## 4 Discussion  
### Accuracy of Site-Level VPP Estimates across PFTs  
The estimation accuracy of both SOG and EOG varied across forest types. Consistent with results from previous studies [17], our results indicate that the VPP for deciduous forest was detected more accurately than that for evergreen forest due to the relatively dramatic seasonal variation in canopy greenness. The accuracy of VPP for MF was medium between DBF and ENF. The results further indicate that the performance of phenology modeling based on vegetation indices largely depends on the variability in canopy greenness. Previous studies showed that the individual use of vegetation indices did not accurately predict LSP of ENF because of stable changes in seasonal canopy greenness [24].  
The importance of temperature in improving phenology modeling for ENF has been investigated in a number of studies [7; 24]. Temperature is the most important driving factor of photosynthetic activity for ENF and MF because these are primarily distributed in boreal and temperate regions (Figure S6). Therefore, the accuracy of VPP estimates for MF and ENF was clearly improved by including temperature as a variable in VPP modeling.  
Compared with forest types, our results indicate that VPP modeling for non-forest types is more challenging, probably due to the higher heterogeneity, sensitivity to climate change, disturbance, and the human management involved in non-forest types [2; 50]. Highly heterogeneous SAVA and OSH include more than two types of plant species, resulting in complicated phenology phases [2]. For example, SAVA consists of mixed grasses and trees, while temporal differences in EOS between these two vegetation types reached three months [2; 51]. Different plant species with the same eddy covariance footprint will vary in their contribution to carbon uptake, making it difficult to predict the phenological cycle of an ecosystem as a whole [16; 52]. Furthermore, increasing the spatial complexity of ecosystems within a MODIS pixel area reduces the ability of vegetation indices to explain carbon flux and be used for further VPP modeling [16]. The spectral signatures of CRO and GRA are usually combined with soil and vegetation at the beginning and end of the growing season, owing to the low presence of vegetation, while the EVI is less sensitive to changes in soil background [16; 53]. Thus, other vegetation indices with a strong ability to minimize the effect of the soil background may be more suitable for predicting phenology than the EVI for CRO and GRA [16]. Additionally, the variability in phenology of CRO is significantly influenced by human management, and there are large differences in phenology between single and double cropping patterns [54; 55; 56]. Furthermore, GRA is more  
Figure 7: Relationships of length of photosynthesis phenology season (LOG) and length of growing season (LOS) estimates to two individual global GPP products, **(a)** GPPVPM from the VPM model and **(b)** GLASS-GPP from the EC-LUE model, across all plant functional types within the seven selected tiles. Correlation coefficients are averaged in 2001-2016. The black vertical lines are \(\pm 1\) standard deviation. Evergreen needle-leaf forest (ENF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), open shrublands (OSH), savannas (SAV), woody savannas (WSA), grasslands (GRA), wetlands (WET), croplands (CRO), urban and built-up regions (URB), and cropland/natural vegetation (CNV).  
sensitive to climate and human disturbance [57; 58], which causes multiple factor effects on variations in VPP for GRA. All of these reasons result in high complexity and uncertainty in the modeling of VPP for non-forest types.