## 3 Results and Analysis
### Phenological Consistency between Remote Sensing and Ground Data  
To study the similarities and differences between remote sensing data and ground data in vegetation phenology above 30\({}^{\circ}\)N, we employed the same five methods used above, extracted the phenological data from weekly mean GPP data from 25 qualified flux towers and applied the average value as ground phenological parameters. Based on the SOS analysis from the image data and ground data (Table 4), we found that the SOS from the image data and ground data under different methods above 30\({}^{\circ}\)N were significantly correlated (_p_ < 0.05); however, according to the correlation coefficients (R) and the root mean square error (RMSE), we found that Method 3 had a higher R (0.94, \(p\) < 0.05) and the lowest RMSE (4.35) than the other methods, and was most suitable for the extraction of the SOS in this area. However, for different latitude regions, the phenological consistency between remote sensing data and ground data is greatly different under different extraction methods. From the R and RMSE, we found that Methods 2 and 4 are more suitable for the extraction of the SOS from 30\({}^{\circ}\)N to 45\({}^{\circ}\)N, and Methods 3 and 5 are more suitable for the extraction of the SOS from 45\({}^{\circ}\)N to 75\({}^{\circ}\)N.  
Figure 7: Phenological trends of different vegetation types. (**A**,**C**) respectively represent the trend of SOS and EOS of different extraction methods under different vegetation types above 30\({}^{\circ}\)N. Among them, different colours represent different extraction methods, and blue represents the average of five extraction methods. (**B**,**D**), respectively represent the trend of different extraction methods and the average difference of five extraction methods, and C, G, S, WS, OS, MF, DBF, and ENF on the abscissa, respectively represent different vegetation types. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phenological Parameters** & **ENF** & **DBF** & **MF** & **OS** & **WS** & **S** & **G** & **C** \\ \hline SOS & 0.03 * & 0.09 & 0.00 * & 0.28 & 0.43 & 0.25 & 0.37 & 0.15 \\ EOS & 0.27 & 0.41 & 0.16 & 0.53 & 0.82 & 0.74 & 0.68 & 0.36 \\ \hline \hline \end{tabular}  
* The values represent the trend difference of phenological parameters under the five extraction methods.
* represents that the significant level of the difference is 0.05 (_p_ < 0.05). ENF, DBF, MF, OS, WS, S, G, and C are different vegetation types.  
\end{table} 
Table 3: Differences of phenological parameters under different extraction methods. By analysing the consistency of the EOS between image data and ground data above 30\({}^{\circ}\)N (Table 5), we found that Method 4 has the highest significant correlation coefficient (0.83, \(p\)! 0.05) and the lowest RMSE (7.49) compared to the other methods, and is most suitable for the extraction of the EOS in this area. For 30\({}^{\circ}\)N to 45\({}^{\circ}\)N and 60\({}^{\circ}\)N to 75\({}^{\circ}\)N, compared with other methods, Methods 3 and 4 have higher significant correlation coefficient and lower RMSE, making them more suitable for the extraction of the EOS in these regions. Method 4 has the highest correlation coefficient (0.73) and the lowest RMSE (5.36) compared to the other methods from 45\({}^{\circ}\)N to 60\({}^{\circ}\)N, thus it is most suitable for the extraction of EOS in this region.