Furthermore, a remote sensing method suitable for extracting the start of season of canopy photosynthesis capacity and GPP in one ecosystem does not necessarily work for all vegetation types [23]. In DBF, the leaf development in spring is a prerequisite for the start of photosynthetic activity and strong correlations are observed between early season phenology (leaf out date, start of LAI development) and the onset of daily net carbon uptake [16; 24]. In contrast to DBF, photosynthesis in evergreen needle-leaf forests (ENF) increases gradually already before the production of new foliage and thus the seasonal recovery of photosynthesis has been difficult to observe in remote sensing observations [25; 26]. Several remote sensing methods have been developed for detecting the start of the growing season in boreal areas [27; 28; 29; 22]. These methods track the greening of the vegetation within the footprint of the satellite pixel and have been evaluated against in situ phenological observations of deciduous trees [27; 28; 30] or phenological indicators determined from micrometeorological CO\({}_{2}\) flux measurements [29]. Because in ENF the onset of photosynthesis occurs before the production of new foliage, remotely sensed start of season, tracking the development of new leaves and shoots at the land surface scale, showed a delay of several weeks when compared to the onset of photosynthesis [16; 23; 29; 31]. Large shifts, of up to three months, between the greening phase from three global remotely sensed data sets of fraction of absorbed photosynthetically active radiation (fAPAR) were obtained for ENF in the boreal region [32]. Instead, a proxy indicator of start of season, determined from satellite time series of Fractional Snow Cover (FSC), was found suitable for this forest type in boreal areas [33]. In this study, we use simulations by the biosphere model JSBACH (Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg) [34] to determine the start of season in DBF and ENF during the past decade in Finland. The start of season is here defined as the beginning of the photosynthetically active period of vegetation. JSBACH is the biosphere component of an Earth system model with a global scope and has, therefore, not been validated for regional applications. Evaluating such a model regionally is challenging, as there are hardly any suitable datasets available at this scale. Even though these large-scale models have been targeted to global scale, it is important to assess their performance at a regional scale to gain understanding about their applicability and characteristics that require further development. The carbon and water cycle of vegetated areas are modelled in JSBACH by considering the physiological response of vegetation to the climatic variables. In this work, JSBACH is run with climatic variables from a regional climate model. Moreover, to assess the sensitivity of the model to the climate drivers, JSBACH is run also with observed meteorological data from micrometeorological measurement sites in Finland. We use remote sensing observations for the evaluation of JSBACH-modelled start of season of boreal forests. Because of structural and functional differences in the photosynthetic recovery in DBF and ENF, specific methods will be employed to extract the start of season date from remote sensing data in the respective forest type. We apply the method developed by [PERSON] et al. [PERSON] _et al._ (2016) and the method based on snow cover [PERSON] _et al._ (2016) for DBF and ENF, respectively. Both methods have been used previously for the mapping of the start of season in boreal areas. For DBF, we also assess here the accuracy of the remote sensing observations against ground observations of bud break of birch trees in Finland. We assess the spatial and temporal correspondence of JSBACH results at the regional scale with these satellite observations. In addition, we evaluate the simulated start of season dates for ENF locally with micrometeorological CO\({}_{2}\) flux measurements.