# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 4 Discussion
### Reasons for the Difficulty in Estimating Phenology from Remote Sensing  
One possible source of error in phenology modelling originates from errors in observations, especially in empirical approaches as adopted in this study, where the model is developed based on observations. Visual phenological observations demand a person to observe and record the phenological phases in the field, and this approach is not free of subjective judgments and biases of an individual observer when visually assessing a canopy. Differences of up to 7 days have been found among different phenological observers. Differences are most visible when an observer is replaced at the same location [60]. The temporal resolution of vegetation indices is very important for phenology modelling, even more important than spatial resolution, especially in pure or nearly pure forests [61; 36; 62]. In the mixed forests of this study, this was also proven by the poor performance of Landsat-derived vegetation indices, which had a low temporal resolution of cloud- and snow-free data, despite their higher spatial resolution. The good performance of MODIS GPP is likely due to their good temporal resolution, despite a very coarse spatial resolution. However, it is worth noting that it is also the combination of both temporal and spatial resolutions, as well as the adequacy of the chosen remote sensing data that determine the performance of remote sensing-based phenology models. In fact, despite thehigher temporal resolution of MODIS LAI data compared to MODIS GPP, the noise in LAI data caused a worse performance than GPP. Events characterizing the EOS (leaf colouring and leaf fall) have been studied less than spring events of SOS (or leaf unfolding), although various authors have reported that the timing of leaf senescence shows less year-to-year variability and is concomitant with less favourable conditions for photosynthesis [63]. However, some factors influence leaf colouring, which could lead to difficulties in predicting the timing of the onset of leaf colouring for some particular years and species. In fact, [PERSON] et al. [64] reported a sensitivity of the general leaf colouring of pedunculated oak to summer and autumn temperature, with higher temperatures (June-October) inducing later leaf colouring, while the leaf colouring of beech was more sensitive to spring temperature (March-May). Autumn plant events are generally more difficult to define and are influenced by sudden individual weather events such as high winds or a single frost [65]. This makes the modelling of EOS particularly more difficult than SOS. For instance, errors of modelled EOS are usually \(\geq\)10 days, whereas usual errors in modelling SOS are around 7 days in physiological phenology models [63; 66], which relates very well to the performance of the remote-sensing-based model of our study, with larger errors for EOS. The phenological phase 'leaf colouring' (or EOS) is more problematic to recognise than the first few green leaves in spring (SOS), because the phase definition refers to 50% of all leaves coloured, including leaves both on branches and on the ground. The variation among individual trees at comparable or identical sites is much higher than in the case of SOS [10]. In the present study, no significant effect of the fractional land share of the different groups of land use (species of interest, coniferous species, other broadleaved species, other land uses) on the best threshold of SOS was found. For EOS, however, higher values of other land uses seem to have a significant effect on the best thresholds, which fluctuate more from the usual default threshold of the SM method (0.5) with high land shares of other land uses (agriculture, grassland, bare land, buildings, etc.). In fact, agriculture and grassland mixed with a species of interest in the same remote sensing pixel would lead to more complex patterns in GPP, with a threshold not being able to capture the interannual variations of the EOS. This is supported by the findings of [PERSON] et al.