## 3 Results  
### GPP Assessments on Seasonal Scale  
According to an eight-day interval GPP analysis shown in Figures 3 and 4, the seasonal changes of the modeled GPPs (GPP\({}_{\rm NDVI3g}\), GPP\({}_{\rm NDVI1g}\), GPP\({}_{\rm MOD15}\) and GPP\({}_{\rm MOD17}\)) were more correlative with the eddy covariance derived GPP (GPP\({}_{\rm OBS}\)) in the subtropical forest site (QYZ) than in the tropical forest site (PDF). The adjusted coefficient of determination (Adj. \(R^{2}\)) between the modeled GPPs and GPP\({}_{\rm OBS}\) were all above 0.8 (\(p<0.001\)) in the subtropical forest site (QYZ) (Figure 3); however, the four Adj. \(R^{2}\) in the tropical forest (PDF) were low and varied in the range 0.15-0.20 (Figure 4). The root-mean-square errors (RMSEs) between the modeled GPPs and GPP\({}_{\rm OBS}\) were 1.49 grams of carbon per square meter per day (gC m\({}^{-2}\) d\({}^{-1}\)) (GPP\({}_{\rm NDVI3g}\)), 1.97 gC m\({}^{-2}\) d\({}^{-1}\) (GPP\({}_{\rm NDVI1g}\)), 1.59 gC m\({}^{-2}\) d\({}^{-1}\) (GPP\({}_{\rm MOD15}\)) and 0.99 gC m\({}^{-2}\) d\({}^{-1}\) (GPP\({}_{\rm MOD17}\)), respectively, in the QYZ site. The corresponding values were 25.37 gC m\({}^{-2}\) d\({}^{-1}\), 16.27 gC m\({}^{-2}\) d\({}^{-1}\), 41.09 gC m\({}^{-2}\) d\({}^{-1}\) and 4.62 gC m\({}^{-2}\) d\({}^{-1}\) in the PDF site. In the PDF site, the GPP\({}_{\rm MOD17}\) and GPP\({}_{\rm OBS}\) values were closer (Figure 4) and GPP\({}_{\rm MOD17}\) had the lowest RMSE with GPP\({}_{\rm OBS}\) less than those of GPP\({}_{\rm NDVI3g}\), GPP\({}_{\rm NDVI1g}\), GPP\({}_{\rm MOD15}\). However, the seasonal variability of GPP\({}_{\rm MOD17}\) was not as consistent with GPP\({}_{\rm OBS}\) as those in GPP\({}_{\rm NDVI3g}\), GPP\({}_{\rm NDVI1g}\), and GPP\({}_{\rm MOD15}\). We also conducted a comparison between the modelled and reference GPPs by using the seasonal and annual sum GPP data (Table 2). At the subtropical forest site (QYZ), GPP\({}_{\rm NDVIEWg}\), GPP\({}_{\rm NDVI1g}\), and GPP\({}_{\rm MOD15}\) were underestimated by 21%, 35% and 21% for the whole year, by 8%, 23% and 11% for the period from May to October, and by 51%, 62% and 43% for the period from November to April (Table 2). However, at the tropical forest site (PDF), GPP\({}_{\rm NDVIEWg}\), GPP\({}_{\rm NDVI1g}\) and GPP\({}_{\rm MOD15}\) overestimated GPP by 36%, 27% and \(-\)30% for the whole year, 36%, 26% and \(-\)26% for May to October, 36%, 28% and \(-\)34% for November to April. These results indicate that the FPAR from GIMMS NDVI3g can capture GPP well in the growing season (May-October) in subtropical forest. MOD17A2 showed elegant performance at the two forest sites with the lower bias of \(-\)2.9% and \(-\)11.8% for the whole year, \(-\)6.1% and \(-\)10.1% for May to October, and 3.6% and \(-\)13.6% for November to April in the QYZ and PDF sites, respectively (Table 2). Even with the closer values, the MOD17A2 has an insignificant correlation with GPP\({}_{\rm OBS}\) (\(p\) = 0.815) in the PDF site (Figure 4). Figure 3: The eight-day mean GPP modeled by using the GLOPEM-CEVSA model and the FPARs from GIMMS NDVI3g (GPP\({}_{\rm NDVI3g}\), (**a**), (**b**)), GIMMS NDVI (GPP\({}_{\rm NDVI3g}\), (**c**), (**d**)) and MOD15A2 (GPP\({}_{\rm MOD15}\), (**e**), (**f**)) as well as the MODIS GPP products MOD17A2 (GPP\({}_{\rm MOD17}\), (**g**), (**h**)) compared with the GPP observed on the eddy covariance towers in the QYZ forest site. The seasonal variation ((a), (c), (e), (g)) and the scatter plots ((b), (d), (f), (h)) of the two kinds of GPPs are compared for all the four GPP products (GPP\({}_{\rm NDVI3g}\), GPP\({}_{\rm NDVI1g}\), GPP\({}_{\rm MOD15}\), and GPP\({}_{\rm MOD17}\)). Sample number is 138.