## 4 Discussion
### Performances of MAIAC VIs and NDVI\({}_{\text{prod}}\)  
Overall, relatively high correlations were found between the MAIAC VI time series and the ground estimated SGS (\(R^{2}\) = 0.36-0.80). Few studies have found that MODIS VIs provide good estimates of phenology at a single site in evergreen conifer forests. [PERSON] et al. [10] found significant correlations between NDVI derived from daily MODIS reflectances and CO\({}_{2}\) fluxes, however, [PERSON] et al. [9] and [PERSON] et al. [62] concluded that the inter-annual variations in the timings of the seasonal transitions in evergreen forest ecosystems were too low relative to the error in the MODIS NDVI time series to accurately predict phenological parameters. The noise introduced by varying viewing and illumination conditions, atmospheric conditions and snow cover becomes too significant to detect the phenological variations. A significant challenge lies in the tendency for photosynthetic activation of boreal coniferous forests to occur in the spring while there is still snow on the ground, affecting the VI observations and thus their utility as predictors of phenology. Furthermore, frequent cloud cover in boreal regions can cause large temporal gaps in the data. Some of these issues may be cause of the weak correlations found between the SGS estimated from NDVI\({}_{\text{prod}}\) and the ground data. The processing of the MOD13Q1 product using the CV-MVC algorithm attempts to correct for some of the effects mentioned, however, the data still exhibited a high degree of noise in comparison with the MAIAC data. The product data are also limited by their coarse temporal resolution. Since each sample is taken at the maximum value within a 16-day interval, the time gap between two samples can be up to 31 days, which is significant in this context as spring reactivation of photosynthetic activity occurs rapidly in evergreen conifers [14]. The significant relationships observed between SGS derived from the MAIAC data and in situ are thus remarkable and they indicate that the MAIAC aerosol-surface algorithm works effectively. This is also evident from the fitted curves in Figure 2, which exhibit steady seasonal curves and low degree of noise. The algorithm takes into account many of the factors that introduce error in the VI data by deriving average bidirectional reflectance factors [41], retrieving aerosol information [40] and by removing cloud-contaminated pixels through time series processing [63]. Although the application of the MAIAC algorithm to the MODIS data had effectively removed 88% of the observations in the study period (i.e., observations were recorded as missing data), the MAIAC dataset had a markedly better temporal resolution during the growing season compared to the 16-day composite NDVI product. Consequently, interpolation between data points in the NDVI\({}_{\text{maiac}}\) and sPRI\({}_{\text{1}}\) time series was less susceptible to biases. Despite the significant relationships observed, the SGS dates were generally predicted to be earlier in the year for all three MODIS time series compared to SGS estimated from the ground data. This was also found in previous MODIS-based phenology studies by [PERSON] et al. [57], [PERSON] et al. [11] and [PERSON] et al. [9]. The reduced number of useful observations during the winter months tends to extend into the beginning of the green-up phase. [PERSON] et al. [11] found that gaps at the transitional stage in an NDVI time series smoothed with a sigmoidal function, shifted green-up estimates towards earlier in the year. The MAIAC dataset had gaps in the data at the beginning of the transition phase in all years (between the winter baseline and the first non-negative MODIS observations), which may be the cause of the early predictions. Furthermore, defining the phenological metrics is somewhat subjective and attempts to describe a gradual temporal transition as a single date.