## 5 Discussion
### Phenology-Based Methods in GPP Estimation with LUE Models--Strengths and Limitations  
Comparisons of four GPP products based on phenology-based \(\epsilon_{0}\) (denoted as GPP\({}_{\text{phe-based}}\)) and VPM GPP (GPP\({}_{\text{VPM}}\)) with GPP\({}_{\text{EC}}\) are shown in Figure 5. For the majority of the flux sites, the overall performances revealed good agreements between the four GPP products and GPP\({}_{\text{EC}}\), and the variation in accuracy was dependent on the use of FAPAR proxies. For forest ecosystems, FAPAR data products based on NDVI and EVI showed weaker improvement than LAI-based FAPAR in most years. In addition, compared with GPP\({}_{\text{EC}}\), these GPP\({}_{\text{phe-based}}\) values were comparable to GPP\({}_{\text{VPM}}\) at the forest sites, which implies that the use of phenology-based \(\epsilon_{0}\) in LUE models has limited effect on improving the accuracy of GPP estimation. For herbaceous plants, the correlations between GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{EC}}\) were subject to different PFTs. Generally, EVI- and NDVI-based FAPAR was found to be more effective than LAI-based FAPAR in GPP\({}_{\text{phe-based}}\) estimation for grassland and wetland ecosystems. The optimal GPP\({}_{\text{phe-based}}\) estimated from phenology-based \(\epsilon_{0}\) and EVI/NDVI/LAI-based FAPAR performed better than GPP\({}_{\text{VPM}}\) (0.62 vs. 0.14 in R\({}^{2}\) max), indicating significant enhancement by the phenology-based strategy in the estimation of \(\epsilon_{0}\) and its advantage of reduced uncertainty of the parameter. In terms of the cropland ecosystem, the LAI-based FAPAR used for GPP\({}_{\text{phe-based}}\) estimation worked better than the others in most years (four out of six site-years). The GPP\({}_{\text{phe-based}}\) was more accurate than the GPP\({}_{\text{VPM}}\) in the rice ecosystem (US-Twt site), but there was not much of difference between them in the dryland ecosystem (US-Ne2 site). Considering the positive influences of dynamic \(\epsilon_{0}\) based on phenological stages in GPP estimation in paddy field environments from previous research and this work [17; 55], \(\epsilon_{0}\) refined at fine spatial scales is a practicable way to extrapolate site GPP to regional or global scale for rice ecosystems. We compared vegetation photosynthesis model (VPM) GPP (GPP\({}_{\text{VPM}}\)), phenology-based VPM GPP (GPP\({}_{\text{phe-based}}\)), and eddy covariance-measured GPP (GPP\({}_{\text{EC}}\)) across five ecosystems. The substantial improvement in GPP reflects that \(\epsilon_{0}\) is influenced by the vegetation canopy absorption of solar radiation and internal physiological traits of plants. Under different solar radiation conditions, \(\epsilon_{0}\) showed different photosynthesis efficiency and rates, for example, increases in light use efficiency under overcast conditions and saturation of vegetation photosynthesis to solar radiation under high solar radiation. Additional factors to be consider are variations in the physiological traits of the vegetation itself, such as changes in chlorophyll content. The distinctive tillage characteristics of drylands and paddy fields, the instability of artificially restored wetlands, and the unique climatic circumstances of grasslands bring about significant changes in these ecosystems compared to in forest ecosystems. Therefore, the phenology-based GPP estimation substantially improved the estimation in croplands, grasslands, and wetlands compared to original models. Meanwhile, we also noticed that GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{VPM}}\) estimates lagged behind GPP\({}_{\text{EC}}\) for herbaceous plant in several years, particularly for the grassland site (US-VAR) in 2013. Examining the camera photos of the extracted SOS and EOS revealed no obvious flaws in the extraction process.