## 3 Results
### Correlation Comparative Analysis  
The correlation analyses between GPP and soil hydrothermal conditions are conducted using the Pearson method, and the results are presented in Figure 5 and Table 2. The findings reveal variations in the correlation effects between soil moisture and GPP across layers. Large areas with negative correlations are observed in the surface layer, particularly in the high-altitude regions of the Qilian Mountains' main peak area. However, as the soil deepens, the negative effects weaken while the positive effects strengthen. The Pearson correlation coefficients (PCCs) across layers range from 0.0945 to 0.5076, indicating that the deep soil has a more pronounced promoting effect on vegetation carbon sequestration, with the help of the strong water retention characteristics. This effect may be influenced by the water absorption characteristics of vegetation roots [45]. Additionally, the areas showing a negative correlation effect align with the interannual decreasing areas of soil moisture, primarily distributed in high-altitude areas (above 4000 m), which can be attributed to the influence of vertical climate zonality on vegetation growth. The correlation analyses between soil temperature and GPP primarily reveal positive effects, with scattered areas exhibiting inconspicuous negative effects in the central peak area. Unlike soil moisture, the correlation results of soil temperature vary slightly across different layers. The PCCs fluctuate around 0.55, with the highest value of 0.5566 in the surface soil layer, indicating that the soil temperature exhibits a noticeable promoting effect on GPP. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Hydrothermal Conditions** & **Soil Layer** & **Correlation Coefficient** \\ \hline \multirow{4}{*}{Soil moisture} & SM-L1 & 0.0945 * \\  & SM-L2 & 0.3176 * \\  & SM-L3 & 0.5076 * \\  & ST-L1 & 0.5566 * \\ Soil temperature & ST-L2 & 0.5539 * \\  & ST-L3 & 0.5480 * \\ \hline \hline \end{tabular}  
* means the correlation is significant at the 0.05 level.  
\end{table} 
Table 2: Correlation coefficients between GPP and soil hydrothermal conditions validated by the double-tailed \(t\)-test. Figure 4: The background investigation of GPP across three layers, including (**a**) spatial pattern of multi-year average values and (**b**) spatial pattern of interannual variation trend. Three dominant vegetation types are identified based on the ranking of coverage area, including grassland (56.99%), shrub (9.33%), and alpine (7.87%). The spatial distribution is illustrated in Figure 6. Grassland exhibits a widespread and continuous distribution, while shrub and alpine vegetation are predominantly interspersed in the eastern regions and high-altitude areas, respectively. The multi-year average values of GPP for each vegetation type are determined by the ArcGIS zonation statistics tool. The descending order of GPP values is as follows: shrub (417.7 gC \(\times\) m\({}^{-2}\)\(\times\) a\({}^{-1}\)), grassland (284.5 gC \(\times\) m\({}^{-2}\)\(\times\) a\({}^{-1}\)), and alpine (109.8 gC \(\times\) m\({}^{-2}\)\(\times\) a\({}^{-1}\)). Accordingly, the correlation difference analyses between GPP and soil hydrothermal conditions are carried out among dominant vegetation types. Table 3 shows the comparative correlation analyses for the dominant vegetation types. When considering the promoting effect of soil moisture on GPP, all three dominant vegetation types exhibit a stronger correlation with the Level 3 layer, which is consistent with the findings for the entire vegetation zone. Furthermore, for the same layer, the magnitude of PCCs corresponds to the area occupancy, with grassland obtaining the highest PCC, followed by shrub and alpine. This indicates that soil moisture has a pronounced promoting effect in different vegetation regions with larger area occupancy.