## 3 Results
### Satellite-Based GPP Estimations  
Six satellite-based GPP estimations (MOD5, MOD, MODR, VPM, PCM and AVM) in this study showed similar seasonal tendencies of GPP_EC but with different quantities in all growing seasons (Figure 5a). Indeed, all these modeled GPP showed an extremely significant (\(p<0.0001\)) linear relationship with EC measurements. This indicates that temporal variation of GPP could be well explained by these satellite-based GPP results (Figure 5b-d). However, different models generally had different performance in accuracy of GPP estimations. For example, MODR had slope (\(k\) value in Figure 5b-d) beyond 1.0 in all years, which indicates that MODR overestimated GPP_EC by 18% to 23%. On the contrary, GPP_EC was generally undervalued by the other satellite-based models. In contrast, MOD and VPM not only had the slope most closely approximating to 1.0, from 0.85 to 0.97 for VPM and from 0.83 to 0.92 for MOD, but the best representative of GPP_EC (\(R^{2}>0.9\), \(p<0.0001\)) (Figure 5). Figure 4: Seasonal variations of land surface vegetation and water indexes (**a**), and the photosynthetically active radiation (PAR) and the fraction of PAR absorbed by vegetation canopy (FPAR). (**b**) FPAR is directly obtained from MODIS products (MOD15A2H), while FPARg is calculated from ground measured leaf area index (LAI) based on the Beer–Lambert law. Daily average GPP_EC in GS were 0.94, 1.09 and 1.09 g C m\({}^{-2}\) day\({}^{-1}\) from 2009 to 2011 in this alpine meadow, respectively. MOD5 and PCM strongly underestimated this daily GPP_EC (39.2% and 28.2% of _RPE_, respectively) with large _RMSE_ values of more than 0.5 g C m\({}^{-2}\) day\({}^{-1}\) (Table 2). Although the rest of the satellite-based GPP models failed to generate significant deviations for GPP estimation compared to GPP_EC (\(p>\) 0.05), they either slightly overestimated (only MODR with 15.9 of RPE), or underestimated, including MOD, AVM, and VPM, the GPP_EC (Figure 5 and Table 2). Specifically, AVM had a 14.5% underestimation for GPP_EC with a _RMSE_ of 0.37 g C m\({}^{-2}\) day\({}^{-1}\). MOD and VPM further improved the accuracy of GPP estimation by 5.5% of RPE and 11.7% of RPE compared to AVM, which caused smaller RPEs less than 10% with the lowest average _RMSE_ values of 0.31 and 0.32 g C m\({}^{-2}\) day\({}^{-1}\), respectively (Table 2). Annual cumulative GPP_EC were 158.09, 183.95 and 183.51 g C m\({}^{-2}\) from 2009 to 2011, which were significantly underestimated by MOD5 and PCM with more than 50 g C m\({}^{-2}\) of RMSE (Table 2). On the contrary, MODR had a significant (\(p<\) 0.05) overestimation for annual cumulative GPP_EC with mean _RMSE_ of 28.43 g C m\({}^{-2}\). AVM could be treated as an alternative method to evaluate annual GPP because of the insignificant differences compared to annual GPP_EC, actually, the magnitude of _RMSE_ (25.86 g C m\({}^{-2}\)) was larger than MOD (17.43 g C m\({}^{-2}\)) and VPM (10.81 g C m\({}^{-2}\)) (Table 2). Therefore, VPM and MOD presented superior performances in daily and annual GPP estimations compared to other satellite-based GPP models. Figure 5: Seasonal variations of gross primary production (GPP) estimations from different methods (**a**); and comparison with the EC-based GPP estimations from: 2009 (**b**); 2010 (**c**); and 2011 (**d**), \(k\) values and dashed lines in (**b–d**) are the linear slope between GPP_EC and modeled GPP, and the reference lines of 1:1, respectively. All linear regressions are extremely significant (\(p<\) 0.0001). Colors in (**b–d**) are one-to-one corresponding with different GPP estimations in (**a**), respectively.