## 3 Results and Discussion
### Relationship between NDVI and Climatic Factors
#### 3.2.2 Spatial Pattern of NDVI-Climate Relationships  
The positive correlation between the overall greening trend and warming trend may vary with vegetation region. The correlation coefficients between NDVI and climate factors at each pixel are shown in Figure 5. In about 23.9% and 12.0% of the vegetated area, annual mean NDVI was correlated significantly with annual mean air temperature and precipitation, respectively, suggested that in larger areas vegetation growth was controlled by temperature in China. This is consistent with previous findings [46,66].  
Figure 5: Map of correlation coefficients between annual mean NDVI with (**a**) annual mean air temperature and (**b**) annual total precipitation. White areas represent pixels with no data, light gray pixels represent insignificant trends, and dark gray pixels are masked with threshold of 30-year mean NDVI \(<0.1\).  
Areas where NDVI was positively correlated with air temperature were dominant (92.76% of the significantly correlated area; Figure 5a). Only about 7.24% of the significant temperature-correlated area was negative, mainly in northeastern China (_i.e._, northern Xinjiang). The significant negative correlation in the Pearl and Yangtze River delta areas is probably spurious, caused by a heat island effect and vegetation degradation, which may be the result of continued rapid urbanization. Examinations of interactions between land-use change and NDVI dynamics are beyond the scope of this study, but represent an important topic of future research.  
In about 12% of the vegetated area, annual NDVI was correlated significantly with annual precipitation, with 64.3% positive and 35.7% negative. In southwestern China, the northern portion of northeastern China and western Xinjiang, the annual NDVI was significantly and negatively related to annual precipitation. In the southern Qinghai-Tibet Plateau, Hebei and Shandong provinces, and north Xinjiang, the annual NDVI was significantly and positively correlated with annual precipitation (Figure 5b). The negative correlation between NDVI and precipitation in the northern part of northeastern China was attributed to the large number of cloudy days caused by precipitation during the cold and snowy spring weather there [37,66]. The negative correlation on the Tibetan Plateau could be attributed to the increasing snow and ice cover caused by increasing precipitation [37]. The negative correlation of annual NDVI with precipitation in southwestern China may be tied to precipitation-related decreases of temperature [37], increases of cloud cover, and a reduction of incoming solar radiation.  
The violin plots [68] of correlation coefficients for each vegetation region are shown in Figure 6. The impacts of vegetation regions on the correlations between NDVI and temperature and precipitation were significant (_p_ < 0.001). Post hoc comparison shows that EBF had the strongest positive  
Figure 5: _Cont._  
correlation with temperature, followed by DBF \(>\) RF \(>\) TG \(>\) TD \(>\) PAV. The annual NDVI for MF and DCF showed significant and negative correlation with annual temperature. TG, DBF, PAV, TD and EBF had the strongest positive correlations with annual precipitation. A negative correlation with precipitation was found only for RF.