## 3 Results  
### Validation of Global LUE and GPP Results in 2014  
We extrapolated the calibrated parameters and models to a global scale and produced LUE and GPP maps for 2014 with a spatial resolution of 5 km and a temporal resolution of 8 days. In this part, we validated the results against FLUXNET measurements. There were 1700 pieces of data observed in 69 sites within 2014. Figure 4 indicates that our LUE and GPP estimates are reliable. The R\({}^{2}\) of LUE ranged from 0.21 to 0.30, and the RMSE ranged from 0.41 to 0.55 gC/MJ. The best result was produced by the parameterization approach with the CI, while the least satisfactory one was estimated by the Cubist regression tree model. There were more overestimated LUE values in the Cubist regression approach, some of which reached or even exceeded 3 gC/MJ. Although the Cubist regression tree approach gained the optimum result during calibration, it revealed clear disadvantages in extrapolating to the global scale. Like other empirical methods, the main weakness of Cubist is the vague explanations behind the formulas and the great possibilities to exceed reasonable values when dealing with some rare and special situations. However, it is most likely to obtain pixels that are unsuitable for the estimated relationships when mapping global LUE. Compared with LUE, the accuracy of GPP was improved, with the R\({}^{2}\) ranging from 0.51 to 0.60 and the RMSE ranging from 2.42 to 2.87 gC/m\({}^{2}\)/d. Similarly, the parameterization approach with the CI gained the most satisfactory GPP. Although the  
Figure 3: The accuracy of 3 approaches in each type of vegetation. V1: Parameterization approach without the CI; V2: Parameterization approach with the CI; V3: Cubist regression tree approach. Figure 2: The validation results of 3 approaches. Approach V1: Parameterization approach without the clearness index (CI); Approach V2: Parameterization approach with the CI; Approach V3: Cubist regression tree approach; MODIS: MODIS 17 algorithm. Cubist regression tree approach still produced the weakest relationship between estimated GPP and FLUXNET GPP, the overestimation was mitigated because of the constraint of PAR. Then, we looked at more details in each type of vegetation (Table 3). In DBF, DNF, GRA, MF and WSA, vegetation gained high-quality LUE with R\({}^{2}\) values greater than 0.3. In EBF, ENF, OSH and SAV, vegetation obtained acceptable accuracy with R\({}^{2}\) values from 0.17 to 0.26. All of the above types of vegetation had RMSE values less than 0.5 gC/MJ. However, all three approaches failed to produce reliable LUE values in CRO, with an R\({}^{2}\) less than 0.03 and an RMSE higher than 0.57 gC/MJ. Crop species in CRO were complicated and might contain C3 plants, C4 plants or both. The different features between C3 and C4 crops introduced errors in CRO. In comparison with the parameterization approach without the CI, the parameterization approach with the CI showed clear advantages in all types of vegetation in terms of calculating LUE. In comparison with the Cubist regression tree approach, the parameterization approach with the CI clearly won in CRO, DBF, ENF, GRA, MF, OSH, and SAV and was slightly superior in DNF and obviously inferior in EBF and WSA. Similar to the general situation, the accuracy of GPP was improved in each type of vegetation compared with LUE. The relationship between estimated GPP and FLUXNET GPP was strong, with the R\({}^{2}\) values were greater than 0.5 in DBF, DNF, EBF, ENF, GRA, MF, SAV and WSA. However, GPP gained only acceptable accuracy, with R\({}^{2}\) values of 0.46 and 0.31 and RMSE values of 2.73 gC/m\({}^{2}\)/d and 0.86 gC/m\({}^{2}\)/d in CRO and OSH, respectively. The main errors in OSH were induced by the heterogeneity of plants. The error resources of cropland vegetation will be discussed in Section 4.