## 4 Results and Analysis
### Analysis of the Impact of Various Factors on WUE
#### 4.3.3 Vegetation Index Correlation Analysis  
As an indispensable component of terrestrial ecosystems, surface vegetation is highly sensitive to climate change and plays an important role in regulating biospheric and atmospheric changes, and changes in vegetation phenology can be a good response to the dynamic relationship between terrestrial ecosystems and climate change [33; 34; 35]. In this paper, the LAI and NDVI from MODIS data were used to analyze and understand their driving role and influence on WUE. The LAI, as an important structural parameter of vegetation, is also one of the indispensable parameters of plant respiration, transpiration, and evaporation in the terrestrial ecological cycling system [36; 37], and the magnitude of the LAI influences plant transpiration and soil evaporation, which indirectly leads to fluctuating changes in ecosystem WUE.  
Figure 8a,b show the spatial distribution of NDVI and LAI trends, and according to the analysis of the figure, it can be seen that the trends in the NDVI and LAI were more or less the same. Based on the NDVI, 74.13% of the total basin indicated an uptrend, 21.43% of the area showed negative growth, and the area with no change was 4.44%. Compared to the NDVI, the LAI had a higher proportion of area with an increasing trend, which was  
Figure 7: Spatial distribution of partial correlation analysis of WUE with precipitation and air temperature in Li River Basin from 2001 to 2020. (**(a1,b1)** Spatial distribution of correlation coefficients in the Analysis Results. (**a2,b2**) Spatial distribution of various significance Levels).  
80.53%, the area with a decreasing trend was about 19.27% of the total area, and the area with no change was 0.2%. Spatially, the NDVI and LAI showed a decreasing to increasing trend from north to south.  
Using correlation analysis in combination with a significance test, the corresponding spatial distribution of correlation coefficients was obtained, as shown in Figure 9. From Figure 9a, it can be seen that the correlation coefficient intervals of WUE and the NDVI ranged from \(-\)0.870821 to 0.955953; the negative correlation areas were relatively large, accounting for 66.89% of the total area; and the positive correlation areas accounted for about 33.11%. Among them, the areas with positive correlation and negative correlation through a \(p\) < 0.05 significance level test accounted for 2.72% and 7.95% of the total basin, respectively, totaling 10.67%. A further analysis of the correlation coefficients by the \(p\) < 0.05 significance level test showed that the areas with negative correlation were mainly concentrated in Pingle in the southern part of the basin, while the areas with positive correlation were concentrated in the central and western part of the basin, with a few distributed in Xing'an on the eastern side of the basin. Figure 9b shows the spatial distribution of correlation coefficients between WUE and the LAI, with values ranging from \(-\)0.870821 to 0.955953, and the proportions of positively and negatively correlated areas were 39.11% and 60.89%, respectively. Using the significance test for further analysis, the proportion of areas with \(p\) < 0.05 was 14.56% of the total area. Among them, the area with a significant negative correlation was about 8.28%, which was similar to the spatial distribution of the NDVI, and the areas with a significant negative correlation in Pingle accounted for most of the area; the areas with positive correlation at \(p\) < 0.05 accounted for about 6.28% of the total area of the basin, which was an increase in comparison with the NDVI, and they were spatially distributed mainly in the middle of the basin.  
Figure 8: Spatial distribution of trend changes in NDVI and LAI in Li River Basin from 2001 to 2020.