## 3 Results  
### GPP Validation  
#### 3.1.1 Eight-Day GPP  
The tower-based GPP measurements (Flux_Tower GPP) were compared with the results of the four experimental groups (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP) over eight days (Figure 2). Overall, when compared with the MOD_NCEP GPP algorithm (R\({}^{2}\) = 0.55), the remaining three algorithms, particularly the NCEP_GLASS GPP (R\({}^{2}\) = 0.65) and Tower_GLASS GPP (R\({}^{2}\) = 0.66) algorithms, were more effective at estimating the Flux_Tower GPP.  
From the perspective of a single site, in CBS, QYZ and YC, all four algorithms (MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP) underestimated the Flux_Tower GPP to varying degrees (Figure 3, Table 2). The MOD_Tower GPP were the most underestimated (\(-13\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for CBS, \(-18\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for QYZ and \(-22\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for YC), and the NCEP_GLASS GPP were the least underestimated (\(-1\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for CBS, \(-2\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for QYZ and \(-14\) gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\) for YC). However, for NMG, all four algorithms overestimated the Flux_Tower GPP. The MOD_NCEP GPP were the most overestimated(3 gC\(\cdot\) m\({}^{-2}\cdot\)8-day\({}^{-1}\)), and the NCEP_GLASS GPP and Tower_GLASS GPP were the least overestimated (1 gC\(\cdot\) m\({}^{-2}\cdot\)8-day\({}^{-1}\)). For the other sites, some algorithms overestimated the Flux_Tower GPP, whereas some underestimated them. For CBS, QYZ and DHS, the Tower_GLASS GPP algorithm most effectively estimated the Flux_Tower GPP (R\({}^{2}\) = 0.92, R\({}^{2}\) = 0.87 and R\({}^{2}\) = 0.75, respectively), and the greatest improvements were obtained over the MOD_NCEP GPP algorithm (R\({}^{2}\) = 0.76, R\({}^{2}\) = 0.52 and R\({}^{2}\) = 0.30) in particular. However, for YC and NMG, the Tower_GLASS GPP algorithm was not as effective as the other three algorithms for estimating the Flux_Tower GPP. In the case of YC, the performance of the Tower_GLASS GPP algorithm (R\({}^{2}\) = 0.77) was close to that of the most effective algorithm (MOD_NCEP GPP, R\({}^{2}\) = 0.81). However, for NMG, the gap between the Tower_GLASS GPP (R\({}^{2}\) = 0.64) and the best estimating algorithm (MOD_Tower GPP, R\({}^{2}\) = 0.75) was large. For HB and DX, all four algorithms effectively estimated the Flux_Tower GPP. In particular, the MOD_NCEP GPP (R\({}^{2}\) = 0.95, Bias = 0 gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\)) and MOD_Tower GPP (R\({}^{2}\) = 0.89, Bias = 0 gC\(\cdot\) m\({}^{-2}\cdot\) 8-day\({}^{-1}\)) algorithms performed best for HB and DX, respectively. Regarding XSBN, however, no algorithm succeeded in estimating the Flux_Tower GPP.  
Figure 2: (**a**) Scatter plots of the eight-day MOD_NCEP GPP _vs._ the Flux_Tower GPP; (**b**) MOD_Tower GPP _vs._ the Flux_Tower GPP; (**c**) NCEP_GLASS GPP _vs._ the Flux_Tower GPP; and (**d**) Tower_GLASS GPP _vs._ the Flux_Tower GPP. Significance levels: \({}^{**}p<0.01\).  
Figure 3: Time series of eight-day GPP derived from the tower estimates (_i.e._, Flux_Tower GPP) and four different experimental groups (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP and Tower_GLASS GPP) at: (**a**) CBS forest site; (**b**) QYZ forest site; (**c**) DHS forest site; (**d**) XSBN forest site; (**e**) YC cropland site; (**f**) HB grassland site; (**g**) NMG grassland site; and (**h**) DX grassland site. The full name for each site is listed in Table 1.