## 3 Results
### Phenology Metrics Estimated by Different VIs  
The performance of estimations extracted by VIs varies across different ecosystems. The estimated SOS and EOS were comparable but not equivalent (Figure 4). Considerable variations were found in the phenology transitions dates. In terms of GPP (as the reference), we found a difference of 74 days between the average phenological transition dates of the four landscapes for SOS, and a difference of 41 days for EOS. Generally, EVI-extracted phenological transition dates seemed to lengthen the season, by contrast, MCD12 and PI seemed to indicate a relatively short seasonal duration. PPI was more robust in phenology extraction especially at the beginning of the growing season. In WET sites in Greenland and northern Europe, the dormancy onset of photosynthesis occurred generally around day 256 according to the EC tower estimates. All of the MODIS estimations seemed to postpone the autumn abscission. Furthermore, both NDVI and EVI indicated longer growing seasons, around day 287 and day 282 respectively, while PPI indicated a similar length of the season. In GRA sites, PI showed a similar SOS but failed to track EOS by postponing the autumn abscission by 11 days, while NDVI and MCD12 shortened the growing season by an average of 10 days and 43 days, respectively. In the OSH site, all the MODIS-based indices tracked the tower-based onset reasonably well, and the variations between SOS and EOS extracted by the different indices were relatively small. Nevertheless, photosynthesis onset based on MODIS PPI were relatively closer to the tower data. Regarding ENF sites, the onset of photosynthesis according to the tower-based data occurred between about days of 70 and 119. The multi-model mean values generally showed a similar, later onset of photosynthesis, except in the case of EVI with the lowest value of day 41. Figure 4: Box plots of SOS and EOS estimated from tower-based GPP and four remote sensing-derived VIs. The central mark represents the mean, the edges of the box are the 25 th and 75 th percentiles, and the whiskers extend to the most extreme data points not considered outliers. SOS and EOS indicate the start and end of the season, respectively. SOS and EOS dates based on all the indices correlated significantly (\(p<0.05\)) with GPP estimations, indicating that remote sensing and in situ phenology dates responded to the same climate variables, but responded differently. Estimates of SOS and EOS in sites with different vegetation types derived using the same fitting function varied according to the different functional plant types, but most had relatively low R\({}^{2}\) values (Table A3). There were substantial differences in the performances of the five indices for predicting SOS across the array of functional plant types included in the study. None of the five indices provided reliable SOS estimates in OSH sites (Figure 5). The SOS in GRA sites was better modeled, with all approaches yielding SOS estimates that were significantly correlated with tower-based observations (R\({}^{2}\) = 0.03-0.41). The correlation between all four indices and GPP in ENF sites was quite low (R\({}^{2}\) = 0.07-0.22), but NDVI performed better in WET sites (R\({}^{2}\) = 0.43, RMSE = 10.4). For EOS, the relationships between GPP-based and remotely sensed estimates were also significant (\(p<0.05\)), but less significant than for SOS, suggesting that the autumn VI values could not provide an accurate indicator of the timing of leaf defoliation. EVI and PPI provided the lowest R\({}^{2}\) (R\({}^{2}\) = 0.01, \(p<0.005\)), while NDVI showed the highest RMSE compared with GPP-based EOS. Compared with SOS, all models predicting observed EOS performed comparably for a given functional type (Figure 5). NDVI performed better in GRA sites, though R\({}^{2}\) was low (0.19), while all methods predicting EOS in WET sites were significantly correlated with observations, with modest differences in the strength of the correlation (R\({}^{2}\) = 0.02-0.36).