## 5 Discussion  
### VI SOS Detection and Evaluation against GPP-Sos  
PPI-based phenology retrieval for SOS assessment has shown an improved performance over NDVI and EVI with regards to several aspects. First of all, PPI was able to detect a more precise timing of SOS when evaluated against ground observed GPP data (Figure 2A-C). We found a surprisingly low and non-significant relation between EO based EVI/NDVI SOS and GPP based SOS (\(r\)-values of 0.11 and 0.08, respectively; 81 site-years of flux measurements used from 14 sites). In addition, a large difference in the timing of spring phenology between the NDVI-based SOS dates and in situ observations was reported in [(39)] for the period 2000-2011 in Western Central Europe. It is clear that a systematic difference between VIs is observed (Figure 2D), which is likely to be related to the different sensitivity to changes in snow cover and vegetation activity [(36)]. Our results comply with the tests of snow influence on VIs as implemented in [(36; 71)]. The tests, as well as examination of VI relation to Normalized Difference Snow Index (NDSI), revealed a pronounced NDVI and EVI association to the changes in snow cover (NDVI was reported to be more sensitive to partly snow-covered surfaces, whereas EVI is more sensitive to high snow depth) [(71; 36)]. The low \(r\)-values reported here indicate very limited to no sensitivity of EVI and NDVI to variability in ground observed SOS for areas characterized by winter snow. This result severely challenges the utility of NDVI/EVI for phenology assessment and change studies in high-latitude regions as such VI SOS estimates are likely to coincide with the disappearance of snow as also discussed in [(50; 11; 50; 42)] unless careful data screening and preprocessing are done to avoid the impact from snow [(28)]. A possible source of uncertainty in the evaluation of SOS retrievals from the different VIs could be related to the threshold method as used here. Another commonly applied method (the derivative method) is to fit functions to the VI signal and to retrieve the SOS as the point of a specific growth rate (usually the maximum growth rate) of this function [(72; 73)]. [PERSON] et al. [(72)] tested different algorithms using both threshold and derivative methods on NDVI for estimating SOS against SOS derived from global FLUXNET sites and also reported moderate relationships regardless of the methods used. [PERSON] et al. [(50)] concluded that the derivative method is misleading for areas with snow cover on the forest floor as there is then two points in time with an increase in the phenology signal, one coinciding with the snowmelt and one with the leaf burst. In addition, in deciduous forests, there are two different leaf bursts during spring time with a corresponding increase in the phenology signal: the first for the ground vegetation and the second for the tree canopy. This may add interannual variability and thereby cause uncertainty in SOS estimates when using the derivative method. EVI was found to perform moderately better than NDVI when compared against ground observed GPP SOS, similar to the results reported by [74]. PPI-SOS on the other hand showed a highly significant agreement with GPP-SOS, yet the correlation coefficient obtained was modest (\(r\) = 0.5). Possible sources of uncertainty impacting the relationship between EO VI SOS and ground observed SOS related to the footprint of satellite and ground measurements must be considered. The MODIS MCD43A4 500 m spatial resolution product was used (3 \(\times\) 3 pixels) for the comparison with flux measurements for a better match with the spatial footprint of the towers whereas the MCD43C4 product of a 0.05\({}^{\circ}\) spatial resolution was used for the large scale analysis. However, using coarse resolution MODIS data when compared to flux tower measurements yielded higher \(r\)-values for PPI and NDVI (PPI = 0.56, EVI = 0.14, NDVI = 0.22).