## 1 Introduction  
Developing robust methods for monitoring terrestrial photosynthetic activity is key to improving our understanding of vegetation dynamics and the carbon cycle in the context of climate change. Temporal shifts in vegetation phenological events, such as green-up and senescence, are effective indicators of climate change [1] and can have a significant impact on the annual carbon uptake by forest ecosystems [2; 3]. Phenological changes can be assessed from various data sources, including field observations and gas exchange measurements from flux tower sites. At regional to global scales, satellite remote sensing provides the most efficient means to observe spatial and temporal variations in vegetation productivity and phenology. Phenological parameters can be extracted from per-pixel time series of spectral vegetation indices (VIs) that have been closely linked with vegetation productivity [4; 5]. Long-term studies using VI time series have revealed widespread greening occurring in various regions and ecosystems [6; 7; 8]. Ongoing efforts are being made to establish the accuracy with which satellite imagery can be used to predict timings of seasonal transitions by validating the estimates with ground-based observations [7; 9; 10; 11]. Remote sensing time series studies of phenology are commonly based on the Normalized Difference Vegetation Index (NDVI) [12]. The NDVI relies on the contrast between absorption of red light by leaf pigments and strong scattering of near-infrared radiation by foliage. As a consequence, changes in NDVI are linked to changes in various vegetation biophysical properties including leaf area, green biomass and the fraction of absorbed photosynthetically active radiation (fAPAR) [13]. While studies utilizing NDVI and similar broad band vegetation indices have provided valuable insights into the response of vegetation to climate change, such indices are only partially linked to photosynthetic activity. Seasonal and periodic down-regulation of photosynthesis can occur while vegetation continues to absorb radiation with very little change in leaf area and greenness and therefore causes no corresponding changes in NDVI. Thus, the NDVI fails to capture the efficiency with which vegetation converts the absorbed light into biomass and therefore is unable to detect all photosynthetic variation [14]. Vegetation light use efficiency (LUE) is influenced by many environmental and physiological factors, so it varies temporally and across different biomes, species and plant functional types [15; 16]. Taking the LUE component into account may have the potential to improve the accuracy of satellite-based estimates of vegetation productivity and phenology. This is of particular importance in coniferous forests and other evergreen-dominated ecosystems as these exhibit less temporal variability in NDVI compared to deciduous vegetation [14]. A promising approach for assessing variations in LUE involves the use of narrow spectral bands to detect changes in plant pigments. Pioneering studies by [PERSON] et al. [17; 18] linked changes in xanthophyll pigment cycle associated with reduced LUE and plant stress with changes in reflectance at 531 nm, which enabled the development of a Photochemical Reflectance Index (PRI). The PRI combines a narrow band centered at 531 nm and a normalizing reference band unaffected by LUE. Xanthophyll cycle pigments provide a means of dissipating excess energy when light absorbed by plants exceeds the photosynthetic capacity to avoid damage to the photosynthetic system, which influences the PRI signal on a diurnal timescale. It was later demonstrated by [PERSON] et al. [19] that on seasonal scales the main physiological mechanism controlling the relationship between PRI and LUE is not the xanthophyll cycle, but rather dynamics in the relative pigments pools of chlorophyll and carotenoids. This was further confirmed by [PERSON] et al. [20] and [PERSON] and [PERSON] [14], who observed high correlations between PRI responses and adjustments in chlorophyll:carotenoid ratios.