## 4 Results
### Evaluation of GPP Estimated by the TL-CLUE Model  
#### 4.2.1 Verification of GPP Estimation against Sites  
The result of GPP estimated by the TL-CLUE model (Bias = 1.04, RMSE = 2.26 g C m\({}^{-2}\) d\({}^{-1}\)) was better than that (Bias = 1.31, RMSE = 2.53 g C m\({}^{-2}\) d\({}^{-1}\)) estimated by the TL-LUE model (Table 4). Compared to the RMSE of the TL-LUE model, the RMSE of the TL-CLUE model decreased by 6.75\(-\)15.25% in different vegetation types (Table 4), particularly in the WSA (15.17%). The Bias values of the TL-LUE model were 1.11\(\sim\)1.14 for DBF and OSH, 1.30 for CRO, ENF, MF, and SVA, and 0.75\(-\)0.91 g C m\({}^{-2}\) d\({}^{-1}\) for WSA and GRA (Table 4), indicating that GPP was generally overestimated by the TL-LUE model. Relatively, the Bias value of the TL-CLUE model was 0.80\(-\)0.96 for DBF and OSH, 0.65\(\sim\)1.95 for CRO, ENF, MF, and SVA, and  
Figure 5: The total sensitivity of the eight-day GPP to the CI in different vegetation types. This total sensitivity is more than 0.72 for various vegetation types in the figure, which indicates that changes in the CI would affect the magnitude of GPP to some extent. 0.56\(\sim\)0.65 g C m\({}^{-2}\) d\({}^{-1}\) for WSA and GRA (Table 4). These results illustrate that the uncertainty of GPP estimation in the TL-CLUE model was approximately 12% lower than this uncertainty in the TL-LUE model. The consistency of the observed GPP with the GPP estimated by the TL-CLUE model (a) and TL-LUE model (b) was shown in the leaf-on season (Figure 6a1,b1), leaf-scattering season (Figure 6a3,b3), leaf-gathering season (Figure 6a2,b2), leaf-off season (Figure 6a4,b4), and OYC (Figure 6a5,b5). Regardless of the period, the regression line of the TL-CLUE model was closer to the 1:1 theory line than the line of the TL-LUE model. The consistency between the estimated GPP and the observed GPP was best in the OYC (Figure 6a5,b5), followed by the LSS (Figure 6a3,b3), and it was worst in the leaf-off season (Figure 6a4,b4). The RMSE of the TL-CLUE model was lower than that of the TL-LUE model by 10.75, 11.9, 8, 1.08, and 10.55% in the leaf-on season, leaf-gathering season, leaf-scattering season, leaf-off season, and OYC, respectively. The uncertainty of GPP estimation from the TL-CLUE model was lower than the uncertainty of the TL-LUE model. The RMSE relying on the TL-CLUE model was reduced by approximately 12, 8, and 10.7% for the leaf-gathering season, leaf-scattering season, and leaf-on season, respectively (Figure 6). The difference in Bias between the TL-CLUE model and TL-LUE model was approximately \(-\)0.68, \(-\)0.26, and \(-\)0.49 g C m\({}^{-2}\) d\({}^{-1}\) for the leaf-gathering season, leaf-scattering season, and leaf-on season, respectively (Figure 6). The improvement of GPP estimation in the TL-CLUE model in the leaf-on season was higher than that in the leaf-gathering season, but lower than that in the leaf-scattering season. The error of GPP estimation from the TL-CLUE model in the OYC (Bias = 0.83 g C m\({}^{-2}\) d\({}^{-1}\)) was higher than that in the leaf-off season (Bias = \(-\)0.16 g C m\({}^{-2}\) d\({}^{-1}\)) but lower than the error in the leaf-on season (Bias = 1.37 g C m\({}^{-2}\) d\({}^{-1}\)), revealing that the uncertainty of GPP estimation in the whole year was mainly due to that in the leaf-on season. The Bias difference between the TL-CLUE model and TL-LUE model was 0.49 and 0.31 g C m\({}^{-2}\) d\({}^{-1}\) in the leaf-on season and OYC, respectively, which indicated that the TL-CLUE model reduced the error of GPP estimations. Overall, the TL-CLUE model improved the accuracy of GPP estimation to some extent, particularly in the leaf-on season, which is critical for the improvement of GPP estimation on the annual scale.