## 5 Conclusions  
Our results show that VPP was accurately estimated with a RMSE of approximately two weeks for forest types and three weeks for non-forest types through a combination of MODIS EVI and LST using a series of PFT-specific statistical models. Accurately estimating the VPP for non-forest types was more challenging than for forest types, primarily due to the high heterogeneity and human disturbances in non-forest types. We suggest that methods that are more effective be developed for improving VPP estimation of those PFTs with complicated seasonal variations in photosynthetic carbon uptake, such as multiple growing seasons, as well as SOG and EOG in the preceding year or in the following year. The dates of snow/ice cover used in this study played a useful role, to some extent, in constraining and improving the VPP estimates in high-latitude regions. The VPP is not identical to LSP, and the bias between VPP and LSP varied across PFTs and climate regions. High agreement between VPP and LSP from different data sources was found for DBF, although systematic differences were present. The spring photosynthetic phenology occurred slightly later than SOS, while the autumn photosynthetic phenology occurred slightly earlier relative to EOS for DBF. The sign of bias between VPP and LSP for ENF was opposite to that of DBF, with SOG occurring earlier than SOS and EOG occurring later than EOS. Generally, for forest types, the bias between VPP and LSP was larger in warm and humid regions than in cold high-latitude and high-altitude regions. Climatic factors and canopy greenness jointly controlled spatial variations in discrepancies between VPP and LSP. Discrepancies between VPP and LSP for non-forest types were more complicated than those for forest types. High discrepancies between VPP and LSP were found in arid tropical and temperate Asian regions for CRO and GRA due to their sensitivity to extreme climatic conditions and the intensive management of planting and grazing. The method proposed in this study failed to extract the VPP of CRO with multiple cropping patternsand other vegetation types with multiple growing seasons triggered by alternating drought and precipitation periods. A stronger correlation was found between LOG from VPP estimates and GPP than that between LOS from LSP estimates and GPP for the majority of PFTs, especially for ENF and CRO. The VPP estimates mapped in this study provided a carbon-objective vegetation phenology and optimized the vegetation phenology module of land surface models. This improves the estimates of carbon and water exchange between the atmosphere and terrestrial ecosystem and enhances our understanding of the relationship and feedback between vegetation and climate under current and future climate change. The following are available online at [[https://www.mdpi.com/article/10.3390/rs13245080/s1](https://www.mdpi.com/article/10.3390/rs13245080/s1)]([https://www.mdpi.com/article/10.3390/rs13245080/s1](https://www.mdpi.com/article/10.3390/rs13245080/s1)). Figure S1: An example shows how starting and ending days of GPP (SOG and EOG, respectively), and GPPmax are derived from daily GPP time series; multi-years averaged daily GPP are indicated with black circle, and solid red line are smoothed daily GPP; 15% amplitude threshold dates are indicated with vertical lines; minimum (GPPmin) and maximum (GPPmax) of smoothed daily GPP are indicated with horizontal lines.