## 3 Results
### Analysis of the Impact of Spatial Differentiation on Vegetation GPP
#### 3.4.1 Impact of Individual Factors on Vegetation GPP
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **ET** & **LAI** & **Precipitation** & **Radiation** & **Temperature** & **VPD** \\  & **(X1)** & **(X2)** & **(X3)** & **(X4)** & **(X5)** & **(X6)** \\ \hline
2001 & 0.2195 & 0.8954 & 0.4776 & 0.3764 & 0.1858 & 0.0414 \\  & [4] & [1] & [2] & [3] & [5] & [6] \\  & *** & *** & *** & *** & *** & *** \\
2006 & 0.3876 & 0.7473 & 0.4847 & 0.369 & 0.218 & 0.0302 \\  & [3] & [1] & [2] & [4] & [5] & [6] \\
2011 & 0.2593 & 0.9012 & 0.4278 & 0.3201 & 0.1345 & 0.0527 \\  & [4] & [1] & [2] & [3] & [5] & [6] \\  & *** & *** & *** & *** & *** & *** \\
2016 & 0.9757 & 0.9103 & 0.3711 & 0.3661 & 0.1368 & 0.0667 \\  & [1] & [2] & [3] & [4] & [5] & [6] \\  & *** & *** & *** & *** & *** & *** \\
2022 & 0.4402 & 0.9072 & 0.4517 & 0.4544 & 0.2287 & 0.0634 \\  & [4] & [1] & [3] & [2] & [5] & [6] \\  & *** & *** & *** & *** & *** & *** \\
2001–2022 & 0.3072 & 0.8976 & 0.5039 & 0.4596 & 0.2468 & 0.0799 \\  & [4] & [1] & [2] & [3] & [5] & [6] \\  & *** & *** & *** & *** & *** & *** \\ \hline \hline \end{tabular} Note: *, *** denote \(p<0.1\), \(p<0.5\) and \(p<0.01\), respectively; the numbers in [] represent the rankings of the corresponding factors’ impacts during the current period.  
\end{table} 
Table 3: Divergence and q-values of factor detection result. modified MODIS-PSN model across the Qinghai-Tibet Plateau (2001-2022). The q-statistic quantifies factor influence, where higher values indicate greater explanatory power for vegetation GPP variation [37; 45]. For the five representative years analyzed (2001, 2006, 2011, 2016, 2022, and 2001-2022), the dominant factors ranked by q-values were as follows: LAI (0.8954), LAI (0.7473), LAI (0.9011), ET (0.9757), ET (0.4401), and LAI (0.8976). LAI consistently served as the primary determinant of vegetation GPP spatial differentiation on the Qinghai-Tibet Plateau, demonstrating the strongest explanatory power in 2001 (q = 0.8954), 2006 (q = 0.7473), and 2022 (q = 0.9072). Notably, ET emerged as the dominant factor in 2016, exhibiting exceptional explanatory power (q = 0.9757) for GPP spatial patterns. From 2001 to 2022, the interannual variation of LAI emerged as the dominant factor among natural factors. Comprehensive analysis revealed that LAI exhibits the strongest explanatory power for vegetation GPP spatial differentiation on the Qinghai-Tibet Plateau, with corresponding influence q-values of 0.8976, 0.8609, 0.9210, and 0.8888. The spatial differentiation of annual vegetation GPP on the Qinghai-Tibet Plateau is not governed by any single dominant factor. This primarily results from regional variations among the various factors influencing GPP's spatiotemporal differentiation characteristics, as well as their dynamic evolution across temporal scales. While temperature and vapor pressure deficit exhibit weaker explanatory power compared to other variables, their influence demonstrates a gradual year-by-year increase. Although their impact on GPP spatial distribution remains generally weaker than that of leaf LAI, these climatic factors show a slowly growing contribution to GPP dynamics.