## 3 Results
### Response of Vegetation to Drought in Xinjiang  
#### 3.3.1 Spatial Distribution of Vegetation Condition Response to Climate Change in Xinjiang  
The maximum correlation coefficients (R\({}_{\rm max}\)) quantify the sensitivity of vegetation indices to climate variability across various SPEI timescales, with the corresponding timescale reflecting the drought tolerance of vegetation. Details on the calculation of R\({}_{\rm max}\) are provided in Section 2.3.5. A higher correlation between vegetation growth conditions and a shorter timescale SPEI indicates a faster drought response, whereas a higher correlation with a longer timescale SPEI suggests a slower response, implying greater drought resistance. As shown in Figure 10, the NDVI and SPEI exhibited a negative correlation across much of Xinjiang, covering approximately 61% of the study area. This negative correlation was strongest in southern Xinjiang (south of the Tianshan Mountains), particularly in the R4 and R5 ecological regions, where R\({}_{\rm max}\) values ranged between \(-0.327\) and \(-0.2\). Conversely, a strong positive correlation was observed in northern and eastern Xinjiang, with over 90% of the R1 and R2 ecological regions showing a positive correlation and R\({}_{\rm max}\) values of approximately 0.5, indicating a more responsive vegetation response to climate or environmental conditions in these areas. The SIF correlation map shows that most areas exhibit a positive correlation, accounting for approximately 86% of the study region. Positive correlations were particularly prevalent in R1 and R3, where more than 90% of the area showed a positive correlation, with R\({}_{\rm max}\) values ranging from 0.3 to 0.7. This indicated a positive response of vegetation photosynthetic activity to climate variability in these regions. A notable spatial difference was observed between the NDVI and SIF distributions, especially in the southern areas, where NDVI showed a stronger negative correlation with SPEI, whereas SIF displayed a weaker negative or even localized positive  
Figure 9: Trends in NDVI and SIF for grassland and forested areas (2000–2020). For forest areas, NDVI shows a statistically significant increasing trend (y = 0.0017x + 0.5584, Corr. = 0.569), whereas SIF displays a slight decreasing trend (y = \(-0.0004\)x + 0.1943, Corr. = \(-0.2389\)). For grassland areas, NDVI exhibits a positive trend (y = 0.0017x + 0.194, Corr. = 0.8019), with a minimal increase in SIF (y = 0.0001x + 0.0779, Corr. = 0.0712). The shaded regions indicate the confidence intervals for each regression line. correlation. This pattern suggests that the vegetation in these regions, despite showing poorer structural indicators (NDVI), maintains a relatively high photosynthetic efficiency (SIF), further indicating the drought resilience of vegetation in arid areas. The correlation between the vegetation indices and SPEI varied across different ecological regions and vegetation types (Figure 11). The R1 ecological region showed the highest overall NDVI-SPEI correlation, with positive correlations in >99% of the area. In contrast, the NDVI-SPEI correlation in the R2 region was generally weak. The remaining three ecological regions (R3, R4, and R5) showed similar NDVI responses to the SPEI, with negative correlations covering 80%, 77%, and 61% of their areas, respectively. The R5 region exhibited the greatest variability in correlation coefficients, ranging from a maximum of 0.5 to a minimum of \(-\)0.3. For SIF-SPEI correlations, most regions demonstrated a positive relationship, with overall correlations slightly higher than those observed for NDVI-SPEI, although not particularly strong. The R2 and R4 regions displayed the largest variation in SIF-SPEI correlations, with maximum values of 0.7 and 0.6, respectively. These results further suggest that vegetation in northern Xinjiang responds more sensitively to SPEI than that in southern Xinjiang, with physiological responses (SIF) being more sensitive than structural responses (NDVI).