## 5 Discussion
### Comparison with Prior Studies  
In recent years, significant progress has been made in the estimation of GPP on the Tibetan Plateau. [PERSON] et al. [51] analyzed six common remote sensing GPP products (GLASS, MODIS MOD17A2, FLUXCOM, VODCA2, an improved EC-LUE dataset, and VPM data) for the Tibetan Plateau. The results indicated that the MOD17A2 GPP was the lowest, at 484.04 Tg C-year\({}^{-1}\), while GLASS and VODCA2 produced the highest and most similar estimates, at 827.78 and 827.20 Tg C-year\({}^{-1}\), respectively. The improved EC-LUE, VPM, and FLUXCOM GPP estimates fell between these values, at 714.55, 634.00, and 587.86 Tg C-year\({}^{-1}\), respectively. [PERSON] et al. [52] reported that the annual GPP across the entire Plateau ranged from 575 to 703 Tg C-year\({}^{-1}\), showing a significant upward trend from 1982 to 2013. In this study, the GPP NIRv data indicated average annual GPP of 790.28 Tg C-year\({}^{-1}\) for the Tibetan Plateau from 1982 to 2018. This estimate lies between the highest values of GLASS and VODCA2 (827.78 and 827.20 Tg C-year\({}^{-1}\), respectively) and the improved EC-LUE (714.55 Tg C-year\({}^{-1}\)), suggesting that the data used in this study are reliable.  
Although significant progress has been made in global GPP prediction, there is a noticeable gap in studies specifically focused on the Tibetan Plateau. Most of the existing studies have concentrated on large-scale projections. For example, [PERSON] et al. [53] applied an ML approach to reduce the uncertainties in the GPP estimates derived from CMIP5, predicting that the global GPP could increase by 39% by the end of the 21 st century under the RCP 8.5 scenario. Similarly, [PERSON] et al. [30] analyzed the future global GPP trends using datasets from 18 ESMs in CMIP6, showing an overall increase in GPP across all four SSP scenarios, with the ssp585 scenario indicating the most substantial growth. In another study, [PERSON] et al. [31] projected that, by 2100, the annual global GPP could reach 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C under the four SSP scenarios. Furthermore, [PERSON] et al. [15] employed a deep learning approach to integrate GPP data from 23 CMIP6 models, producing a high-precision monthly GPP dataset with a spatial resolution of 0.25\({}^{\circ}\), spanning 1850 to 2100 across four climate scenarios.  
In contrast to global studies, research on GPP prediction on the Tibetan Plateau is relatively limited. This research gap underscores the critical need for region-specific  
Figure 11: The importance of environmental factors for the GPP simulation.  
studies, such as the current investigation, which focuses on the Tibetan Plateau. This study presents a comprehensive projection of the annual GPP on the Tibetan Plateau under four SSP scenarios. In 2021, the estimated GPP values were 1008.264 Tg C, 992.754 Tg C, 989.377 Tg C, and 998.812 Tg C under ssp126, ssp245, ssp370, and ssp585, respectively. By 2100, these values are projected to shift to 1011.986 Tg C, 1032.679 Tg C, 1044.358 Tg C, and 1055.504 Tg C, reflecting changes of 3.722 Tg C, 39.925 Tg C, 54.980 Tg C, and 56.691 Tg C, respectively, which correspond to percentage changes of 0.36%, 4.02%, 5.55%, and 5.67%. In comparison, [PERSON] et al. [31] indicated increases of 1.67%, 3.29%, 4.22%, and 6.05% for the same scenarios by the end of the century for the global GPP prediction. A comparison of the GPP projections for the global scale and the Tibetan Plateau reveals that the findings of this study are reasonable. These findings highlight the possible changes in vegetation productivity on the Tibetan Plateau under different climate scenarios, providing valuable insights for future ecological and environmental planning in this sensitive region.