## 4 Discussion
The ensemble mean GPP from our eight sets of GPP estimates could reduce the uncertainty to a certain extent. The uncertainty of the GPP estimates could also result from the uncertainty in our gridded SIF estimates. The prediction of SIF was affected by several factors, such as explanatory variables used in the predictive model, training sample size or uncertainty of the gridded input data products [31], and the errors associated with these factors could be propagated to the GPP estimates. For the GPP estimates based on the grid cell level relationships between SIF and GPP, the scale mismatch between the tower footprint and the 0.05\({}^{\circ}\) grid cell could affect the developed SIF-GPP relationships and consequently introduce uncertainty to the GPP estimates. The ensemble GPP estimates resulting from the different SIF-GPP relationships can partly account for these uncertainties, and the ensemble mean is likely more suitable for examining GPP globally than a single GPP product. Capturing the interannual variability well is still a grand challenge for both non-process and process-based methods in carbon cycle studies. Our approach also underestimated the interannual variability in GPP. The establishment of our SIF-GPP relationships is based on the short record of OCO-2 SIF which is likely to be insufficient to account for the interannual variability of GPP. The availability of a longer fine-resolution SIF record in the future is expected to improve GPP estimates and better capture the interannual variability of GPP. In addition, we did not separate C\({}_{3}\) and C\({}_{4}\) for croplands and grasslands due to a limited number of samples at the site level. Several previous studies found that the GPP-SIF relationship had a higher slope for C\({}_{4}\) ecosystems than for C\({}_{3}\) ecosystems [17; 25; 48]. In our site-based analysis, the observed much higher slope of C\({}_{4}\) species was mainly contributed by one corn site [17]. Future work should explore whether and how the differentiation of C\({}_{3}\) and C\({}_{4}\) ecosystems could influence the accuracy of the resulting GPP estimates for croplands and grasslands. The overall spatial pattern of annual GPP found in our study was consistent with GPP estimates based on satellite-based production efficiency models [49], process-based models [50; 51], and data-driven methods [5; 52]. The pattern of maximum daily GPP throughout a year is generally similar to that of the maximum daily productivity based on GPP estimates [53] or directly inferred from coarse-resolution SIF data [33; 54]. Our estimate of global annual GPP (\(135.5\pm 8.8\) Pg C yr\({}^{-1}\)) is between the 75 th percentile and 95 th percentile of the estimates from non-process based methods and higher than the estimate (\(\sim\)110 Pg C yr\({}^{-1}\)) from the MODIS GPP product [49] and the estimates from data-driven methods (119 - 123 Pg C yr\({}^{-1}\)) [5; 52]. The estimates of global GPP from process-based models varied from 98 to 177 Pg C yr\({}^{-1}\)[6; 7], and our estimate is slightly lower that the 50 th percentile of these estimates. A variety of factors could lead to large biases in global GPP estimates, such as imperfect model structures, parameter uncertainty, and the errors of input variables (e.g., climate forcing, land cover map) [55]. Our global GPP estimate is between the mean estimate of the non-process based methods and process-based models. Our GOSIF GPP product is useful for benchmarking state-of-the-art terrestrial biosphere/Earth system models. These models exhibit substantial uncertainty in GPP estimates due to various sources of uncertainty. It is challenging to evaluate these models partly because of the lack of reference data at regional to global scales. Our SIF-derived GPP product provides a new and independent dataset that can be used to benchmark terrestrial biosphere models and ESMs.