## 4 Discussion
### The Limitations of the Model and Future Improvements  
While this study successfully demonstrates a method for SIF downscaling, we acknowledge several limitations that offer clear directions for future research. These limitations primarily concern the resolution of the input data, uncertainties from the downscaling methodology, and the source of data used for modeling and validation.  
First, the accuracy of our final product was fundamentally constrained by the low spatiotemporal resolution of the GOME-2 SIF data. While coarse initial data limits achievable detail, long-term records like GOME-2 are invaluable for historical trend analysis. Previous studies have conducted downscaling processing by integrating SIF data from various sources [9]. One promising direction for future research is to combine the long-term observational records of GOME-2 with high-resolution data like TROPOMI. This approach would not only enhance the spatial resolution of the downscaled product but also generate a consistent, long-term, and verifiable SIF dataset, which is crucial for climate change and long-term ecosystem studies.  
Secondly, machine learning methods often overlap local spatial attributes when analyzing spatial data. Although our explanatory variables provide geographic information,  
Figure 9: Analysis of SHAP values of model parameters and the effect of land use parameters on training effectiveness. (**a**) The SHAP values of each input parameter ranked by their importance. (**b**) RMSE changes for model training with land use parameters included. (**c**) Model RMSE variation without land use parameters.  
this can lead to the smoothing of fine-scale details. Future work should focus on optimizing the downscaling algorithm to better capture spatial heterogeneity. For instance, a hybrid model combining Geographically Weighted Regression (GWR) with machine learning could be highly effective [53]. GWR accounts for spatial non-stationarity, while machine learning can model complex nonlinear relationships, allowing the combined model to better address both spatial dependence and heterogeneity.  
Finally, the model's robustness and validation were limited by its reliance on a single auxiliary data source (MODIS) and the lack of ground-based validation [54]. To build a more robust model, future iterations could integrate multi-source auxiliary data, such as radar-based satellite products that are less sensitive to cloud cover [55], or dynamic variables like seasonal Leaf Area Index (LAI) to better differentiate land cover types. Crucially, validating the downscaled SIF products against direct, ground-based SIF measurements is the most critical next step. The expansion of global ground-based SIF observation networks will be invaluable not only for direct validation but also for data assimilation approaches that merge \"ground truth\" with satellite observations to produce a new generation of high-resolution, high-accuracy SIF products [56]. In addition, a key application of long-term SIF products is to monitor vegetation responses to extreme climate events such as drought. As a direct probe of photosynthesis, SIF is theoretically highly sensitive to drought stress. Recent studies have demonstrated this potential. For example, [PERSON] et al. effectively monitored the impact of drought on agricultural systems using SIF time series data [57]. [PERSON] et al. also highlighted the value of integrating SIF data in rapid drought warning systems [58]. The high temporal resolution and long-term characteristics of the SIF products we have generated provide a solid foundation for capturing such stress signals. Although a comprehensive analysis of specific historical drought events is beyond the scope of this study, it will be a key direction for future research. Our subsequent work will focus on validating the performance of this product during well-documented drought periods to comprehensively assess its capabilities in monitoring extreme events.