## 3 Results
### Phenology Estimated from Satellite-Based NDVI and SIF  
In this section, we assess the phenological transition dates derived from two satellite-based products including SIF\({}_{\text{740}}\) retrieved from GOME-2 (SIF\({}_{\text{GAME-2}}\)) and NDVI\({}_{\text{MODIS}}\). Because both have coarse spatial resolutions, the large field of view of these sensors may include considerable landscape heterogeneity. Due to the contribution from evergreen forest and mixed forest (Table 1), NDVI\({}_{\text{MODIS}}\) of deciduous forest still shows relative high values in the dormant season, and SIF\({}_{\text{GAME-2}}\) detects photosynthetic activities in the very early spring (Figure 2a,e). The start of spring (S1) determined from SIF\({}_{\text{GAME-2}}\) occurs more than one month earlier than those from the NDVI\({}_{\text{MODIS}}\) and GPP time series (Table 1). The springtime rise in SIF\({}_{\text{GAME-2}}\) is the earliest among all the time series. GOME-2 data are characterized by the large field of view of its sensor and coarse temporal resolution. The former feature may cause them contain signals from mixed vegetation. For the half-degree grid cell surrounding Harvard Forest, evergreen forest and mixed forest account for 25% and 16% (Table 1), which makes SIF\({}_{\text{GAME-2}}\) still receive SIF emission in the dormant season (Figure 2e). Similarly, understory component with an earlier greenness may also lead to an early onset of spring in SIF\({}_{\text{GAME-2}}\). All these factors more or less explain why S1 from SIF\({}_{\text{GAME-2}}\) occurred very early in 2014 (Table 1). In addition, the length of their maturity period (A1-S3) is clearly different from that derived from the GPP data, with 28 days longer in NDVI\({}_{\text{MODIS}}\) and 36 days shorter in SIF\({}_{\text{GAME-2}}\) (Table 1, Figure 2e). Remotely sensed datasets typically use values with the best quality within each time step to compose time series. Without information of date used in temporal composite, linear \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**GCC90**} & \multicolumn{2}{c}{**NDVI\({}_{\text{MODIS}}\)**} & \multicolumn{2}{c}{**NDVI\({}_{\text{Ground}}\)**} & \multicolumn{2}{c}{**SIF\({}_{\text{GAME-2}}\)**} & \multicolumn{2}{c}{**SIF\({}_{\text{Ground}}\)**} \\ \cline{2-10}  & **S** & **A** & **S** & **A** & **S** & **A** & **S** & **A** & **S** & **A** \\ \hline GPP & 0.18 & 0.13 & 0.22 & 0.29 & 0.32 & 0.37 & 0.17 & 0.07 & 0.08 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Mean Absolution Error (MAE) between tower-based GPP versus the 90 th percentile of the green chromatic coordinates (GCC90), ground-based NDVI (NDVI\({}_{\text{Ground}}\)) and ground-based SIF (SIF\({}_{\text{Ground}}\)) in both the spring (S) and autumn season (A). The phenological metrics derived from the tower-based GPP time series were used to define the spring and autumn. All time series are normalized and have a daily step. interpolation may cause large uncertainties in generating daily time series. Especially, the monthly time step of GOME-2 data may result in at most two-month bias in estimating these phenological metrics. However, the two satellite data and tower-based GPP produce the similar dates of middle of spring (S2), with an estimate of seven days earlier for both \(\text{SIF}_{\text{GOME-2}}\) and \(\text{NDVI}_{\text{MODIS}}\) compared to GPP data (Table 1). It again shows the scheme using half-maximum values provides a good resistance to noises induced by spatial heterogeneity. Although the spatial and temporal resolutions of \(\text{SIF}_{\text{GOME-2}}\) are coarse, it provides a better estimation of the autumn phenological metrics compared to \(\text{NDVI}_{\text{MODIS}}\). For example, the middle of fall (A2) derived from \(\text{SIF}_{\text{GOME-2}}\) is only seven days earlier compared to GPP observations, while the estimated A2 from \(\text{NDVI}_{\text{MODIS}}\) is more than three weeks later (Table 1).