## 3 Results
### Correlation Analysis of NPP Influencing Factors  
#### 3.2.1 Correlation Analysis of Meteorological Factors  
Meteorological elements, as crucial environmental factors, influence various aspects of the ecosystem. Suitable TEM is essential for vegetation to absorb CO\({}_{2}\). Under extreme TEM conditions, the growth of plants is significantly hindered, and their ability to photosynthesize and perform vital biological processes is greatly diminished. PRE also directly affects vegetation growth and carbon sequestration. Sufficient SUN is necessary for photosynthesis. EVA indirectly affects vegetation carbon sequestration by impacting atmospheric water vapor pressure, thus making plants regulate their stomatal size to avoid water vapor pressure deficit, which may limit NPP. Adequate PRE and SUN, along with appropriate TEM and EVA, will exert a positive effect on NPP. To investigate the impact of meteorological factors on NPP, Pearson's correlation coefficients (PCCs) between annual NPP and TEM, PRE, SUN, and EVA were calculated based on MOD17 data and annual raster data of meteorological factors in the Songnen Plain using ArcGIS 10.6 software platform (Figure 9). A positive correlation between NPP and PRE was observed, which was more pronounced in the west and less in the northeast and southeast. PCC \(>\) 0.7 indicates a significant positive correlation, and the zone where the correlation was significantly positive accounted for 26.97% of the study area. The absolute values of Pearson's correlation coefficients between NPP and TEM were all below 0.5, indicating the correlations were weak. The positive correlation regions accounted for 11.68% and were primarily located in the northeast and southwest of the study area. NPP was negatively correlated with SUN, and the regions where the correlation was significant (PCCs \(<\) \(-\)0.7) covered 6.36% of the Songnen Plain and were mainly concentrated in the central and eastern parts. NPP was negatively correlated with EVA, and the regions with significant correlation (PCCs \(<\) \(-\)0.7) covered 16.51% of the Songnen Plain and were mainly concentrated in the southern central part.  
Figure 8: The monthly NPP increments for different land cover types.  
Figure 9: The distribution of the correlation coefficients (PCCs) between NPP and meteorological factors precipitation (PRE), temperature (TEM), sunshine hours (SUN), and evaporation (EVA).  
To investigate the correlation between NPP and meteorological factors in different land cover types, the PCCs were calculated separately for each land type, relating the two variables (Figure 10). The PCCs of NPP and PRE in FOR, GRA, PAD, and DRY were 0.21-0.91, indicating a strong positive correlation. The most significant positive correlation was found in the case of GRA, with the mean of PCCs being 0.65. Average absolute values of PCCs for NPP and TEM corresponding to all land cover types are below 0.2, indicating weak correlation. The PCCs between NPP and SUN for all land cover types show a negative correlation in over 75% of the regions. NPP was significantly and negatively correlated with EVA in all cases of land cover types. The average PCCs for NPP and EVA were less than \(-0.52\), except in the case of SWA.