## 2 Study Area and Materials
### Materials
#### 2.2.1 Remote Sensing Data  
The Tropical Rainfall Measuring Mission (TRMM) was a joint international project developed by National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) to study weather and precipitation [39]. TRMM3B43 monthly mean grid precipitation data are the precipitation product of the TRMM satellite, other satellites and ground observation. This dataset makes the best use of the existing detection data and provides an optimal estimate of precipitation per grid for each standard observation time, especially with a good suitable for mid-latitude areas. The alternate TRMM precipitation abstracts were accumulated monthly. Additionally, precipitation data were also used to calculate SPI.  
The monthly SMC datasets were obtained from the National Tibetan Plateau Data Center. It is made from 3 passive microwave remote sensing data: Level 3 SM data from The Japan Aerospace Exploration Agency (JAXA), and soil moisture data for SMOS products developed by the French Institute of Agricultural Sciences (INRA) and the French Centre for Space Biosphere Research (CESBIO) [40]. The value of its spatial resolution was unified as 1 km, and then we calculated the SMCI using the monthly soil moisture.  
Terra MODIS LST (MOD11A2) and NDVI (MOD13A2) with a resolution of 1 km were obtained from NASA and EOSDIS, respectively. The TGI and VCI, which were used to construct the TVMPDI, were determined by using the cumulative monthly LST and Enhanced Vegetation Index (EVI), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Type** & **Production** & **Time** & **Spatial Resolution** & **Temporal Resolution** & **Description** & **Source** \\ \hline MODIS & MOD11A2 LST & 0.008333\({}^{\circ}\) & 8-Daily & & NASA \\  & MOD13A3 EVI & & & Monthly & (accessed on 22 March 2022) \\ TRMM & TRMM 3B43 precipitation & 0.25\({}^{\circ}\) & Hourly & Calculate TVMPDI & NASA \\  & & & & & [[https://trmm.gsfc.nasa.gov](https://trmm.gsfc.nasa.gov)]([https://trmm.gsfc.nasa.gov](https://trmm.gsfc.nasa.gov)) (accessed on 22 March 2022) \\ SMC dataset & Soil Moisture in & & 0.05\({}^{\circ}\) & & National Tibetan Plateau Data Center \\  & China dataset & & & & [[http://data.tpdc.ac.cn/zh-hans/](http://data.tpdc.ac.cn/zh-hans/)]([http://data.tpdc.ac.cn/zh-hans/](http://data.tpdc.ac.cn/zh-hans/)) \\ GPP & Gross Primary & 2003–2018 & 0.05\({}^{\circ}\) & & As agriculture and Meteorological & (accessed on 23 March 2022) \\  & Production data & & & & Meteorological \\ SPEI & SPEI base Dataset & 0.5\({}^{\circ}\) & Monthly & Drought Proxy & Global SPEI database \\  & & & & & [[https://digital.csic.es/handle/10261/](https://digital.csic.es/handle/10261/)]([https://digital.csic.es/handle/10261/](https://digital.csic.es/handle/10261/)) \\ meteorological & Precipitation, temperature and soil relative & & & & (accessed on 23 March 2022) \\  & moisture data sets & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used in the study.  
These data also include SMC, SPEI and GPP, which are included in Table 1. SMC were normalized and again acclimated to account for TVMPDI. The SPEI and GPP as meteorological and agronomical aridity factors for appraising the achievement of aridity indices for both droughts.