## 5 Discussion
### Limitations and Future Prospects of the Study  
Although the products used in this study are relatively accurate, errors may still arise due to variations in methods and models, which could affect the final results. Despite conducting independent analyses for each grid point within the Qinba Mountain area forests, issues such as coarse spatial resolution, environmental changes affecting the GPP-SIF linear relationship, and imperfect parameter fitting in the remote sensing ecosystem respiration model remain challenges. This study spans a long temporal period; however, the forest area data used remain static, potentially failing to capture forest dynamics and leading to an underestimation of carbon losses in harvested areas. To improve accuracy, future research should incorporate annual land cover change data for more precise carbon sinks assessments. The temporal mismatch between forest age and type data (from 2020 and 2021) and carbon sinks estimation data (from 2011 to 2018) may introduce uncertainties into the analysis and potentially affect the accuracy of the conclusions, despite the relative stability of land use patterns during the study period. Due to data availability constraints, the current data combination represents a practical and necessary compromise. However, this limitation underscores the importance of improving data alignment in future research. To enhance the precision and reliability of carbon sinks assessments, it is recommended that future studies prioritize the acquisition of forest structural data--such as forest age and type--that are temporally and spatially consistent with the study period. Forest carbon sinks in the Qinba Mountain region were estimated using SIF remote sensing technology. While comparisons were made with other remote sensing methods, the absence of ground-measured carbon sinks data--due to data limitations--precluded direct validation of SIF-based estimates against field measurements, introducing uncertainties in carbon sinks quantification. Additionally, the lack of ground-based GPP and NEP data further complicates the estimation of carbon sinks, thereby increasing the uncertainty in carbon sinks calculations. To address these limitations, future research should establish long-term ground observation plots in the Qinba Mountains to supplement the lack of field data. This would not only allow for more accurate validation of remote sensing estimates but also provide essential ground truth data to refine and improve the reliability of carbon sinks assessments. The ecosystem respiration model employed in this study is based on a temperature-dependent formulation but does not account for the critical influence of soil moisture in regulating respiration processes. Previous studies have demonstrated that soil moisture significantly affects ecosystem respiration [71]. The current model is a simplification of ecosystem respiration. Incorporating this variable in future models would improve simulation accuracy and enhance process representation. Although this study examined the relationships between forest carbon sinks and forest type, age, and elevation in the Qinba Mountains, carbon sinks dynamics are also influenced by various factors such as climate and soil conditions. Relying solely on forest type, age, and elevation provides an incomplete understanding of carbon sequestration patterns. Future research should integrate multisource data, including climatic and soil information, to improve the explanatory power of carbon sinks models and refine predictive accuracy. Despite these limitations, the approach used in this study holds significant promise for estimating forest carbon sinks at regional and global scales due to its large-scale spatial representativeness. The models employed are simple, direct, and clear in their mechanisms, and they do not require auxiliary meteorological data or vegetation index data [43].