## 4 Discussion
### Effects of Meteorological Variables on Estimating Phenology  
In this section, we examine effects of two meteorological variables including air temperature (\(T_{\text{air}}\)) and PAR on phenological transition dates using the three in situ observations (i.e., GCC90, NDVI\({}_{\text{Ground}}\) and \(\text{SIF}_{\text{Ground}}\)) and tower-based GPP. Both \(T_{\text{air}}\) and PAR start to increase much earlier than the in situ time series and tower-based GPP (Figure 4). In comparison to LAI and Chl, both meteorological variables show much larger MAEs compared to GPP data in the spring (Table 3). It suggests \(T_{\text{air}}\) and PAR may not be direct factors determining GPP variations in the springtime in 2014, although these meteorological variables play as prerequisites of spring onset [64].  
Again, \(T_{\text{air}}\) plays a limited role in determining the autumn phenology metrics in 2014: the autumn evolution of \(T_{\text{air}}\) lags much behind that of the other four time series (Figure 4). In comparison to \(T_{\text{air}}\), PAR demonstrates a much earlier and faster decline trend (Figure 4). It is important to note these four time series show different responses to the drop of PAR: GCC90 and NDVI\({}_{\text{Ground}}\) show a delayed response to variations in PAR, while the \(\text{SIF}_{\text{Ground}}\) and GPP data closely follow the decreasing trend of  
Figure 3: Comparison of physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl) versus the 90 th percentile of the green chromatic coordinates (GCC90) **(a)**, ground-based NDVI (NDVI\({}_{\text{Ground}}\)) (**b)**, ground-based SIF (\(\text{SIF}_{\text{Ground}}\)) (**c)** and tower-based GPP (**d**) at Harvard Forest in 2014. All the time series are normalized and have a daily time step.  
PAR (Figure 3). Particularly, PAR and GPP show the highest synchronicity with each other with a MAE of only 0.09 (Table 3), which indicates that the GPP in the fall season is limited by the amount of PAR.  
In the autumn, solar radiation declines first, photosynthesis activities are immediately suppressed and then physiological traits decrease as a consequence of low carbon uptake. In other words, a good autumn phenology indicator should be able to account for effects of meteorological factors including PAR on GPP. All the reflectance-based data which use reflected signals as proxies to track photosynthesis activities actually capture subsequent consequences of decrease in GPP such that the autumn phenological metrics derived from them tend to lag after those from GPP time series. In contrast, SIF is a byproduct of photosynthetic activity and it can respond immediately to variations in photosynthesis caused by all possible factors including PAR. It explains why \(\text{SIF}_{\text{Ground}}\) has an advantage in estimating GPP-based phenology over \(\text{NDVI}_{\text{Ground}}\) and GCC90 in the fall season.  
Our finding is consistent with a recent study [20] which found that photosynthesis may already significantly decrease before the onset of yellowing in the autumn. In fact, carbon uptake is mainly driven by variations in solar radiation in many mid-to-high latitude ecosystems such as temperate forests, particularly after summer solstice [65]. Reflectance-based data may cause later estimations of autumn phenological metrics in these ecosystems [20].