## 4 Discussion
### Estimation Performances of the SIF-Based and LUE-Based Models  
The performances of the three GPP models were highly consistent in their predictions of the magnitudes and seasonal dynamics of GPP, as validated using GPP observations (Figure 6). The performance of the SIF model was better than that of the SL-LUE model and was only slightly poorer than that of the TL-LUE model. Similar to this study, previous research (see Table 6) indicates that the models performed better at the daily timescale compared to the mid-day and half-hour timescales. Furthermore, the goodness of fit in the process-based two-leaf model provides the best simulation, and the GOME-2 SIF linear model is also better than the big-leaf LUE model used in the MOD17A2 product. Figure 7: Relationship between SIF yield (SIF/APAR, \(SIF_{\text{yield}}\)) and light-use efficiency (\(\varepsilon\)) at the satellite overpass time throughout 2015 at the CN-YuC cropland site in northern China, where 0.8 is the threshold value at which ECI values are fit separately for the mid-day, daily, and monthly timescales, respectively. The whole canopies are prone to experiencing exposure to excess light when the ECI is greater than 0.8 compared with when the ECI is lower. [PERSON] et al. [44] demonstrated that SIF is more relevant to the PAR absorbed by the photosynthetic parts of the maize canopy. That is, the components of PAR related with the SIF are mainly absorbed by vegetation chlorophyll, while the SL-LUE model in this study uses the PAR absorbed by the plant canopy (\(fPAR_{\text{canopy}}\)) to estimate GPP. However, vegetation photosynthesis and chlorophyll fluorescence can only be driven by the PAR absorbed by photosynthetic parts, not by non-photosynthetic parts, such as branches, senescent foliage, and stems. This may be the reason that the SIF model has a better performance than the SL-LUE model. Nonetheless, previous studies have indicated that remote sensing SIF retrievals tend to represent the top layer of the vegetation canopy, which is primarily composed of sunlit leaves, thereby causing deviation between the SIF-based simulation and the tower observations [35; 39]. Similar results were found when we analyzed the relationship between the SIF and LAI, the latter including the LAI of both sunlit and shaded leaves. The values of R\({}^{2}\) for the relationship between the SIF and the LAI of sunlit leaves were all greater than 0.85, while the values of R\({}^{2}\) ranged from 0.62 to 0.70 and from 0.53 to 0.65 for the relationships between SIF and the LAI of the whole canopy and between SIF and the LAI of shaded leaves across multiple temporal aggregation levels, respectively (Figure 8). Although the \(fPAR_{\text{canopy}}\) was also used in the TL-LUE model, it explicitly considers the difference in the LUE and APAR of sunlit and shaded leaves within the canopy. [PERSON] et al. [28] illustrated that considering the entire canopy as a big extended leaf and ignoring the difference in the LUE and APAR of leaves can cause biased values of the simulated GPP. Consequently, the TL-LUE model can reduce the bias in GPP simulated by the SL-LUE model and the SIF model. On the other hand, the LUE-based models depend on a set of biome-specific parameters (\(\varepsilon_{\text{max}}\), \(\varepsilon_{\text{msu}}\), and \(\varepsilon_{\text{msh}}\)) and also require different inputs, such as air temperature, PAR, and water or soil moisture scalars, while the SIF-based models are completely based on SIF information without requiring any other ancillary variables. In this case, using SIF to estimate GPP may save computational resources and reduce the uncertainty in GPP estimates across spatial and temporal scales compared with the LUE-based algorithm.