# Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022
## 2 Materials and Methods
### Data  
The NDVI and GPP data from 2003 to 2022 were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) product (MOD13A3 and MOD17A2H) ([[https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)]([https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)), accessed on 15 May 2024). The MODIS NDVI product is provided at a monthly temporal interval with a spatial resolution of 1000 m [41]. The MODIS GPP product is available with a cumulative 8-day temporal frequency and a spatial resolution of 500 m [42]. The SIF data used in this study from 2003 to 2022 were sourced from the recently released global dataset of the solar-induced chlorophyll fluorescence (GOSIF) dataset. This product is based on observations obtained by the Orbiting Carbon Observatory-2 (OCO-2), using machine learning methods to derive discrete SIF measurements. It provides 8-day SIF data at a spatial resolution of 0.05\({}^{\circ}\) globally [43] ([[https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)]([https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html))). The monthly dataset was generated by applying the Maximum Value Composition (MVC) method to the GPP and SIF. The monthly mean annual temperature (MAT), mean annual precipitation (MAP), AVP, Tmax and Tmin data from 2003 to 2022 were obtained from the gridded Climate Research  
Figure 1: The spatial distribution map of aridity levels (**a**) and vegetation types (**b**) in the Eurasian drylands. WCE, EEU, the MED, WSB, ESB, WCA, ECA, TIB, EAS, ARP and SAS represent West and Central Europe, E. Europe, Mediterranean, W. Siberia, E. Siberia, W. C. Asia, E. C. Asia, Tibet Plateau, E. Asia, Arabian Peninsula and S. Asia, respectively. Unit, University of East Anglia (CRU TS v4.07) dataset [30; 44] ([[https://crudata.ue-a.ac.uk/cru/data/hrg/cru_ts_4.07/](https://crudata.ue-a.ac.uk/cru/data/hrg/cru_ts_4.07/)]([https://crudata.ue-a.ac.uk/cru/data/hrg/cru_ts_4.07/](https://crudata.ue-a.ac.uk/cru/data/hrg/cru_ts_4.07/)), accessed on 8 June 2024). The SM data were obtained through the ERA5 product, with a spatial resolution of 0.1\({}^{\circ}\). This dataset includes four layers (0-7 cm, 7-28 cm, 28-100 cm and 100-289 cm) ([[https://cds.climate.copernicus.eu/cd-sapp#](https://cds.climate.copernicus.eu/cd-sapp#)]([https://cds.climate.copernicus.eu/cd-sapp#](https://cds.climate.copernicus.eu/cd-sapp#))!/dataset/reanalysis-era5-land-monthly-means?tab=form, accessed on 12 June 2024) [45; 46]. Monthly SM was calculated by weighted average. The TWS data from 2003 to 2022 were obtained from GRACE level RL06 data provided by JPL ([[https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/)]([https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/)), accessed on 12 June 2024) [47; 48]. Missing months were supplemented using linear interpolation. All the variables above were resampled to a spatial resolution of 0.5\({}^{\circ}\) using the bilinear interpolation method. The VPD can be calculated as follow [29]:  
\[\text{VPD}=\text{SVP}-\text{AVP} \tag{1}\]  
wherein SVP can be calculated based on the following formula using \(T_{max}\) and \(T_{min}\):  
\[\text{SVP}=0.5\times\left(0.611\times\exp\left(\frac{17.3\times T_{min}}{T_{ min}+29.3}\right)+0.611\times\exp\left(\frac{17.3\times T_{max}}{T_{max}+29.3} \right)\ \right) \tag{2}\]  
The land cover dataset for 2010 was obtained from the European Space Agency (ESA) Climate Change Initiative (CCI) (www.esa-landcover-cci.org/, accessed on 20 January 2024). The ESA CCI dataset, developed according to the resolution of 300 m using a classification scheme by the Food and Agriculture Organization (FAO), categorizing land cover into 37 classes (22 global categories and 15 regional categories).