## 3 Results
### GPP Assessments on Annual Scale  
More annual GPP data estimated from the observations on the eddy towers were collected from the peer-reviewed literature (see Table 1). A comparison of results is shown in Figure 5. GPP\({}_{\rm MOD15}\) had the best estimate of GPP (Adj. \(R^{2}\) 0.49) and was significantly correlated with GPP\({}_{\rm OBS}\) products (\(p<0.001\)) based on all the available validation data. GPP\({}_{\rm NDVI3g}\) (Adj. \(R^{2}\) 0.35), and GPP\({}_{\rm MOD17}\) (Adj. \(R^{2}\) 0.16) showed less significant correlations (Figure 5, Table 3) while GPP\({}_{\rm NDVI1g}\) was not significantly correlated with GPP\({}_{\rm OBS}\) (\(p=0.74\)). The GPP validation data were diverse, e.g., PDF-DB is a site where the forest was burnt in 1997 and 2002. Also, the fetch of the eddy tower with 4-m height was too small (around 250-1000 m in different directions) [73], such that the GPP simulation at 8-km resolution was unable to capture the fire effects. After excluding the PDF-DB site, the Adj. \(R^{2}\) increased to 0.85 and 0.51, respectively, for the GPP\({}_{\rm MOD15}\) and GPP\({}_{\rm MOD17}\) estimates (Table 3). We excluded more abnormal observation points progressively to achieve a group of best fits (shown as the solid lines in Figure 5) at which the Adj. \(R^{2}\) reached a maximum value (best-fitting hereafter). The adjusted \(R^{2}\) for GPP\({}_{\rm NDVI3g}\) was always higher than that for GPP\({}_{\rm NDVI1g}\) in each step, which suggested that GIMMS NDVI3g has higher quality than its old version (GIMMS NDVI) in GPP monitoring. The adjusted \(R^{2}\) for GPP\({}_{\rm NDVIg}\) and GPP\({}_{\rm NDVI1g}\) underwent a dramatic improvement to 0.82 and 0.73 after the best-fitting. The GPP\({}_{\rm NDVI2g}\) overestimated the regression slope (1.05 in Table 3) by  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multicolumn{4}{c}{**Subtropical Evergreen Needleaf Forest (QYZ)**} & \multicolumn{4}{c}{**Tropical Peat Swamp Forest (PDF)**} \\ \cline{2-10}  & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** & **GPP\({}_{\rm NDVIg}\)** \\ \hline Whole Year (gC m\({}^{-2}\) a\({}^{-1}\)) & & & & 1803.1 & & 4365.3 & 3350.4 & 2176.6 & 3122.6 & 3246.1 \\
2003 & 1430.7 & 1080.8 & 1362.6 & 1674.2 & 1610.4 & 4785.2 & 4656.2 & 2487.7 & 3053.4 & 3460.6 \\
2004 & 1453.7 & 1319.8 & 1606.9 & 1884.6 & 1867.4 & 4786.3 & 4294.8 & 2447.7 & 3166.0 & 3593.8 \\
2005 & 1319.6 & 1098.2 & 1259.3 & 1653.4 & 1912.0 & 4632.4 & 4023.8 & 2534.2 & 2711.3 & \\ May–October (gC m\({}^{-2}\) in 6 months) & & & & & & & & & & \\
2002 & & & & & 1180.2 & & 2221.6 & 1517.7 & 939.1 & 1575.0 & 1652.3 \\
2003 & 1091.7 & 862.6 & 1039.4 & 1042.9 & 1068.5 & 2501.3 & 2410.5 & 1333.0 & 1586.6 & 1806.9 \\
2004 & 1151.0 & 1058.5 & 1211.1 & 1237.4 & 1294.5 & 2451.0 & 2183.2 & 1358.8 & 1698.9 & 1848.3 \\
2005 & 1130.6 & 935.0 & 1022.2 & 1188.6 & 1340.9 & 2243.7 & 1954.3 & 1366.6 & 1423.3 & \\ November–April (gC m\({}^{-2}\) in 6 months) & & & & & & & & & & \\
2002 & & & & 623.3 & & 2143.6 & 1832.6 & 1237.5 & 1547.4 & 1593.8 \\
2003 & 339.0 & 218.1 & 323.2 & 631.4 & 541.9 & 2283.8 & 2245.7 & 1154.7 & 1467.1 & 1653.7 \\
2004 & 302.6 & 261.3 & 395.8 & 647. 5% and the GPP\({}_{\rm NDVI1g}\) underestimated it by 43%. The GPP\({}_{\rm MOD15}\) reached the best-fit after removing only one site (PDF-DB) with the Adj. \(R^{2}\) 0.85; while the GPP\({}_{\rm MOD17}\) reached the best-fit after removing two sites (PDF-DB and MKL) with the Adj. \(R^{2}\) 0.67. The GIMMS NDVI3g had a better estimate of GPP (slope = 1.05) relative to the MOD15 (slope = 0.87) and MOD17 (slope = 0.45) based on the final best-fit sites. These results indicate GIMMS NDVI3g has a reasonably good capability to simulate GPP.