## 5 Conclusions  
In this study, we evaluated the timing of the start of the photosynthetically active season in evergreen needle- and deciduous broad-leaf forest in Finland in the biosphere model JSBACH against satellite observations. The differences between the two forest types with respect to photosynthetic recovery in spring were taken into account. The accuracy of the satellite-derived dates for the deciduous forest was assessed with bud break observations. The NDWI-based method is applicable for deciduous forest in Finland and the estimated dates can be used for the evaluation of model results at a regional scale. The inaccuracy of the satellite-observed date was one week and the bias was small (Figure 2), but regional differences in bias need further evaluation. The comparisons between JSBACH-simulated and satellite-observed start of season at the regional scale showed that the general spatial and temporal dynamics were rather well captured by JSBACH, for both ENF and DBF (Figures 7 and 10). The performance was slightly better for evergreen forest. We found an early bias of three days and a late bias of five days in the start of season of evergreen needle- and deciduous broad-leaf forest, respectively. The bias showed a south-north gradient for both forest types. While in the deciduous forest the start of the vegetation active season is determined by the phenological model LoGro-P, the photosynthesis model is more important for the evergreen forest type. The date of the predicted \"growth phase\" in evergreen forest occurred about one month after the start of the vegetation active season. Therefore, an improved calibration of the phenology model would not be sufficient for this plant functional type. Instead, JSBACH photosynthesis parameters would need to be adjusted to limit too strong early photosynthesis and hence to improve the behaviour of gross primary production in early spring (Figures 4 and 5). In the deciduous broad-leaf case, results could be slightly improved with different JSBACH phenology parameters, but the model formulation seems not optimal with respect to the south-north gradient in Finland. In further work, one could optimize the phenological parameters with a Bayesian method using the remote sensing observations to better understand the effectiveness of individual parameters and the relationships between them. Furthermore, using in situ measurements such as chlorophyll fluorescence observations and greenness change from phenology cameras could be used to improve the photosynthesis and phenology descriptions of the model. **Supplementary Materials:** The Supplementary Materials are available online at www.mdpi.com/2072-4292/8/7/ 580/s1. Table S1: Proportions of land cover classes in Finland determined from CORINE land cover 2000 ([PERSON] et al. 2005). Table S2: Relationship between the mean of start of season from remote sensing and determined from JSBACH modelling for evergreen needle-leaf forest (ENF_SOSmod) and deciduous broad-leaf forest (DBF_SOSmod) for whole Finland and the three boreal sub-zones. The mean start of season was calculated for the period 2003 to 2010. All correlations were significant (\(p<0.0001\)). A positive bias means that the simulated date is late compared to observations. Table S3. Relationship between start of season derived from remote sensing and from JSBACH simulations for evergreen needle-leaf forest (ENF) and deciduous broad-leaf forest (DBF) for different years in Finland. All correlations were significant (\(p<0.0001\)). A positive bias means that the simulated date is late compared to observations. **Acknowledgments:** The work was mainly funded by European Commission through the Life+ projects SnowCarbo (K.B., T.M., A.N.A; Grant agreement LIFE+ 07/ENV/FIN/000133) and MONIMET (K.B., T.M., A. N.A. ; Grant agreement LIFE12 ENV/FI000409). The work was also supported by the Academy of Finland by the project \"CLIMES Impacts of climate change on multiple ecosystem services: Processes and adaptation options at landscape scales\" (K.B.