## 4 Results
### Comparison with Other Global GPP Products  
In this study, we used FLUXNET GPP data accumulated over three years to evaluate the daily and yearly simulation accuracy of different global GPP products. Figure 10 showed comparisons between FLUXNET GPP and various GPP products at 8-day time scales. Among all products, the NI-LUE GPP generated in this study had a good performance (R\({}^{2}\) = 0.65, RMSE = 2.43 gC/m\({}^{2}\)/d, MAE = 1.73 gC/m\({}^{2}\)/d, IOA = 0.89). MuSyQ, VPM, GOSIF, and FLUXCOM GPP products also have high accuracy, with R\({}^{2}\) exceeding 0.6. FLUXCOM GPP had the highest R\({}^{2}\) of all products, reaching 0.67. The MOD17 GPP product performed the worst, with an R\({}^{2}\) of only 0.55 and a significant underestimation. It was worth noting that, compared with other products, the linearly fitted line of NI-LUE GPP was closer to the 1:1 line, and the underestimation phenomenon was significantly weakened (slope = 0.72). Figure 11 further showed the comparison of simulation accuracy of various GPP products for different vegetation types. Compared with different GPP products, the performances of NI-GPP improved in GPP simulations of CRO, DBF, EBF, and GRA. For SAV, WET, and WSA, although GOSIF GPP performed the best among all products, NI-LUE still had an advantage in all products based on LUE models. For ENF, MF, and OSH, the performance of NI-GPP was not outstanding, while MuSyQ GPP, FLUXCOM GPP, and GOSIF GPP performed better for these vegetation types, respectively. Moreover, the annual GPP simulations of two process-based biophysical models in TRENDY, LPJ-GUESS, and SDGVM, were added to the comparison. As shown in Figure 12, the annual GPP simulations of LPJ-GUESS and SDGVM had poor performance, showing significant underestimation. Compared to the verification of daily GPP estimates, the accuracy of other products improved. Among them, the NI-LUE GPP produced in this study still had a good performance in the annual GPP verification, and the annual GPP of rEC-LUE, MuSyQ, GOSIF, and FLUXCOM also performed well, with an R\({}^{2}\) exceeding 0.7. Across all products, the regression line between the annual gross GPP of NI-LUE, MuSyQ, and GOSIF and FLUXNET GPP is closer to the 1:1 line, with a slope exceeding 0.75. Figure 13a showed the inter-annual variations in the annual total GPP of different products. There were great differences in the global annual total GPP of different products, ranging from 104.48 Pg C to 137.28 Pg C. Among all products, the global annual total GPP of the five LUE models ranged from 108.79 Pg C to 129.70 Pg C. The annual total GPP simulations of two process-based biophysical models in TRENDY differed considerably, with LPJ-GUESS GPP of 106.62 Pg C and SDGVM GPP of 133.65 Pg C. However, these two products had similar trends, and they can reflect the fluctuations around 2009 as well as the NI-LUE GPP produced in this study. For data-driven GPP products, the global annual total GPP of GOSIF GPP was the highest (137.28 Pg C), while that of FLUXCOM GPP was  
Figure 9: Spatial pattern of the uncertainty in global GPP estimated by the NI-LUE model. the lowest (104.48 Pg C). The global annual total NI-LUE GPP ranged from 125.09 Pg C to 133.75 Pg C from 2001 to 2018, placing at the middle of the various GPP products, which is in a reasonable range based on statistics for other products. Figure 13b showed the correlation coefficient matrix of the interannual variations of different products. The study showed that the interannual variation of NI-LUE GPP was significantly positively correlated with GOSIF GPP, FLUXCOM GPP, VPM GPP, LPI-GUESS GPP, and SDGVM GPP, and the correlation coefficients with MOD17 GPP and MuSyQ GPP were only 0.36 and 0.33, respectively. However, since rEC-LUE GPP showed a decreasing trend from 2001 to 2018, it was negatively correlated with most of the GPP products. To demonstrate that the spatial pattern of NI-LUE GPP products was reasonable, we separately calculated the SPAEF between the mean annual NI-LUE GPP and different GPP products.