## 4 Results
### Changes in GPP and CI in Different Vegetation Types
#### 4.1.2 Sensitivity of GPP to CI of Different Vegetation Types  
CI presented similar seasonal variation characteristics in that its value was widely lower in the LOS than in the leaf-off season, but its uncertainty (measured by SD) should not be ignored (Figure 4). The highest CI values that occurred in the leaf-off season were approximately 0.72-0.78 for DBF, MF, and OSH, 0.63 for ENF, and 0.82-0.86 for CRO, GRA, WSA, and SAV. In the leaf-off season, the CI uncertainty quantified by SD was approximately 0.055-0.077 for CRO, DBF, GRA, OSH, and WSA, 0.02-0.03 for MF and  
Figure 3: Comparison of the eight-day change in GPP (red point line) observed at the EC site with GPP (deep green point line) estimated by the TL-LUE model for DBF (**a**): deciduous broadleaf forest, ENF (**b**): evergreen needleleaf forest, MF (**c**): mixed forest, OSH (**d**): open shrublands, CRO (**e**): croplands, GRA (**f**): grasslands, WSA (**g**): woody savannas, SAV (**h**): savannas during 2002–2020, respectively. There is an evident overestimation of GPP in the TL-LUE model, particularly in the growing peak, by approximately 5.70 for MF, 3.78 for OSH, and 2.37 g C m\({}^{-2}\) d\({}^{-1}\) for CRO. SAV, and 0.078 for ENF (Figure 4). In the leaf-off season, the lowest CI of 0.63 with a response uncertainty of 0.078 occurred in ENF, but the highest CI of 0.86 occurred in CRO (Figure 3). The constant \(\Omega\) that is currently used in the TL-LUE model was near the CI in the leaf-off season but significantly higher than that in the leaf-on season, which led to GPP overestimation in the leaf-on season. The CI decreased from the end of the leaf-off season until it was reduced to the lowest CI around the leaf-gathering season (Figure 4), and the lowest CI and its uncertainty represented by SD were 0.64 (0.084) for DBF, 0.53 (0.087) for ENF, 0.57 (0.084) for MF, 0.68 (0.066) for OSH, 0.76 (0.071) for CRO, 0.73 (0.074) for GRA, 0.72 (0.072) for WSA and 0.68 (0.056) for SAV (Figure 4). The large uncertainty of the CI, as measured by SD, led to a wide fluctuation of the eight-day GPP estimation in the leaf-on season. For the OYC, the greatest difference in the CI between the LFS and LOS ranged from approximately 0.068 to 0.12 in CRO, DBF, ENF, GRA, and WSA, and the uncertainty denoted by SD ranged from approximately 0.065 to 0.082 (Figure 4), which implied that the temporal variation features of the eight-day CI were likely disturbed by the large uncertainty measured by SD. Figure 4: Eight-day changes in the mean CI (black point line) for different vegetation types during 2002–2020. The gray solid vertical line represents the uncertainty of the CI, as measured by the standard  
deviation (SD), the black dashed line is the default of \(\Omega\) currently used in the TL-LUE model, and the black dots are eight-day CI mean in each vegetation type by averaging CI values from 2002 to 2020 in all sites extracted from the MODIS CI. This figure shows that although the eight-day average CIs capture general seasonal trends for some vegetation classes, some large uncertainties exist due to the changes in the background and ambient reflectivity, abnormal land surface shadows, insufficient BRDF information, and even some systematic vibrations that cannot be well understood in some classes, e.g., approximately from 0.016 to 0.161 for MF, from 0.032 to 0.132 for WSA, and from 0.048 to 0.093 for GRA. The total sensitivity of GPP to CI was quantified by the variance of the GPP estimation from the TL-LUE model caused by the changes in CI, and this sensitivity could be implemented by the SIMLAB software. There was an obvious sensitivity of the GPP estimated by the TL-LUE model to the CI (>0.7) (Figure 5). The total sensitivity of GPP to CI was 0.85\(-\)0.88 for DBF, MF, CRO, and OSH and 0.73\(-\)0.80 for WSA, SAV, GRA, and ENF (Figure 5), of which the largest sensitivity (0.88) was in DBF and the smallest was in WSA (0.73). The analysis of the sensitivity of GPP to the CI revealed that CI played an important role in the estimation of GPP from the TL-LUE model.