## 4 Results
### Site-Scale Model Comparisons  
While the six tested models are shown to have divergent spatiotemporal patterns of derived AAGPP, model evaluation against the eddy covariance flux tower data shows that the accuracies of different models are all high for studies on the 8-day basis (Figure 4). Even for the MOD17A2H product, which has the lowest modeled accuracy among the tested models, the R\({}^{2}\) value obtained is as high as 0.744 and the RMSE value is as low as 2.201 gC/m\({}^{2}\)/day. The GPD model shows the best model performance on the 8-day basis (R\({}^{2}\) = 0.885 and RMSE = 1.478 gC/m\({}^{2}\)/day) and the TL-LUE (R\({}^{2}\) = 0.868, RMSE = 1.650 gC/m\({}^{2}\)/day), VPM (R\({}^{2}\) = 0.860, RMSE = 1.873 gC/m\({}^{2}\)/day), and BESS (R\({}^{2}\) = 0.844, RMSE = 1.880 gC/m\({}^{2}\)/day) models have similar model performance. Both TL-LUE (bias = 0.456 gC/m\({}^{2}\)/day) and BESS (bias = 0.619 gC/m\({}^{2}\)/day) have positive biases, while the VPM  
Figure 3: The spatial distributions of 12-year (2001–2012) (**a**) average leaf area index as derived from the MODIS data; and (**b**) average shortwave solar radiation, (**c**) average air temperature, and (**d**) average vapor pressure deficit as derived from the Daymet data at 1000 m spatial resolution. model (bias = \(-\)0.864 gC/m\({}^{2}\)/day) shows negative bias. The MOD17A2H (bias = 0.043 gC/m\({}^{2}\)/day) and GPD (bias = \(-\)0.073 gC/m\({}^{2}\)/day) models have small biases as evaluated against the flux tower data. All models greatly underestimated the 8-day GPP for the US-Ha1 site. However, on the yearly basis, the modeled annual GPP are far from satisfactory when validated against the flux tower data (Figure 5). Among the tested methods, the best-performing model of TL-LUE only achieved R\({}^{2}\) values as low as 0.373 and the RMSE values as high as 278.8 gC/m\({}^{2}\)/year. Both GPD (R\({}^{2}\) = 0.279, RMSE = 245.6 gC/m\({}^{2}\)/year) and BESS (R\({}^{2}\) = 0.224, RMSE = 382.7 gC/m\({}^{2}\)/year) perform slightly better than the remaining models. The EC-MOD data as derived based on a data-driven approach, however, is leaps and bounds from the flux tower data on the yearly basis, despite the fact that its spatial distribution of AAGPP appears to be reasonable in Figure 1.  
To understand the model uncertainties introduced by different climate data, three models (i.e., the TL-LUE, VPM, and GPD models) were also implemented with the meteorological data measured at flux towers. The modeled 8-day GPP as driven using flux tower data (Figure 6) performs similarly to that driven by the Daymet data (Figure 4), suggesting that the mismatch in meteorological data between flux tower measurements and the Daymet data is not the decisive factor. The modeled yearly GPP can vary largely using different climate input data for the TL-LUE and VPM models (Figures 5 and 6), indicating that both light-use-efficiency-based models are sensitive to the climate inputs on the yearly basis. The GPD model produces consistent results when modeling using the flux tower climate data and the Daymet data. Figure 4: Comparisons between the GPP measured at flux towers and the GPP derived from (**a**) MOD17A2H; (**b**) TL-LUE; (**c**) VPM; (**d**) GPD; and (**e**) BESS on an 8-day basis. Data were extracted for the pixel that contains the corresponding flux tower site from the 1000 m datasets. Because only yearly EC-MOD data were available, the EC-MOD data were not validated on the 8-day basis. Figure 5: Comparisons between the GPP measured at flux towers and the GPP derived from (**a**) MOD17A2H; (**b**) TL-LUE; (**c**) VPM; (**d**) GPD; (**e**) BESS; and (**f**) EC-MOD on a yearly basis. Data were extracted for the pixel that contains the corresponding flux tower site from the 1000 m datasets.