## 3 Results
### Reconstruction with Seasonal Segment Time-Series Fitting  
3.2.1. Smoothing Process  
Before the smoothing process of the SG filter, the gap-filled time series are extracted, and \(R^{2}\) and RMSE between the gap-filled time series and smoothing processed time series are calculated. The final statistical results are shown in Table 2.  
Before and after smoothing, nearly 95% of GPP time series data of all land cover types except EBF have smoothing RMSEs <5 g C m\({}^{-2}\). Almost 95% of LAI time series data of all land covers except EBF have a smoothing RMSE <5. The GPP and LAI time series data for forests, such as EBF, ENF, DBF, and MF, have relatively high RMSEs than those for grassland, cropland, savanna, and shrubland. However, the time series values of the forest types are higher than those of other land cover types, including cropland, grassland, savanna, and shrubland.  
The \(R^{2}\) result of the GPP and LAI smoothing process shows that: (1) except for cropland, EBF, and shrubland, nearly 90% of GPP time series points have \(R^{2}\)-values >0.90. The EBF time series points have lost more original characteristics (about 30% LAI time series points have RMSE >0.5 and >30% GPP time series points have RMSE >5) than other land cover types (>90% LAI time series points have RMSE <0.5 and >95% GPP time series point have RMSE <5); (2) nearly 85% of LAI time series points of all land covers except EBF have \(R^{2}\)-values >0.90. The smoothing processes applied to GPP and LAI time series maintained the original time series characteristics.  
For the NDVI data, the smoothing process was used to reduce noise. Nearly all of the data except for shrubland have RMSEs > 0.01, and almost 95% of data except that of EBF have \(R^{2}\)-values >0.9.  
For reflectance data, more than 90% of time series points have RMSEs <0.035, and more than 90% of land cover types except shrubland have \(R^{2}\)-values >0.9. The data for forest types, including the ENF, DBF, and MF, have higher \(R^{2}\)-values than other land covers. In addition, the reflectance data of land cover types except shrubland have lower RMSEs  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{**Data type**} & \multicolumn{6}{c}{**GPP**} \\ \hline land cover & Grassland & Cropland & DBF & EBF & ENF & MF & Savannas & Shrubland \\ \hline \(R^{2}\) (\(>\)0.9) & 99 & 97 & 100 & 93 & 100 & 100 & 99 & 96 \\ RMSE (\(<\)5) & 99 & 99 & 93 & 64 & 97 & 97 & 97 & 100 \\ num\_points & 985 & 985 & 490 & 485 & 480 & 490 & 470 & 980 \\ \hline \multicolumn{2}{c}{**Data type**} & \multicolumn{6}{c}{**NDVI**} \\ \hline landcover & Grassland & Cropland & DBF & EBF & ENF & MF & Savannas & Shrubland \\ \hline \(R^{2}\) (\(>\)0.9) & 95 & 90 & 100 & 33 & 94 & 98 & 94 & 86 \\ RMSE (\(>\)0.01) & 96 & 99 & 100 & 99 & 100 & 100 & 100 & 79 \\ num\_points & 945 & 842 & 481 & 202 & 415 & 470 & 446 & 977 \\ \hline \multicolumn{2}{c}{**Data type**} & \multicolumn{6}{c}{**LAI**} \\ \hline landcover & Grassland & Cropland & DBF & EBF & ENF & MF & Savannas & Shrubland \\ \hline \(R^{2}\) (\(>\)0.9) & 98 & 97 & 97 & 93 & 98 & 98 & 97 & 97 \\ RMSE (\(<\)0.5) & 100 & 100 & 91 & 70 & 94 & 92 & 98 & 100 \\ num\_points & 979 & 831 & 490 & 490 & 480 & 490 & 485 & 923 \\ \hline \multicolumn{2}{c}{**Data type**} & \multicolumn{6}{c}{**MSR**} \\ \hline landcover & Grassland & Cropland & DBF & EBF & ENF & MF & Savannas & Shrubland \\ \hline \(R^{2}\) (\(>\)0.9) & 91 & 96 & 99 & 94 & 98 & 99 & 97 & 67 \\ RMSE (\(<\)0.035) & 91 & 97 & 99 & 99 & 99 & 98 & 92 & 93 \\ num\_points & 817 & 736 & 440 & 142 & 374 & 395 & 382 & 730 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of the percentages of sampling points in the threshold range after smoothing.  
and higher \(R^{2}\)-values. This can, to some extent, indicate that the smoothing process reduces noise in the time series data and maintains its original long-term trend.