## 4 Discussion
### Effects of Biophysical Properties on Estimating Phenology  
In this section, we discuss the roles of two physiological properties, LAI and Chl, in determining the plant phenological events. We use the time series from three ground-based sensors including GCC90, \(\text{NDVI}_{\text{Ground}}\) and \(\text{SIF}_{\text{Ground}}\). Because all these sensors are near ground surface and collect measurements with high temporal frequencies, negative influences from cloud and landscape heterogeneity can be effectively suppressed. The temporal trajectory of GPP is highly consistent with LAI and Chl in the spring season (Figure 3). The statistical analysis also supports that GPP is more synchronous with the two physiological properties than meteorological factors including air temperature and Photosynthetically Active Radiation (PAR, umol photons m\({}^{-2}\) s\({}^{-1}\)) during the spring period: LAI and Chl have MAE values less than 0.10 with respect to GPP in the spring, whereas MAE values between GPP and the two meteorological variables are more than 0.25 (Table 3). It shows that: (1) GPP-based phenological transition dates in the spring are more regulated by LAI and Chl than the two meteorological factors; and (2) phenological indicators which can track seasonal variations of these two plant physiological properties should also have a potential to predict the GPP-based spring phenology well. Previous studies [54; 63] showed that GCC90 is highly correlated with both LAI and Chl. Our results also confirm that GCC90 generally follows the temporal patterns of LAI and Chl in both spring and autumn periods (Figure 3a), which explains why GCC90 can estimate the middle of spring (S2) well (Table 3). However, we found a clear saturation phenomenon: GCC90 stops increasing when LAI and Chl exceed their thresholds (Figure 3a). It is one of the important reasons why there are discrepancies in estimating the other two springtime phenology metrics (S1 and S3) using GCC90. Although NDVI\({}_{\text{Ground}}\) also generally follows the pattern of LAI and Chl, its estimated spring onset is earlier (Figure 3b) compared to LAI and Chl, probably due to its relative large footprint. It also partially explains why NDVI\({}_{\text{Ground}}\) produces an earlier S2 than GPP (Table 1). Compared to the GCC90 and NDVI\({}_{\text{Ground}}\), \(\text{SIF}_{\text{Ground}}\) is more closely with these two properties in the leaf development stage (Figure 3c). \(\text{SIF}_{\text{Ground}}\) shows the lowest MAE values with LAI and Chl in the spring season (Table 3), which supports that it is strongly synchronous with the physiological properties in the spring season. Both GCC90 and NDVI, similar to many other reflectance-based data, are designed to capture variations in selected physiological properties such as greenness, LAI and Chl. Because photosynthesis is largely regulated by these properties in the spring, GCC90 and NDVI have a good potential to track GPP-based phenology metrics in the spring well if the impact of spatial heterogeneity and coarse temporal resolution can be effectively reduced. Furthermore, SIF has a more direct link with photosynthesis activities such that it has a good performance in estimating GPP-based phenology in the spring as well. On the contrary, we found an obvious decouple between these two physiological properties and photosynthesis rate in the fall offset season (Figure 3d). LAI and Chl show a clear lag behind GPP: GPP start dropping on DOY 235, while autumn leaf Chl reduction and LAI decline begins by about 15 and 8 days later, respectively. In addition, LAI and Chl have much larger MAE values relative to GPP data in the fall season compared to the spring season (Table 3). It suggests that the two physiological properties are not good indicators of GPP-based transition dates in the autumn. The decouple between the plant physiological properties (e.g., LAI and Chl) and photosynthesis rate in the fall season is