## 3 Results
### Spatial and Temporal Comparison among Four GPP Results  
A linear regression analysis was applied to investigate the consistency between GPP\({}_{\rm NDVI3g}\) and the other three results (GPP\({}_{\rm MOD15}\), GPP\({}_{\rm NDVI1g}\), GPP\({}_{\rm MOD17}\)) and the scatter plots are shown in Figure 6. The results indicated that the GPP\({}_{\rm NDVI3g}\) have significant linear correlations (\(p<0.0001\)) with the other three results. Due to the higher data range of the original GIMMS NDVI3g data, GPP\({}_{\rm NDVI3g}\) overestimated the GPP values relative to the other three results. As GPP\({}_{\rm MOD15}\) was the best estimation stated in Section 3.2(2), we used it as the baseline and adjusted the GPP\({}_{\rm NDVI3g}\) by using the linear regression coefficient. The adjusted GPP\({}_{\rm NDVI3g}\) was used for the following analysis and comparisons. Figure 5: GPP data published in peer-reviewed literature (Table 1) compared with GPP modeled with the FPAR from GIMMS NDVI3g (GPP\({}_{\rm NDVI3g}\), (**a**)), GIMMS NDVI (GPP\({}_{\rm NDVI1g}\), (**b**)), MOD15A2 (GPP\({}_{\rm MOD15}\), (**c**)) through GLOPEM-CEVSA model, and the MODIS GPP product (GPP\({}_{\rm MOD17}\), and (**d**)) through the PSN model. Solid points were used in the linear regressions; open points were excluded as exceptions. The relationships between the modeled (GPP\({}_{\rm MOD}\)) and the observed GPP (GPP\({}_{\rm OBS}\)) are shown in Table 3 for three levels: all data, data excluding the PDF-DB site, and best fit data (solid points). The sample numbers are detailed in Table 3. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Modeled GPP**} & \multicolumn{2}{c}{**Intercept**} & \multicolumn{2}{c}{**Slope**} & \multicolumn{2}{c}{**Statistics**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{1}{c}{**Sample**} \\ \cline{2-9}  & **Value** & **Error** & **Value** & **Error** & **Adj. \(R^{2}\)** & \(p\)** & **(\%)** & **Number** \\ \hline
**All data** & & & & & & & & \\ GPP\({}_{\text{NDVI3g}}\) & 3.10 & 0.33 & 0.53 & 0.12 & 0.35 & 0.00 & 72.8 & 38 \\ GPP\({}_{\text{NDVI1g}}\) & 3.10 & 0.52 & \(-\)0.06 & 0.18 & \(-\)0.03 & 0.74 & 37.9 & 33 \\ GPP\({}_{\text{MOD15}}\) & 0.53 & 0.25 & 0.57 & 0.09 & 0.49 & 0.00 & 32.6 & 43 \\ GPP\({}_{\text{MOD17}}\) & 1.76 & 0.22 & 0.23 & 0.08 & 0.16 & 0.00 & 34.1 & 43 \\
**Exclude PDF-DB** & & & & & & & & \\ GPP\({}_{\text{NDVI3g}}\) & 2.84 & 0.41 & 0.61 & 0.14 & 0.35 & 0.00 & 66.2 & 35 \\ GPP\({}_{\text{NDVI1g}}\) & 2.79 & 0.63 & 0.04 & 0.21 & \(-\)0.03 & 0.87 & 32.1 & 31 \\ GPP\({}_{\text{MOD15}}\) & \(-\)0.42 & 0.17 & 0.87 & 0.06 & 0.85 & 0.00 & 29.8 & 39 \\ GPP\({}_{\text{MOD17}}\) & 1.03 & 0.22 & 0.45 & 0.07 & 0.51 & 0.00 & 28.0 & 38 \\
**Best-Fitting** & & & & & & & & \\ GPP\({}_{\text{NDVI3g}}\) & 2.07 & 0.30 & 1.06 & 0.11 & 0.82 & 0.00 & 46.2 & 19 \\ GPP\({}_{\text{NDVI1g}}\) & 2.04 & 0.25 & 0.57 & 0.09 & 0.73 & 0.00 & 26.6 & 14 \\ GPP\({}_{\text{MOD15}}\) & \(-\)0.42 & 0.17 & 0.87 & 0.06 & 0.85 & 0.00 & 29.8 & 39 \\ GPP\({}_{\text{MOD17}}\) & 1.02 & 0.15 & 0.45 & 0.05 & 0.67 & 0.00 & 30.2 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of linear regression results of modeled (GPPMOD) and observed GPP (GPPOBS) with the formula: \(GPP_{\text{MOD}}=GPP_{\text{OBS}}\times slope+Intercept\). GPP was modelled by GLOPEM-CEVSA with FPAR from GIMMS NDVI3g, GIMMS NDVI and MODIS product (MOD15A2) respectively. MOD17A2 is the GPP product of MODIS and was calculated by the MOD17 algorithm applying the FPAR from MOD15A2. The results are given with the value and error of intercept and slope, the statistics of adjusted coefficient of determination (Adj. \(R^{2}\)) and significance level (\(p\)), and the relative root-mean-square error (RMSE, %). PDF-DB is for data acquired at the swamp forest (DB) site of PDF disturbed by burning.