## 3 Results
### GPP Validation
#### 3.1.3 Annual GPP  
The tower-based GPP measurements (Flux_Tower GPP) were compared with the results of the four algorithms (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP) during 2003-2005. (Figure 8, Table 3). For all the sites, the annual average value of the Flux_Tower GPP was 1331 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), and those of the four algorithms were 1170 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 827 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), 1410 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) and 972 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), respectively. The NCEP_GLASS GPP algorithm accurately estimated the measurements for CBS, QYZ and HB (Figure 8c) and therefore had the smallest bias (79 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)). However, for the MOD_Tower GPP and Tower_GLASS GPP algorithms, the Flux_Tower GPP for CBS, HB, QYZ, XSBN and YC were severely underestimated (Figure 8b,d), which led to large biases (\(-\)504 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) for MOD_Tower GPP and \(-\)359 gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\) for Tower_GLASS GPP). In terms of the estimating effectiveness, the MOD_NCEP GPP algorithm had the greatest performance because it yielded the largest R\({}^{2}\) (0.76) and smallest RMSE (31%) and RE (34%).  
Based on the various biome types, the tower-based GPP measurements (Flux_Tower GPP) were compared with the results of the four algorithms (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP) over several years (Table 4). In the case of MF, the Tower_GLASS GPP algorithm exhibited a significant correlation (R\({}^{2}\) = 0.97), though it seriously  
Figure 8: Comparisons of the annual Flux_Tower GPP with: (**a**) MOD_NCEP GPP; (**b**) MOD_Tower GPP; (**c**) NCEP_GLASS GPP; and (**d**) Tower_GLASS GPP. Significance levels: \({}^{**}p<0.01\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comparison** & **R\({}^{2}\)** & **Mean (SD)** & **Bias** & **RMSE** & **RE** \\ \hline
**Annual** & & **(gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\))** & **(gC\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\))** & **(\%)** & **(\%)** \\ \hline Flux\_Tower \(\tt{vs.}\) MOD\_NCEP & 0.76 \({}^{**}\) & 1331(806) \(\tt{vs.}\) 1170(731) & \(-\)161 & 31 & 34 \\ Flux\_Tower \(\tt{vs.}\) MOD\_Tower & 0.72 \({}^{**}\) & 1331(806) \(\tt{vs.}\) 827(559) & \(-\)504 & 50 & 44 \\ Flux\_Tower \(\tt{vs.}\) NCEP\_GLASS & 0.68 \({}^{**}\) & 1331(806) \(\tt{vs.}\) 1410(913) & 79 & 39 & 35 \\ Flux\_Tower \(\tt{vs.}\) Tower\_GLASS & 0.69 \({}^{**}\) & 1331(806) \(\tt{vs.}\) 972(664) & \(-\)359 & 43 & 38 \\ \hline \hline \end{tabular}  
Significance levels: \({}^{**}p<0.01\).  
\end{table}
Table 3: _Cont._underestimated the Flux_Tower GPP (\(-454\) gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\)). For ENF, the estimated results of the four algorithms were not statistically significant. The NCEP_GLASS GPP algorithm performed relatively well and had the smallest bias (\(-76\) gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\)), RMSE (6%) and RE (4%). For EBF, the MOD_NCEP GPP algorithm was the most effective; it exhibited a significant correlation (R\({}^{2}\) = 0.58) and the smallest bias (55 gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\)), RMSE (22%) and RE (21%). For Crop, none of the algorithms provided effective estimates, with the exception of the NCEP_GLASS GPP algorithm, which also exhibited a significant correlation (R\({}^{2}\) = 0.98) and the smallest bias (\(-645\) gC\(\cdot\) m\({}^{-2}\)\(\cdot\) year\({}^{-1}\)), RMSE (39%) and RE (37%). Regarding Grass, the NCEP_GLASS GPP algorithm outperformed the other algorithms due to its significant correlation (R\({}^{2}\) = 0.80) and smallest RMSE (29%) and RE (46%).