## 3 Results  
### Phenology Derived from Reflectance-Based Indices  
In this section, we compare phenological transition dates derived from three reflectance-based indices: GCC90, MODIS NDVI (NDVIMODIS) and the ground-based NDVI sensor (NDVIGround). The two NDVI data remain at a stable level around 0.5 during the dormant period (Figure 2a,b) because their footprints are relatively large and thus they also reflect impacts from evergreen vegetation within the corresponding pixel. Compared to rapid increases during the leaf development phase in the spring, the two NDVI data show a relatively slower decline in the period of leaf senescence phase in the autumn (Figure 2a,b). This long tail phenomenon can be partially explained by the contribution from newly fallen leaves on the ground [61]. In contrast, GCC90 demonstrates a more rapid change in both rising and falling parts of the seasonal trajectory (Figure 2c) and it shows a clear peak at the end of spring, which can be explained by seasonal variations in foliage pigments [15]. Among these three reflectance-based indices, GCC90 shows the smallest MAE compared to GPP in both spring and autumn (Table 2), highlighting its potential in predicting the GPP-based phenology events. Its strong synchronicity with GPP agrees well with a recent study which reported GCC had a good performance to predict the GPP-based seasons [62]. The phenological transition dates extracted from the NDVIMODIS and NDVIGround are comparable to those from the GCC90 in the spring, especially for the middle of spring (S2), and autumn (A2), which are observed on about Day of Year (DOY) 136 and 288, respectively (Table 1). This finding is consistent with previous studies [13; 62], which also supported that NDVI and green index had similar performance in estimating spring phenology metrics. However, the other two autumn phenological metrics from the NDVIMODIS are largely different to those derived from GCC90 and NDVIGround: the start of autumn (A1) from NDVIMODIS occurred around 20 days earlier and the end of autumn (A3) occurred about 30 days later (Table 1). One weakness of spaceborne optical remote sensing likely explains these discrepancies: cloud cover may significantly reduce its usable observations. High frequent clouds in September and October 2014 may cause many missing values and the temporal interpolation may shift the position of A1 to the left and A3 to the right. It also exhibits the stable performance of using the date of half-maximum values to predict dates of the middle of spring (S2) and autumn (A2), especially under strong noisy conditions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **S1** & **S2** & **S3** & **A1** & **A2** & **A3** & **Footprint** & \begin{tabular}{c} **Percent Coverage of** \\ **Main Forest Types** \\ \end{tabular}  \\ \hline NDVIMODIS & 127 & 137 & 149 & 245 & 291 & 351 & 500 \(\times\) 500 m & \begin{tabular}{c} DF (40\%) \\ EF (16\%) \\ MF (16\%) \\ \end{tabular}  \\ \hline NDVIGround & 123 & 136 & 149 & 261 & 288 & 316 & 150 \(\times\) 300 m & \begin{tabular}{c} DF (85\%) \\ EF (25\%) \\ MF (16\%) \\ \end{tabular}  \\ \hline SIF\({}_{\text{Ground}}\) & 89 & 137 & 184 & 216 & 260 & 304 & 0.5 \(\times\) 0.5 degree & \ \begin{tabular}{c} DF (31\%) \\ EF (25\%) \\ MF (16\%) \\ \end{tabular}  \\ \hline SIF\({}_{\text{Ground}}\) & 127 & 141 & 151 & 257 & 273 & 296 & 4 \(\times\) 4 m & \begin{tabular}{c} DF (100\%) \\ \end{tabular}  \\ \hline GCC90 & 129 & 135 & 141 & 276 & 287 & 295 & \textasciThe autumn phenological metrics derived from the reflectance-based time series lagged significantly compared to GPP, although their performance is good for the spring phenological metrics. For example, the GPP-based A2 in 2014 occurred 20, 21 and 24 days ahead compared to GCC90, NDVI\({}_{\text{Ground}}\) and NDVI\({}_{\text{MODIS}}\), respectively (Table 1). The multiyear comparison (Tables S1-S3 and S6) also confirms that these three reflectance-based data tend to provide significant later transition dates (A2) (\(p<0.05\)).