## 3 Results  
### GPP Validation  
#### 3.1.1 Eight-Day GPP  
The eight-day Flux_Tower GPP were compared with the results of MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP (Figure 1, Table 2). In general, the MOD_Tower GPP algorithm (R2 = 0.52) did not result in obvious improvements in fitting the Flux_Tower GPP when comparing with the MOD_GMAO GPP algorithm (R2 = 0.52). However, the GMAO_GLASS GPP algorithm (R2 = 0.63) and the Tower_GLASS GPP algorithm (R2 = 0.65) made considerable improvements, and the Tower_GLASS GPP algorithm exhibited the best estimating effectiveness. It should be noted that all of the above four algorithms underestimated the Flux_Tower GPP to varying degrees. The degrees of underestimation by the MOD_GMAO GPP (\(-11\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)), MOD_Tower GPP (\(-13\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)) and Tower_GLASS GPP (\(-11\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)) algorithms were larger than that by the GMAO_GLASS GPP algorithm (\(-8\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)). Figure 1: Scatter plots of the eight-day MOD_GMAO GPP vs. the Flux_Tower GPP (**a**), MOD_Tower GPP vs. the Flux_Tower GPP (**b**), GMAO_GLASS GPP vs. the Flux_Tower GPP (**c**) and Tower_GLASS GPP vs. the Flux_Tower GPP (**d**). Significance levels: \({}^{**}p<0.01\). GMAO, Global Modeling and Assimilation Office. From the perspective of individual sites, the four algorithms (MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP) underestimated the Flux_Tower GPP more obviously (Figure 2). The Flux_Tower GPPs were underestimated by above four algorithms at 15 of the 18 sites. For the remaining three sites (AU-ASM, AU-Whr and US-Wkg), GMAO_GLASS GPP and the Tower_GLASS GPP algorithms still underestimated the site-based GPP. In detail, AR-Vir, BR-Sa3 and PA-SPs suffered the highest degree of underestimation by the four algorithms (Figure 2a,g,p, respectively), and CA-Gro, CZ-BK2 and FR-Pue suffered the lowest degree of underestimation (Figure 2h,l,m, respectively). With respect to the estimating effectiveness of the four algorithms, the MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP algorithms fitted relatively effectively for one site (US-Wkg), two sites (AU-Rig and AU-Whr), ten sites (AU-ASM, BE-Lon, BE-Vie, CA-Gro, CZ-BK2, DK-Sor, FR-Gri, FR-Pue, IT-Ren and PA-SPs) and four sites (AR-Vir, DE-Hai, GH-Ank and US-CRT), respectively. None of the four algorithms successfully fitted the Flux_Tower GPP for BR-Sa3 (Figure 2g). The GMAO_GLASS GPP algorithm had a relative advantage in terms of bias (\(-14\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)). The eight-day GPP values of MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP were also compared with the Flux_Tower GPP at different biome types (Table 2). As the analyses of individual sites showed, all above algorithms underestimated the Flux_Tower GPP for all six biome types. In detail, deciduous broadleaf forests (DBF) suffered the highest degree of underestimation, mainly because the four algorithms seriously underestimated the Flux_Tower GPP of DE-Hai (Figure 2j) and DK-Sor (Figure 2k). In contrast, the degree of underestimation was low for grasslands (GRA). This could be largely attributed to the fact that the four algorithms effectively estimated the Flux_Tower GPP of CZ-BK2 (Figure 2i) and US-Wkg (Figure 2r). With respect to the estimating effectiveness of the four algorithms, the GMAO_GLASS GPP and Tower_GLASS GPP