## 5 Discussion  
### SOS/EOS Estimating Methods of Different PFTs  
Extracting the phenology of different PFTs from time series satellite data is a preliminary step in ecological remote sensing research. In this study, we used two methods, the HGAM and DLF, on three vegetation indices (EVI/NDVI/GCC) to extract the crucial phenological stages (SOS/EOS) of different vegetation types [61; 62]. On the one hand, the three vegetation indices have inherent differences in their characteristics. NDVI serves as the foundation of remote sensing phenology and is sensitive to the biochemical and physiological characteristics of vegetation, commonly used for distinguishing between vegetation and bare soil [48]. Using the dynamic threshold method to extract phenological parameters from NDVI time series can improve the prediction of soil organic carbon content during crop rotation [63]. EVI, as an enhanced vegetation index, exhibits higher sensitivity in areas with high biomass. Research has shown that EVI outperforms NDVI in vegetation monitoring, particularly in regions dominated by evergreen broadleaf  
Figure 4: The comparisons of seasonal variations of the five GPP products and GPP\({}_{\text{EC}}\). forests [49]. Using the sigmoidal logistic function, the mutual comparison and interpretation of phenological indicators derived from MODIS EVI and flux tower GPP were validated in southern Taihang Mountains, China [64]. GCC, a derivative product that was developed after the emergence of phenocamera data, holds great potential for detecting vegetation canopy development [65]. The study conducted by [PERSON] et al. provided a comprehensive perspective on the phenology of complex wetland landscapes by utilizing the DLF method and GCC data [60]. The amplitude of NDVI variation within a year is frequently greater than that of EVI and GCC due to its sensitive response to the change of vegetation canopy compared to other indices. For the estimation of the SOS and EOS of the forest ecosystem in our study, NDVI-based DLF/HGAM algorithms were inferior to the same EVI-based algorithms. On the other hand, the principles of the two phenology extraction algorithms are different. The double logistic function algorithm evolved from the simple logistic (SL) method with the inclusion of additional variables that are assumed to better detect the changes in greenness from extreme events [51]. The DLF algorithm has shown its potential in monitoring vegetation seasonal growth dynamics as it showed good performance in fitting growth curves in some PFTs (e.g., it fitted well in ENF, paddy fields, and wetlands in our study). However, the maximum rate of change (both increasing and decreasing) of the fitted VI curves supposed to be the SOS and EOS was not accepted. Adversely, over-fitting in green-up or senescence stages, resulting in false inflection points, may have been recognized as the SOS and EOS. Moreover, we found under-fitting of the EVI curve in growth peaks at US-MMS (DBF site), as well as the identical circumstance for the NDVI curves at US-Ne2 (cropland site) and US-Ho1 (ENF site), which implies that there are large uncertainties relating to DLF algorithms applied at regional scale. Generally, the HGAM algorithms showed better performance than DLF in the extraction of the SOS and EOS of different PFTs. It could capture not only a single peak in cropland/grassland/ENF ecosystems, but also asymmetrical peaks in DBF ecosystems during the peak season. Furthermore, VI curves in the periods before green up and after senescence were well tracked by the HGAM algorithms. The HGAM approach allows for the automatic derivation of the predictor functions, which eliminates the requirement to know the fitting parameters and the ultimate kind of predictive functions in advance [20; 61]. This can detect intra-annual variations of NDVI/EVI/GCC in the entire growth season across different PFTs using regression splines and smoothing splines [20].