## 4 Discussion
### Future Prospects for Capturing Phenology Metrics in Pan-Arctic regions  
The ability to monitor phenology at a landscape scale is important to provide a better understanding of temporal and spatial phenology patterns in pan-Arctic ecosystems and their responses to climate change. Although remote sensing methods can deliver this scale of analysis, uncertainties over their use remain. The current results suggested that remotely sensed phenology and phenology derived from tower-based GPP were related, but with a low overall R value (\(-0.77\) to \(0.82\)). Here, we discussed the possible factors responsible for the highly varied and uncertain estimations. Remote sensing VIs more closely reflect structural canopy development. Systematic differences could be explained by the different responses of VIs to greenness, wetness, and brightness dynamics. The uncertainties were therefore mainly associated with several factors, as described below. The main impact factor was background reflectance contamination due to factors such as snow, soil, soil thawing, and snow melt, which also had distinct seasonal dynamics. The presence of snow decreased NDVI values sharply due to changes in canopy background brightness, while values recovered when the snow melted in April [47]. Phenology metrics extracted from remote sensing VIs at high latitudes are known to be subject to background interference, largely due to snow cover. Although targeted snow flags and weighting schemes have been included in the pre-processing algorithms of some products, it remains difficult to distinguish soil and snow cover interference from vegetation phenology when the greenness signal from the vegetation is very weak. Previous studies acknowledged that snow cover had significant impacts on VIs [22; 57; 58]. Careful data screening and pre-processing are therefore needed to eliminate the impact of snow [59]. Another possible source of uncertainty in the estimations of SOS and EOS from the different indices could be attributed to the extraction method. The rise of NDVI at the start of spring was generally more abrupt than the end of spring, meaning the fitted VIs may slightly overestimated the values before the start of spring while underestimating the values at the start of spring [30]. Other methods such as a threshold method or other fitting functions have also commonly been used. However outputs using data acquired from MODIS tended to give earlier SOS and EOS dates compared with in situ observations in other commonly used approaches [23]. To illustrate the influence of snow, NDSI was computed from MODIS surface reflectance product MCD43A4 C5 (Figure 6). In both sites of FI-Hyy and SE-Deg, opposite variations tendency of NDVI and NDSI occurred suggesting that NDVI might be influenced by snow melt. Large fluctuations werefound in the NDVI before the start and after the end of the growing season. EVI and PI seemed to be hindered by similar but weaker effects of snow melt. Meanwhile, there were large jumps in the EVI in deep winter. Conversely, those effects did not appear on PPI. Other than the background reflectance contamination, there were other contributors to the limitations of LSP estimates in pan-Arctic regions. First, optical remote sensing methods are subject to atmospheric influences including atmospheric noise [60] and persistent cloud cover [61], low viewing angles, and low solar zenith angles. Second, the frequency of data sampling was 8 days over a 16-day period for the MODIS reflectance product, which meant that bud burst occurring within the 8-day periods was difficult to detect. Moreover, site-specific conditions were also a source of uncertainty, such as the pronounced spatial heterogeneity of pixels caused by the sparse vegetation [62]. Sites with relatively short growing seasons with few good-quality observations would make it even harder to extract the seasonal cycles.