## 3 Results
### Meteorology  
In this paper, the MOD_NCEP GPP algorithm followed the standard MOD17A2 algorithm, which used the NCEP-DOE Reanalysis II meteorology data. However, the NCEP-DOE Reanalysis II meteorology data were replaced with the site meteorology data for use with the MOD_Tower GPP algorithm. Thus, the MOD_NCEP GPP and MOD_Tower GPP algorithms were compared to study the impacts of the meteorological data on the estimation results. An analysis of the seasonal and annual GPP estimates showed that the MOD_Tower GPP algorithm with the site meteorology data was inferior to the MOD_NCEP GPP algorithm (Table 3). However, in the case of the eight-day GPP estimates, the estimating effectiveness of the MOD_Tower GPP algorithm was better than that of the MOD_NCEP GPP algorithm, and the improvements were seen at most of the sites (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Mean (SD)**} & \multicolumn{2}{c}{**Bias**} & \multirow{2}{*}{**RMSE (\%)**} & \multirow{2}{*}{**RE (\%)**} \\  & & **(gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\))** & & **(gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\))** & & \\ \hline \multicolumn{4}{c}{**Mixed Forest**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & - & 1341(110) _vs._ 1085(14) & \(-256\) & 21 & 19 \\ Flux\_Tower _vs._MOD\_Tower & 0.71 * & 1341(110) _vs._ 752(72) & \(-589\) & 44 & 44 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.27 & 1341(110) _vs._ 1281(32) & \(-60\) & 11 & 11 \\ Flux\_Tower _vs._Tower\_GLASS & 0.97 * & 1341(110) _vs._ 887(64) & \(-454\) & 34 & 34 \\ \hline \multicolumn{4}{c}{**Evergreen Needleleaf Forest**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & 0.14 & 1745(100) _vs._ 1467(131) & \(-278\) & 17 & 16 \\ Flux\_Tower _vs._MOD\_Tower & 0.55 & 1745(100) _vs._ 906(105) & \(-839\) & 48 & 48 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.50 & 1745(100) _vs._ 1669(56) & \(-76\) & 6 & 4 \\ Flux\_Tower _vs._Tower\_GLASS & 0.58 & 1745(100) _vs._ 1019(78) & \(-726\) & 42 & 42 \\ \hline \multicolumn{4}{c}{**Evergreen Broadleaf Forest**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & 0.58 * & 2072(715) _vs._ 2127(423) & 55 & 22 & 21 \\ Flux\_Tower _vs._MOD\_Tower & 0.34 & 2072(715) _vs._ 1562(441) & \(-510\) & 35 & 21 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.52 * & 2072(715) _vs._ 2700(190) & 628 & 41 & 48 \\ Flux\_Tower _vs._Tower\_GLASS & 0.18 & 2072(715) _vs._ 1903(363) & \(-169\) & 29 & 21 \\ \hline \multicolumn{4}{c}{**Cropland**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & - & 1707(105) _vs._ 888(38) & \(-819\) & 49 & 48 \\ Flux\_Tower _vs._MOD\_Tower & - & 1707(105) _vs._ 690(36) & \(-1017\) & 60 & 59 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.98 * & 1707(105) _vs._ 1062(35) & \(-645\) & 39 & 37 \\ Flux\_Tower _vs._Tower\_GLASS & - & 1707(105) _vs._ 814(25) & \(-893\) & 53 & 52 \\ \hline \multicolumn{4}{c}{**Grassland**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & 0.65 ** & 354(182) _vs._ 379(185) & 25 & 30 & 53 \\ Flux\_Tower _vs._MOD\_Tower & - & 354(182) _vs._ 255(61) & \(-99\) & 58 & 55 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.80 ** & 354(182) _vs._ 399(223) & 45 & 29 & 46 \\ Flux\_Tower _vs._Tower\_GLASS & 0.52 * & 354(182) _vs._ 258(39) & \(-96\) & 51 & 48 \\ \hline \multicolumn{4}{c}{Significance levels: * \(p\) < 0.05, ** \(p\) < 0.01.} \\ \hline \multicolumn{4}{c}{**Grassland**} \\ \hline Flux\_Tower _vs._MOD\_NCEP_ & 0.65 ** & 354(182) _vs._ 379(185) & 25 & 30 & 53 \\ Flux\_Tower _vs._MOD\_Tower & - & 354(182) _vs._ 255(61) & \(-99\) & 58 & 55 \\ Flux\_Tower _vs._NCEP\_GLASS & 0.80 ** & 354(182) _vs._ 399(223) & 45 & 29 & 46 \\ Flux\_Tower _vs._Tower\_GLASS & 0.52 * & 354(182) _vs._ 258(39) & \(-96\) & 51 & 48 \\ \hline \end{tabular}
\end{table} 
Table 4: Biome-based comparison of the annual GPP derived from the tower estimates with those derived from the MODIS algorithms (MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP). The correlations between the daily NCEP-DOE Reanalysis II meteorology data and the daily site meteorology data were analyzed (Figure 9).