## 1 Introduction  
Land surface phenology (LSP) is defined as the seasonal pattern of variation in vegetated land surfaces [1, 2]. Because photosynthetic carbon uptake is closely associated with the length of the growing season, LSP has been widely used to discover the effects of climate change on vegetation biophysical processes, especially related to carbon exchange between the atmosphere and terrestrial ecosystems [3, 4]. Various data sources have been applied to extract LSP, including remote sensing, ground camera imagery, and FLUXNET measurements [4, 5, 6, 7, 8]. The phenological metrics of vegetation obtained from these different data sources have various definitions and biophysical meanings [9]. However, spatial- and species-specific discrepancies as well as the effectiveness in accurately describing variations in vegetation carbon fluxes among these vegetation phenological metrics remain unclear. Remote sensing data have been widely used to extract LSP data and analyze their regional- to global-scale changes [3, 6]. Time series of vegetation indices include three key transition dates, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). The SOS and EOS indicate the timing of vegetated land surface green-up and dormancy, respectively [2,6]. In addition to remote sensing data, an increasing number of studies have focused on the retrieval of LSP through flux-measured gross primary productivity (GPP) [10, 11, 12, 13]. Key transition dates derived from GPP are commonly defined as in LSP, including SOS and EOS [7,14,15]. However, results from previous studies indicate that LSP dates derived from GPP are related to the starting and ending days of photosynthetic phenology [11,16], which differ from the LSP derived from remote sensing data [11]. Unlike LSP from remote sensing data that represents the annual cycle of leaf coloration, LSP from flux-measured GPP more objectively represents the key transitions of ecosystem processes, such as vegetation carbon fluxes [11,13,14]. Therefore, to differentiate from LSP obtained using remote sensing data, we refer to LSP dates from GPP as vegetation photosynthetic phenology (VPP), based on previous studies on photosynthetic phenology [4,11,16]. Three key VPP dates, including the starting and ending days of GPP (SOG and EOG, respectively) and the length of the photosynthetic phenology season (LOG), were defined and used in this study. The VPP is related to the LSP and is controlled by it, yet the two metrics are not identical and have different biophysical meanings. A study based on the comparison between LSP from remotely sensed vegetation indices and VPP from GPP showed that LSP estimates were not equivalent to VPP estimates and differed in both the sign and magnitude of lags [11]. Furthermore, the relationship between LSP and VPP varied across the same plant functional type (PFT), implying that LSP and VPP do not covary each other in the same direction [11]. A large difference was found between LSP and VPP estimates, particularly for evergreen needle forests (ENFs), which feature relatively stable seasonal variation in vegetation indices [17]. The main reason for these differences is that LSP and VPP are regulated by different driving factors. While LSP largely depends on the intra-annual changes in canopy greenness, VPP is strongly regulated by external factors, such as temperature, radiation, and precipitation [18, 19, 20]. Among these external factors, temperature is an important driver of variation for VPP in boreal and temperate regions [12]. Investigating the difference between LSP and VPP and improving our understanding of the relationship between phenology metrics and carbon fluxes are important for accurately estimating carbon fluxes based on land surface models. This can further our understanding of the effects of global change on the carbon cycle [11,21,22, 23].