## 2 Materials and Methods
### Data and Preprocessing  
The primary data sources for this study were meteorology, remote sensing, soil, historical disasters, and yield data (Table 1). The SPEI was calculated using meteorological data from 113 weather stations in NEC, including daily precipitation, maximum and minimum temperatures, sunshine duration, wind speed, and average relative humidity. The VCI, TCI, and VHI were computed using the normalized difference vegetation index (NDVI) and the brightness temperatures (BT) extracted from remote sensing data. SMCI was calculated using root-zone soil moisture data obtained using data assimilation techniques based on satellite and reanalysis data. The SIF data were used to determine the weights of the atmospheric, vegetation and soil drought indices. The scPDSI, historical disaster data (area of drought-affected agricultural regions with total grain yield reduction exceeding 10% were used to evaluate the impact of agricultural drought) [39; 40] and yield data were used to verify the validity of the CDI and hazard indices. All raster data were resampled to the same spatial and temporal resolution (4 \(\times\)4 km, monthly) using ArcGIS10.4 software.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Data Contents** & **Resolution** & **Time Span** & **Data Sources** \\ \hline Meteorological & Daily precipitation, temperature, & 113 weather stations & & Meteorological Data Center of China Meteorological Administration ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 17 March 2022) \\ data & sunshine, wind speed and average relative humidity, etc. & in NEC & 1960–2020 & Global Vegetation Health Products ([[https://www.stars.estio.noaa.gov/](https://www.stars.estio.noaa.gov/)]([https://www.stars.estio.noaa.gov/](https://www.stars.estio.noaa.gov/)) smcd/emb/vci/VH/index.php, accessed on 17 March 2022) \\ \hline Remote sensing & 8-day SIF & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) & 2000–2020 & Global OCO-2 SIF data set (GOSIF) ([[https://globalecology.unh.edu/data/](https://globalecology.unh.edu/data/)]([https://globalecology.unh.edu/data/](https://globalecology.unh.edu/data/)) GOSIF.html, accessed on 17 March 2022) \\ \hline \multirow{2}{*}{Remote sensing data} & \multirow{2}{*}{8-day SIF} & \multirow{2}{*}{0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)} & \multirow{2}{*}{2000–2020} & Global OCO-2 SIF data set (GOSIF) ([[https://globalecology.unh.edu/data/](https://globalecology.unh.edu/data/)]([https://globalecology.unh.edu/data/](https://globalecology.unh.edu/data/)) GOSIF.html, accessed on 17 March 2022) \\ \cline{1-1} \cline{2-5}  & & & & The Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences (RESDC) \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Land cover} & \multirow{2}{*}{\(1\times 1\) km} & \multirow{2}{*}{2010} & \multirow{2}{*}{2010} \\ \cline{1-1} \cline{5-5}  & & & & ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 17 March 2022) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data types and sources.  
Figure 1: Location (**a**) and land use type (**b**) of the study area.