## 4 Discussion
### Consistency of Characteristics and Trends in Vegetation Phenology  
By analysing the characteristics of phenological parameters under different extraction methods, we found that there were great differences in the SOS and EOS. Specifically, the average of the SOS extracted under Methods 1, 3, and 5 was obviously earlier than Methods 2 and 4, and the average of the EOS under Methods 1 and 3 was significantly later than Methods 2 and 4 in different regions and vegetation types (Figure 3). This is mainly because the mechanism of different extraction methods is different. Compared with the other methods, Methods 2 and 4 have higher requirements for the NDVI threshold and do not identify some vegetation with an earlier growing season and smaller NDVI, so the SOS is obviously later than that of other methods, and the EOS is obviously earlier than that of the other methods.  
The trend of vegetation phenological parameters is a crucial indicator affecting vegetation phenology. In our study, we found that the spatial distribution of the SOS and the five methods were consistent, showing an advancing trend, which is the same as the results obtained by [PERSON] et al. for vegetation phenology in the Northern Hemisphere [15]. However, the SOS trends under the five selected methods differed in some regions, especially in Asia and Europe between 45\({}^{\circ}\)N and 60\({}^{\circ}\)N. This may be due to the different responses of different vegetation types to hydrothermal conditions. Because in Asia between 45\({}^{\circ}\)N and 55\({}^{\circ}\)N, the main vegetation is G, mainly distributed in the arid areas [45], the water stress will significantly affect the dynamic changes in NDVI in this region [46], and lead to significant differences in trend of the SOS under different extraction methods. Temperature has been widely recognised as the dominant climatic variable driving changes in vegetation phenology for temperate and boreal ecosystems across the Northern Hemisphere [31; 47; 48]. Furthermore, for the Asian and European regions between 55\({}^{\circ}\)N and 60\({}^{\circ}\)N, the main vegetation is MF, which is a mixture of various tree species. Due to the different responses of various trees to temperature [49], increases in temperature will lead to large fluctuations in the composition of vegetation in this region and aggravate the inconsistency of vegetation phenology under different extraction methods [8; 31; 50].  
For the trend of the EOS, most areas from 30\({}^{\circ}\)N to 60\({}^{\circ}\)N show a lagging trend under the five selected methods, which is the same as the results obtained by [PERSON] et al. [15]. However, the EOS trend under Method 5 above 60\({}^{\circ}\)N is significantly different from that under the other extraction methods. This may be because Method 5 has poor NDVI recognition for the OS in this region, which affects the trend. Therefore, when studying the change in the EOS in this region, the deficiency of this method should be considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Different Regions** & **Different Methods** & **R** & \(\boldsymbol{p}\) & **RMSE** \\ \hline  & Method 1 & 0.47 & 0.042 * & 8.32 \\  & Method 2 & 0.44 & 0.037 * & 9.04 \\
60\({}^{\circ}\)-75\({}^{\circ}\)N & Method 3 & 0.69 & 0.046 * & 7.22 \\  & Method 4 & 0.76 & 0.018 * & 6.37 \\  & Method 5 & 0.36 & 0.077 & 10.55 \\ \hline \end{tabular} R represents the correlation coefficient of the EOS between remote sensing data and ground data. \(\boldsymbol{p}\) represents the significant correlation of the two, and * indicates the significant level is 0.05 (\(\boldsymbol{p}<0.05\)). RMSE represents the root mean square error, the higher the value, the worse the fitting degree between remote sensing data and ground data; on the contrary, the lower the value, the better the fitting degree between them.  
\end{table}
Table 5: _Cont._