## 4 Discussion
### Shortcomings of the Work  
Although this study has yielded several insights into the spatiotemporal dynamics of GPP in northwestern Hubei, there are still many issue that need further enhancement. In terms of data sources, the current analysis relied primarily on MODIS and Landsat datasets to achieve a balance between temporal frequency and spatial resolution. Integrating additional satellite observations, such as those from the Sentinel constellation, which offer higher spatial resolution (up to 10 m) and improved temporal revisit frequency, could further enhance the precision of NDVI and GPP estimates. During the multi-source remote sensing data fusion process, incorporating sensors with complementary spectral characteristics or cloud-penetrating capabilities may improve the accuracy and temporal continuity of the fused products. Nonetheless, it is important to note that multi-source fusion is inherently subject to certain uncertainties and potential instabilities, arising from sensor discrepancies, differences in radiometric calibration, and preprocessing protocols. These factors may introduce noise or artifacts, particularly in heterogeneous landscapessuch as mountainous regions. Expanding and refining the range of input datasets may help to mitigate these challenges by improving robustness and ensuring more consistent spatiotemporal coverage across the complex topography of northwestern Hubei [51]. Regarding the model, in this study, we utilized a big-leaf CASA LUE model, which offers computational simplicity but assumes uniform canopy properties. However, model parameters play a crucial role in accurately estimating GPP. Due to the lack of observational data, parameter calibration in this study remains limited [52]. Future work may require the acquisition of more accurate and site-specific parameters to improve model reliability. Additionally, more advanced modeling approaches could be explored, such as two-leaf LUE models that differentiate between sunlit and shaded leaves, or process-based models that incorporate detailed physiological and environmental interactions. These models could enhance the precision of GPP estimation, particularly in heterogeneous landscapes such as northwestern Hubei, where vegetation types vary significantly with elevation. Finally, in terms of the analysis, in this study, we focused on Pearson correlation coefficients to examine the GPP-climate relationships. One limitation of this approach is that, due to resampling, multiple pixels may share identical climate variable values, which can reduce the credibility of the correlation results. This issue highlights a shortcoming of the current study and should be addressed in future research to ensure more robust conclusions. Similarly, while the Theil-Sen median slope estimator and Mann-Kendall test are widely used for detecting monotonic trends due to their robustness against outliers and non-normal data, they also present limitations. Specifically, these methods are primarily designed to detect linear or monotonic trends and may fail to capture nonlinear or seasonal patterns in GPP time series. Moreover, the Mann-Kendall test can be sensitive to heteroscedasticity and autocorrelation, potentially affecting the reliability of significance testing. In addition, the use of the Hurst exponent to analyze the persistence or long-term memory of GPP time series is also limited, as it cannot fully capture short-term fluctuations or nonlinear dependencies. To overcome these limitations, future research could incorporate nonlinear trend detection techniques, as well as methods that explicitly account for temporal autocorrelation. In addition, methods such as causal inference or structural equation modeling could be employed to better distinguish correlation from causation. Incorporating other influential factors--such as soil moisture or land use changes, which are relevant to the region's ecological context--could further broaden the scope of the analysis. These improvements would help capture a wider range of drivers affecting GPP dynamics.