## 5 Conclusions  
In this study, the effects of temporal aggregation and meteorological conditions on the parameter robustness of OCO-2 SIF-based and LUE-based GPP models, as well as the models' estimation performances were examined and compared, respectively, at a maize-wheat rotation site in Yucheng, China. The main findings are as follows:  
(1) The values of \(\varepsilon/SIF_{\text{yield}}\) in the SIF model and those of \(\varepsilon_{\text{max}}\) in the SL-LUE model are similarly robust and both are considerably more stable than those of \(\varepsilon_{\text{msu}}\) and \(\varepsilon_{\text{msh}}\) in the TL-LUE model across all temporal aggregation levels. Under different meteorological conditions, all the parameters show a certain degree of fluctuation and are most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale overall. Nonetheless, the averaged coefficient of variation of the \(\varepsilon/SIF_{\text{yield}}\) was relatively small and obviously lower than those of \(\varepsilon_{\text{max}}\), \(\varepsilon_{\text{msu}}\), and \(\varepsilon_{\text{msh}}\). Therefore, compared with the LUE-based models, for different temporal aggregation levels and environmental restrictions in cropland, the SIF-based model is a good predictor of GPP and its parameter is more likely to converge. Moreover, the SIF models were completely based on SIF and did not contain any ancillary inputs, such as air temperature, PAR, and VPD. This study thus strongly supports the continuous effort to take advantage of SIF for improved monitoring of GPP.  
(2) It is feasible to combine the OCO-2 SIF data of the Nadir observation mode with those of the Glint observation mode after fully considering the viewing zenith effects of the latter on the magnitude of SIF values and on the relationship between SIF and GPP. This enables the limitation of sparse data to be alleviated and better exploration of the link between the ground-observed GPP and remote-sensing-based SIF retrievals at the canopy scale to be achieved. Additionally, the OCO-2 SIF data are found to be more strongly correlated with the LAI of sunlit leaves. It indicates that SIF observations may be useful to constrain the maximum photosynthetic rates of sunlit leaves in the two-leaf LUE model, thus reducing the uncertainty of parameter estimates and improving the accuracy of GPP prediction in future studies.  
All authors contributed extensively to the work presented in this paper. [PERSON] designed the research. [PERSON] completed the data preparation, processing, analysis, and writing of the manuscript. [PERSON] approved the study and supervised the whole research work with valuable inputs especially in the methodology, analysis, and reviewing. [PERSON] and [PERSON] assisted in data processing and the evaluation of the results. [PERSON] provided critical review and comments. [PERSON], [PERSON], and [PERSON] contributed in evaluating and revising the manuscript. This research was funded by National Key R&D Program of China [Grant # 2018 YFA0606001, 2017 YFA0604301 & 2017 YFA0604302]; the research grant [O88 RA901 YA] funded by the State Key Laboratory of Resources and Environment Information System; and the research grants [41771114 & 41271116] funded by the National Natural Science Foundation of China. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.