## 1 Introduction  
Phenology is the study of the timing of vegetation growing/senescence events based on vegetation seasonal dynamics [1]. Satellite observations of land surface provides spatially-continuous time-resolved reflectance data of terrestrial vegetation. Therefore, extracting phenological timing from time-series remote sensing data is an effective tool for monitoring large-scale vegetation dynamics, especially where in-situ observations are lacking (e.g., [2]). Generally, remote sensing of vegetation phenology is based on the seasonal cycle of reflectance-based vegetation indices (VIs). VIs have been regarded as proxies of the green biomass of vegetation canopy, carrying information of leaf greenness and canopy structure [3]. Because VIs often show clear seasonal cycles over mid-high latitude forests, due to seasonal changes of temperature and precipitation [4, 5], numerous researches have utilized VI time-series curves to study the phenology of such vegetation. Among all the reflectance-based VIs, the normalized difference vegetation index (NDVI) has been most widely used to understand vegetation phenology at both regional and global scales. NDVI is a proxy of green biomass, which is related to canopy photosynthesis. Many studies used NDVI to track seasonal dynamics of carbon assimilation, comparing NDVI with gross primary production (GPP) which represents the photosynthetic carbon uptake [6, 7] over boreal forests. However, NDVI-baseddurations of growing season and photosynthesis season have been found to be significantly longer than those derived from both tower-based and simulated GPP [8; 9]. As a matter of fact, NDVI increases earlier in the spring and decreases later in the fall than GPP. As a result, start of season (SOS) date determined by NDVI is earlier than that determined by GPP while end of season (EOS) date appears to be later. Thus, NDVI is considered not sensitive for tracking photosynthesis dynamics of vegetation alone. However, a direct comparison between NDVI-based phenology and GPP-based phenology may be not reasonable. GPP can be described by the light use efficiency (LUE) model as follows:  
\[\text{GPP}\ =\ \text{PAR}\times\text{fPAR}\times\text{LUE}\ =\ \text{APAR}\times\text{LUE}, \tag{1}\]  
where PAR stands for photosynthetically active radiation (i.e., shortwave solar radiation between 400 nm and 700 nm) and fPAR stands for fraction of absorbed PAR (APAR). LUE model is a conceptual model originated from [10] based on physiological process of light absorption (APAR) and conversion (LUE) [11]. According to the LUE model, dynamics of GPP are jointly controlled by changes of PAR, fPAR and LUE. At seasonal scale, PAR is related to solar zenith angle and fPAR is determined by canopy structure and leaf greenness, which are directly related to NDVI, while LUE dynamics are more complex because it is related not only to vegetation growing status but to environmental (e.g., radiation, temperature and soil water) stress as well. Thus, in order to use NDVI-based phenology to track photosynthesis dynamics, one should also take seasonal cycles of PAR and LUE into consideration. Because VIs have been used as a robust proxy of fPAR [12] while PAR can be obtained from external data sources, many studies have used VI \(\times\) PAR as a proxy for APAR [8; 9]. However, Moderate Resolution Imaging Spectroradiometer- (MODIS-, onboard Aqua and Terra satellite instruments) based APAR is found to overestimate both the greenness season duration and the photosynthesis duration [8], and APAR is temporally biased compared to GPP [13], indicating that VI-based APAR is still not an appropriate proxy for carbon assimilation. Thus, LUE might be the main reason for inconsistencies between APAR-based phenology and GPP-based phenology. LUE can be estimated through narrow-band VIs such as photochemical reflectance index (PRI) [14; 15] or by introducing maximum LUE, temperature and precipitation stress factors [16].