-Var assimilation technique to guarantee the data quality [56; 57; 58]. In this study, ERA5 SM included 0-7 cm and 7-28 cm layers during 1981-2020, allowing comparison of flash drought data with the SMCI 1.0 data. We aggregated ERA5 SM values of 0-28 cm via the soil layer thickness weighting methods. ERA5 SM has an hourly time scale and a spatial resolution of 0.25\({}^{\circ}\). To facilitate the comparison and analysis with the SMCI 1.0 data, we processed the ERA5 SM hourly data into daily series, and then processed it to 0.1\({}^{\circ}\) spatial resolution by the bilinear interpolation method. Figure 1: Location of the four major agricultural regions in southern China (UYR: the upper Yangtze River region; MYR: the middle Yangtze River region; LYR: the lower Yangtze River region; and SCR: the South China region). Daily 0.25\({}^{\circ}\) gridded observational meteorological data (CN05.1) for 2001-2020 were available from the Climate Change Research Center of the Chinese Academy of Sciences. These observations encompassed precipitation (P), relative humidity (RH), wind speed, maximum temperature (T\({}_{\rm max}\)), and minimum temperature (T\({}_{\rm min}\)). The CN05.1 dataset was compiled utilizing the anomaly approach, drawing upon observed data from approximately 2400 meteorological stations across China [59]. This set of observations was of high quality and was widely used in Chinese meteorology studies [60; 61; 62]. To minimize the error due to spatial mismatch, we uniformly resampled the CN05.1 data to 0.1\({}^{\circ}\)spatial resolution by the bilinear interpolation approach. We employed two sets of SIFs and one set of GPP data during 2001-2020 to explore the impact of flash drought on vegetation photosynthesis in the agricultural areas of southern China. The CSIF data were produced utilizing a neural network algorithm that incorporated surface reflectance from MODIS and SIF from OCO-2 [38]. The CSIF data were obtained from the National Tibetan Plateau/Third Pole Environment Data Central in China ([[https://data.tpdc.ac.cn/zh-hans/data/d7](https://data.tpdc.ac.cn/zh-hans/data/d7) cccf31-9 bb5-4356-88a7-38c5458f052b]([https://data.tpdc.ac.cn/zh-hans/data/d7](https://data.tpdc.ac.cn/zh-hans/data/d7) cccf31-9 bb5-4356-88a7-38c5458f052b), accessed on 5 July 2024). These data were collected under clear sky conditions, with a temporal resolution of four days and a spatial resolution of 0.05\({}^{\circ}\). GOSIF data were developed through a data-driven method based on discrete OCO-2 SIF soundings, MODIS data, and meteorological reanalysis data [36].