## 5 Discuss  
To address the issue of data loss in FY-3C VIRR data due to cloud cover, this study developed an LST-filling method based on a Random Forest (RF) and Ridge Regression (RR) algorithm stacking model, which relies entirely on Fengyun FY-3C data. This method combines FY-3C MWRI data and FY-3C VIRR's NVI data, using Singular Spectrum Analysis (SSA) to fill missing orbital data and performing time reconstruction with Savitzky-Golay filtering to obtain continuous, seamless NDVI data. Further, the PMW LST data from FY-3C MWRI and the NDVI data are used to downscale the surface temperature, resulting in higher-resolution 1 km LST data. The stacked Random Forest and Ridge Regression machine learning algorithms are employed to model the relationships between various environmental variables and surface temperature. These environmental variables include continuous, seamless NDVI, land cover (LC), longitude, latitude, digital elevation model (DEM), and slope. By optimizing the model's hyperparameters and applying stacking (Model Stacking), Elastic Net Regression was selected as the final regression model. The data for different land cover types were integrated to train the regression model. After model training and testing, the data for the target date are used to generate a 1 km surface temperature product (FM-LST) under all-weather conditions, ultimately producing a daily continuous, seamless 1 km surface temperature product.  
In this study, daily noon time series data from 2017 to 2019 were used to build 36 RFRR stacking models (12 \(\times\) 3) on a monthly basis. In the presence of missing data, 1062 FM-LST products were generated (350 + 352 + 360). To analyze the feature importance of the RFRR stacking model from 2017 to 2019, a SHAP value analysis was conducted, as shown in Figure 14. The results indicated that Dow_LST is the dominant feature in the model, particularly in 2018 and 2019. The SHAP value of Dow_LST was significantly higher than the other variables, and its positive impact showed a yearly increasing trend. This result aligns with the key role of Dow_LST in the microwave data downscaling product, further validating its importance for the accuracy of surface temperature prediction under cloud cover conditions.  
Figure 14: SHAP-based feature importance analysis of the RFRR model from 2017 to 2019.  
Specifically, the average SHAP value of Dow_LST in 2019 was around 10, significantly higher than 5 in 2017 and 8 in 2018, indicating that its influence on the model increased year by year. The impact of the DEM and NDVI remained relatively stable, with average SHAP values fluctuating between three and five. In contrast, geographically related features--latitude and longitude--contributed less to the model. For the NDVI, the average SHAP value in 2017 was around four, dropping to about two in 2018, which may reflect changes in climate conditions or vegetation growth patterns that year. By 2019, the influence of the NDVI had slightly increased. The DEM showed relatively consistent influence over the analysis period.  
Overall, Dow_LST made a significant contribution to the model, especially in addressing cloud cover issues. Moreover, the findings highlight the importance of integrating topographical, vegetation, and geographical information for surface temperature prediction.