## 4 Discussion
### Uncertainties  
Selecting the most suitable data smoothing and phenology extraction methods is not straightforward due to the variety of recommendations expressed in literature regarding the best performing techniques [37; 38; 50]. The reliability of the methods can be difficult to assess and [PERSON] and [PERSON] [51] emphasize that smoothing processes do not always result in an accurate depiction of seasonal variations in photosynthetic activity, particularly when the data are very noisy. For instance, the Savitzky-Golay filter did not produce reliable results for the NDVI\({}_{\text{prod}}\) time series, which is consistent with previous studies that have demonstrated the sensitivity of this method to noise [36; 50]. Applying four different methods provided some insight into the dependability of the phenology estimates. A comparative study by [PERSON] et al. [5] tested 10 different methods for estimating spring phenology and found that different techniques produced significantly different results. Likewise, in this study, the choice of filtering or fitting technique did indeed influence the estimates and none of the methods applied were consistently superior at this site. However, the less noisy time series (NEE, NDVI\({}_{\text{maiac}}\) and sPRI\({}_{1}\)) were less impacted by the choice of smoothing method (Figures 3 and 4) and the long study period of 13 years provided enough data points to observe the relationships between SGS predicted by the MAIAC time series and the NEE data with a fair degree of certainty as indicated by the correlation coefficients (Table 3).  
The ability of NDVI\({}_{\text{maiac}}\) and sPRI\({}_{1}\) to estimate timings of spring seasonal transitions at the study site is very promising, however, the results should be interpreted with caution. It is important to note that the seasonal signal identified from the VI time series could occur as a results of other factors than photosynthetic activity [5]. At boreal forest sites, the seasonal cycle in vegetation activity is highly consistent, driven by temperature and to some extent by available PAR. It can therefore be observed in several co-correlating variables, including snow cover [61] and any variations in the timings of the growing seasons identified from satellite-derived VIs could indicate temperature changes that result in green-up and senescence, but do not necessarily directly track increases or decreases in photosynthetic activity. The impact of sun elevation was tested by examining the correlation between the annual estimates of phenology based on NEE and PAR. The fact that no significant relationships were found between these demonstrates that the NEE-VI estimates are not an artefact of inter-annual variations in PAR. Other measures should be taken to distinguish the effects in snow cover on the VI signal. For instance, [PERSON] et al. [70] developed a snow detection algorithm specifically for forest ecosystems, which could potentially be applied to the data.  
To gain a better understanding of the factors that complicate comparison of the satellite data and eddy covariance flux data, in situ radiation measurements have been incorporated in previous studies [9; 44]. Obtaining ground-measured PRI and NDVI would help bridge the gap between the two different types of observations and thus provide a more direct assessment of the accuracy of the MODIS-estimated timings of seasonal transitions.