## 4 Discussion  
NDVI has been widely used to track seasonal dynamics of photosynthesis and phenology based on NDVI are often compared to both flux tower measurements and remote sensing measurements. Not surprisingly, NDVI-based growing season has been found to be longer than GPP-based growing season. This is because, in the spring, photosynthesis lags behind leaf growth [26] while in the fall, leaf decay lags behind photosynthesis decline [25]. In order to improve VI-based phenology, temperature, which high-latitude forests are sensitive to and therefore dominating their phenology, is often taken into consideration (e.g., [7]). However, for boreal forests whose phenology is mainly determined by environmental factors, seasonal VI cycles also carry information of temperature and precipitation. Instead of introducing environmental stress factors, the present study aims to improve NDVI-based phenology at the perspective of using VI-based GPP estimation model.  
Because NDVI has been considered as a proxy for fPAR, the key of using the VI-based LUE model to track seasonal cycles of GPP is whether NDVI can be a reasonable proxy for LUE at seasonal scale. Remote sensing of LUE from greenness-related VIs is challenging due to the fact that LUE is jointly controlled by multiple factors and is sensitive to changes of environmental stress. As a matter of fact, VIs often show obvious lag when environmental stress occurs. At short-term scale (e.g., diurnal), where physiological factors such as non-photochemical quenching (NPQ) dominate LUE changes [15], greenness-related VIs generally fail to track LUE changes [14]. At seasonal scale, however, both LUE and NDVI are sensitive to canopy chlorophyll concentration [27; 28]. Thus NDVI may track seasonal changes of LUE. These facts give opportunities for using VI as a proxy for LUE at seasonal scale, especially for phenology study. As a matter of fact, linear relationships between seasonal NDVI and LUE has been reported by empirical analyses [18; 19]. Another possible reason for why NDVI-based phenology is improved by applying VI-based LUE model is that NDVI2 might be a good proxy for ecosystem/apparent LUE (i.e., the ratio of GPP and PAR). Numerically, squaring NDVI gives smaller value and the squared NDVI can be closer to actual fPAR related to GPP due to the fact that only a part of the canopy APAR (PAR absorbed by photosynthetically related pigments) is used for photosynthesis. Since LUE model is conceptual [11], NDVI2\(\times\) PAR might be a direct estimation of GPP regardless of using one NDVI as proxy for fPAR while using another as proxy for LUE.  
Results of the present study show that VI-based phenology is improved towards better tracking seasonal photosynthesis (GPP) dynamics through applying a VI-based LUE model. Better performance of the proposed method is found over ENF than DBF. This is because VI and photosynthesis have different mutual influencing mechanisms over these two plant functional types. NDVI variations of ENF are more controlled than those of DBF by changes of canopy pigment concentration, which is the main factor determining canopy LUE. Using remotely sensed VIs to monitor phenology over ENF is generally more difficult because ENF has smaller annual VI fluctuations.  
Figure 2: Start of season (SOS) and end of season (EOS) dates derived from the four selected indicators, for North America (**left**) and for Eurasia (**right**). DOY stands for day of year.  
A limitation of this study is that errors of MPI-GPP were not considered although MPI-GPP is generally considered to have overall good quality.