## 3 Results
### Trends in Vegetation and Response to Drought Trends in Vegetation and Response to Drought  
Precipitation is recognized as a key factor regulating the spatial and temporal dynamics of vegetation. Prolonged precipitation deficits can cause widespread drought, leading to vegetation degradation. Therefore, this study investigated the response of vegetation to the SPI-30 time scale during the May-September period from 2008 to 2023 in the Northeast (Figure S9, Figures 7 and 8). The NDVI in Northeast China showed a significant upward trend over time (k = 0.0015/a, \(p<0.05\)). The interannual increase in NDVI showed a highly stable linear trend, indicating that the vegetation change process is characterized by persistence and regularity. Similarly, [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016) found that the overall trend of the NDVI in Northeast China showed an improving condition. The occurrence of high-NDVI zones reflects the positive effects of ecological restoration measures on vegetation recovery in Northeast China, while the presence of low-NDVI zones may be related to extreme weather conditions during this period ([PERSON] et al., 2017; [PERSON] et al., 2018). As a crucial grain production base in China, the Northeast plays an irreplaceable role in ensuring national food security and maintaining market stability. The crop growing areas in the Northeast are primarily concentrated in the Liaohe Plain, Sanjiang Plain, and Songnen Plain. During the growing period of crops, the vegetation cover in these three plains shows a significant increasing trend. This result indicates a continuous increase in vegetation cover in the region, suggesting that crops are growing well and contributing to national food security. Spatially, there were significant differences in the trends of vegetation cover in Northeast China. The overall rate of change in NDVI ranged from \(-0.00485\) to \(0.00355\) per year. Areas with an increasing NDVI (slope \(>0\)) accounted for \(85.8\%\) of the total study area, while areas with a decreasing NDVI (slope \(<0\)) accounted for \(14.2\%\). NDVI increases are primarily distributed in the central and southwestern parts of the Northeast, as well as the eastern plains. In contrast, NDVI decreases were observed in the Greater Khingan Mountains and Lesser Khingan Mountains in the northern part of the Northeast and higher elevations in the southeastern part. This pattern indicates that the NDVI distribution in the Northeast region exhibits significant spatial and temporal heterogeneity ([PERSON] et al., 2017). This may be attributed to the fact that higher-elevation areas experience lower temperatures, poorer water and heat conditions, and infertile soils, all of which are detrimental to vegetation growth. In contrast, lower elevation plains benefit from better light and water conditions, promoting better vegetation growth and a more pronounced increase in vegetation cover. Figure 7: Correlation between SPI and different periods such as current month, one-month-lag, two-months-lag, and three-months-lag NDVI in Northeast China. Many researchers have used the Pearson correlation coefficient to explore the relationship between drought and vegetation. By calculating the correlation coefficients between drought indices (e.g., SPI, PDSI) and vegetation indices (e.g., NDVI, EVI), the degree of vegetation response to drought can be analyzed [66]. This approach provides an important basis for ecosystem and climate research [67; 68]. From the perspective of the correlation between vegetation and drought, the correlation between the SPI and NDVI during the growing season of crops in the Northeast increased with increasing months, reaching a maximum correlation of 0.29 in September. This indicates that the vegetation response to drought is most significant in September, during the late growing season [69]. In addition, we found that the correlation between the NDVI and SPI, with a one-month lag, is strongest during May-August.