## 3 Results
### Correlations between GPP and LAI and Climate Variables  
We explored the relationships between the GPP and the other variables in each pixel through partial correlation analysis (Figure 10). The results revealed that the correlations between the GPP and the different variables exhibited an obvious spatial distribution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Returning**} & \multicolumn{2}{c}{**Artificial**} & \multicolumn{2}{c}{**Cycland**} & \multicolumn{2}{c}{**Self-**} \\  & \multicolumn{2}{c}{**Cropland to**} & \multicolumn{2}{c}{**Restoration**} & \multicolumn{2}{c}{**Maintenance**} & \multicolumn{2}{c}{**Reclamation**} & \multicolumn{2}{c}{**Urbanization**} & \multicolumn{2}{c}{**of Forest–**} \\  & \multicolumn{2}{c}{**Grassland**} & \multicolumn{2}{c}{**Grassland**} & \multicolumn{2}{c}{**Vegetation**} & & & & **Grassland** \\ \hline \multirow{6}{*}{LAI (10\({}^{4}\) km\({}^{2}\))} & NE & 0.79 & 0.67 & 6.16 & 1.00 & 0.37 & 3.96 \\  & NC & 1.38 & 2.41 & 5.50 & 1.77 & 0.36 & 11.85 \\  & EC & 1.45 & 0.85 & 4.29 & 1.81 & \(-\)0.28 & 12.93 \\  & SC & 2.31 & 1.44 & 4.65 & 2.43 & 0.18 & 17.70 \\  & CC & 1.43 & 0.82 & 4.21 & 1.80 & 0.10 & 9.26 \\  & NW & 1.58 & 1.01 & 6.34 & 2.50 & 0.20 & 13.15 \\  & SW & 4.41 & 3.78 & 11.55 & 4.84 & 0.04 & 19.26 \\ \hline \multirow{6}{*}{GPP (TgC)} & NE & 14.26 & 9.61 & 157.99 & 19.81 & 7.53 & 69.99 \\  & NC & 20.13 & 26.47 & 106.20 & 28.10 & 7.22 & 169.96 \\ \cline{1-1}  & EC & 17.10 & 8.42 & 116.88 & 24.93 & 5.26 & 129.20 \\ \cline{1-1}  & SC & 10.77 & 6.89 & 25.28 & 11.51 & 0.75 & 72.57 \\ \cline{1-1}  & CC & 12.27 & 6.18 & 66.52 & 17.43 & 3.23 & 65.49 \\ \cline{1-1}  & NW & 19.52 & 15.64 & 81.33 & 25.17 & 2.94 & 144.70 \\ \cline{1-1}  & SW & 31.80 & 35.60 & 80.28 & 35.56 & \(-\)0.69 & 205.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Total changes in the LAI and GPP in the land use change areas in each region from 2000 to 2018. Among them, the LAI was more strongly correlated with the GPP, with a positive correlation in 86% of the areas (\(p<0.05\)), which mainly occurred in SW (157.51 \(\times\) 10\({}^{4}\) km\({}^{2}\)), NW (132.56 \(\times\) 10\({}^{4}\) km\({}^{2}\)), and NC (116.32 \(\times\) 10\({}^{4}\) km\({}^{2}\)). In most of the regions, the correlation coefficient between the GPP and precipitation (PRCP) was negative (58%), and the areas with positive correlations were mainly concentrated in SW (74.36 \(\times\) 10\({}^{4}\) km\({}^{2}\)) and NC (73.14 \(\times\) 10\({}^{4}\) km\({}^{2}\)). The GPP was positively correlated with temperature (TEMP) in 50% of the areas, mainly in SW (106.19 \(\times\) 10\({}^{4}\) km\({}^{2}\)). The degree of correlation between the GPP and solar radiation (SR) had a spatial distribution similar to that of the correlation between the GPP and TEMP. The regions with positive correlations were mainly concentrated in SC (54%). The statistics of the average correlation coefficients and standard deviations of the GPP with LAI, TEMP, PRCP, and SR correlations in the different regions are presented in Figure 11. Compared with the other correlations, GPP-LAI exhibited a stronger correlation in all of the regions, especially NW, NC, and NE. The average correlation coefficient of GPP-TEMP was negative in NC, CC, and NW, indicating that the increase in TEMP inhibited the carbon sequestration capacity of the three regions. It should be noted that the average correlation coefficient of GPP-PRCP was only positive in NC. The average correlation coefficients of GPP-SR in the different regions ranked as follows: SC > CC > EC > SW > NW > NC > NE. Moreover, the regions with positive correlations were all located in southern China. Figure 10: Partial correlations and spatial distributions of significance (\(p<0.05\)) between the GPP and the (**a**) LAI, (**b**) temperature, (**c**) precipitation, and (**d**) solar radiation.