## 4 Discussion
### Analysis of Results  
In this study, we utilized a multi-source remote sensing data fusion approach, GPP time series at a 16-day temporal resolution, and 30 m spatial resolution in northwestern Hubei from 2001 to 2020, obtained using the revised LUE model. Cross-validation with GOSIF GPP and MODIS GPP products achieved correlation coefficients of R\({}^{2}\) = 0.9150 and 0.8473, respectively, confirming the accuracy and reliability of the estimates. However, the RMSE was relatively high, especially for the GOSIF product, which is also widely indicated by many previous studies. This phenomenon can also be mainly attributed to the coarse spatial resolution of the product, as well as the machine learning-based SIF reconstruction and the linear conversion relationship from SIF to GPP [17; 49]. To the contrary, many studies have indicated that MODIS data tend to underestimate GPP due to the applied big-leaf model ignoring the contribution of shaded leaves, especially in regions with high GPP [50].  
This dataset supported the analysis of regional GPP variations, covering temporal changes, spatial distribution along elevation intervals, and future predictions using the Hurst exponent and Theil-Sen median slope methods. The results of the median slope analysis indicate that high-elevation areas with high GPP values generally exhibit an upward trend, while the plain regions with lower GPP values tend to show a downward trend. This pattern aligns with the fact that high-GPP areas are often located in ecological conservation zones with forest cover in high mountains, where government policies and restoration efforts have been implemented to protect the environment. In contrast, the low-GPP areas covered by cropland in plains are primarily urbanized or developed regions, where increased human activities and land use have contributed to a decline in vegetation productivity.  
Subsequently, the relationship between GPP and climatic factors are analyzed to understand the mechanism of GPP distribution and variation, through the computation of Pearson's correlation coefficients across multiple scales and elevation gradients. The results show that the GPP-climate correlations in northwestern Hubei exhibit notable seasonal heterogeneity, with the seasonal scale displaying stronger relationships than the 16-day or annual scales. This variability can be tied to the elevation-driven climate gradients, such as changes in temperature and precipitation, and the vertical zonation of vegetation, particularly the diverse forest ecosystems in the Shennongjia Forest District. Based on the study of intra-annual correlations, it can be inferred that higher summer temperatures enhance plant photosynthesis intensity, thereby increasing water demand and resulting in a higher correlation with precipitation. Conversely, during the winter months, low-precipitation correlation values are observed as most plants winter, shed leaves, or enter dormancy, exhibiting weak photosynthesis and consequently lower water demand. This leads to a reduced correlation with precipitation, reflecting the strong seasonal differentiation of precipitation correlation. Similarly, as temperatures rise, the temperature correlation with GPP increases. However, during the summer, temperatures may exceed the optimal range for photosynthesis, causing a decline in the temperature correlation. As summer ends and temperatures decrease to more suitable levels for photosynthesis, the temperature correlation rises again. In winter, as temperatures drop sharply, limiting plant photosynthesis, the temperature correlation decreases to a lower level. This highlights the strong seasonal differentiation of the temperature correlation as well.