# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 4 Discussion  
### Mixed Tree Species Forests Peculiarities  
Modelling phenology from remote sensing requires reliable, dense, and frequent ground observation, which is a challenge in the case of mixed forests. In fact, observations are made on individual tree species, often selected not considering site characteristics such as the aspect and slope of the terrain, or the microclimate conditions of trees in near forests. Remote sensing pixels, however, blend together several trees and site conditions, making it difficult to relate field observations to remote sensing data [52]. A mixture of coniferous and broadleaved species is a well-studied case of the influence of mixture on phenology metrics. In fact, at the end of season in autumn, pixels with significant cover of coniferous tree species would probably lead to a slow decrease in vegetation indices as compared to pixels with pure deciduous broadleaved species, which makes the estimation of the date of EOS more uncertain, as it was the case in our study. On the other hand, a mixture of different deciduous species in the same pixel leads to more difficulties in estimating the date of SOS for individual species, due to the different timing of bud-burst for the different species [53]. The use of remote sensing to study phenology at species level requires a continuous and large mono-species area [54; 55], which needs to be even larger for coarse-resolution remote sensing data such as the MODIS GPP product. This is usually resolved by finding larger pure stands in similar conditions as the observed tree [49]. This could constitute a substantial source of error in phenology modelling from remote sensing data, since the slight differences (altitude, slope, aspect) in the substitute forest stand could result in substantial discrepancies in the modelled phenology. In fact, the bioclimatic law suggests four-day offsets in phenology events for a difference of 120 m in altitudes [56], which means that the 200 m margin considered in the search of substitute stands could be an important source of error in our study. In addition, the relatively low minimum area of pure forest (1 ha) adopted in our study due to the scarcity of large pure stands of some tree species, meant low fractional cover of the species of interest in the remote sensing pixel, which is an important source of uncertainty in the model. [PERSON] et al. [53] predicted SOS with a mean absolute error of 11 days in mixed forests, 9 days in hardwood dominated forests, and 7 days in a nearly pure sugar maple forest using Landsat TM imagery. This is consistent with our study that showed a low SOS RMSE (7.89 days) for _Fagus sylvatica_, which represents the prevalent deciduous forest across Slovenia. The lower SOS RMSE observed for _Tilu_ sp. (7.71 days) is probably due to the fact that _Tilia_ species is a secondary tree species which rarely forms pure stands in Slovenia, and the few significant stands are mostly located in areas less suitable for other tree species. This, however, does not explain the high error obtained for EOS for the same tree species (16.7 days).