## 2 Materials and Methods
### Datasets
#### 2.2.2 Climate Data  
This study employed the filtered and spatially processed remotely sensed climate data such as the monthly precipitation and land surface air temperature (Tmean, Tmax and Tmin), which were freely downloaded from the Climate Engine data source powered by Google Earth Engine at 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolution [32]. The other climate datasets used including soil moisture (SM), vapor pressure deficit (VPD), land surface temperature  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Sources**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Factors**} & \multicolumn{2}{c}{**Resolutions**} \\ \cline{3-5}  & & & **Spatial** & **Temporal** \\ \hline ISR & PAR & Vegetation status & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & daily \\ MODIS & FPAR & Vegetation status & 500 m & 8 days \\ NDVI & Vegetation indices & 500 m & 16 days \\  & EVI & Vegetation indices & 500 m & 8 days \\  & Land cover & Vegetation types & 500 m & yearly \\  & LSWI & Soil water content & 500 m & 8 days \\  & LAI & Vegetation canopy & 500 m & 16 days \\  & GPP (17A2H) & Carbon flux & 500 m & 8 days \\ Global FLUXNET & GPPEC & Carbon flux & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & daily \\ TERRACLIMATE & Vapor pressure deficit & Soil water content & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Soil moisture & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Wind speed & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Shortwave radiation & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Longwave radiation & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Land surface air & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & temperature & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Solar radiation & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Soil heat density & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Evaporungspiration & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\ CLIMATEENGINE & Precipitation & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & Air temperature & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\  & (Tmean, Tmin, Tmax) & Climate & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) & monthly \\ \hline \hline \end{tabular} MISR: Multi-angle Imaging SpectroRadiometer; MODIS: Moderate Resolution Imaging Spectroradiometer; FPAR: Fractional Photosynthetic Active Radiation (PAR); NDVI: Normalize Difference Vegetation Index; EVI: Enhanced Vegetation Index; LSWI: Land Surface Water Index; LAI: Leaf Area Index; GPP: Gross Primary Productivity; mean, tmax and tmin are mean, maximum and minimum temperature (\({}^{\circ}\)C), respectively.  
\end{table}
Table 1: Summary of datasets used in this study.  
(LST), solar radiation (shortwave and longwave radiations), daily wind speed, potential evapotranspiration (PET) and evapotranspiration (ET) were acquired and freely downloaded from Terra Climate databases at 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolutions [33]. These climate datasets were selected as GPP-LUE models' climate inputs and used to estimate the aridity index and analyse the effects of climate characteristics on GPP variations.