## 3 Results
### Response of Phenology to Climate Change
#### 3.4.2 Response Analysis of Vegetation Phenology to Climate Factors at Different Scales  
In order to more accurately analyze the response degree of vegetation phenology to Temp, Prcp, and RH, we correlated vegetation phenology with pre-season climate factors (Figure 10). The results showed that the correlation between the phenological parameters estimated by SIF and NDVI data and the pre-season climate was different. The SOS\({}_{\rm SIF}\) was mainly affected by the RH in March and April, and higher RH would lead to the advanced  
Figure 9: Correlation between phenology and climate factors of different vegetation types in Northeastern China from 2001 to 2020. (**a**) Correlation between SOS and climate factors based on SIF and NDVI; (**b**) correlation between EOS and climate factors based on SIF and NDVI; (**c**) correlation between LOS and climate factors based on SIF and NDVI; (**d**) correlation between POP and climate factors based on SIF and NDVI.  
SOS. The correlation between the SOS\({}_{\text{NDVI}}\) and the pre-season climate was consistent, among which it was positively correlated with the Temp, and negatively correlated with Prcp and RH. The correlation between the EOS\({}_{\text{SIF}}\) and Temp was the highest. The EOS\({}_{\text{NDVI}}\) had a higher correlation with Prcp and RH, both of which were positively correlated. The LOS\({}_{\text{SIF}}\) was mainly influenced by Prcp and RH. The LOS\({}_{\text{NDVI}}\) was negatively correlated with Temp, but positively correlated with Prcp and RH. The POP\({}_{\text{SIF}}\) and POP\({}_{\text{NDVI}}\) had the same response to climate factors, mainly affected by Temp and RH, and the response mechanism was the opposite.  
Figure 10: Correlation between vegetation phenological indicators and pre-seasonal climatic factors in Northeastern China. (**a**) Correlation between SOS\({}_{\text{SIF}}\) and pre-season; (**b**) correlation between SOS\({}_{\text{NDVI}}\) and pre-season; (**c**) correlation between EOS\({}_{\text{SIF}}\) and pre-season; (**d**) correlation between EOS\({}_{\text{NDVI}}\) and pre-season; (**e**) correlation between LOS\({}_{\text{SIF}}\) and pre-season; (**f**) correlation between LOS\({}_{\text{NDVI}}\) and pre-season; (**g**) correlation between POP\({}_{\text{SIF}}\) and pre-season; (**h**) correlation between POP\({}_{\text{NDVI}}\) and pre-season.  
We investigated the spatial distribution of monthly climatic impacts on phenology at the pixel scale (Figure 11). We found that the vegetation SOSIF in the north and east was primarily influenced by February Temp and April Prcp, but the overall SIFNDVI was most closely associated with March and April Temp, as well as Prcp in February, March, and April. The central plains and Hulunbuir grassland EOSIF was most strongly associated with July Temp and August Prcp, but the vegetation EOSINDVI was primarily affected by August Temp, Prcp of May and June, and RH of June and August. The LOSIF was most strongly related to July Temp, May Prcp, and RH in the central plains and Hulunbuir grassland, while the LOSNDVI was most strongly related to June and August Temp, except in northern Daxinganling, the Sanjiang plain, and the southern Changbai Mountain, where it was primarily affected by May Prcp and RH. The vegetation POPIF was influenced mostly by May Temp, Prcp of March and June, and RH of March and April. The POPINDVI was influenced primarily by March and May temperature, March and June Prcp, and April and June RH.  
Figure 11: _Cont._