## 4 Results
### Spatial Layout and Difference Comparison Between FMLST and MODIS-LST  
In this study, we employed an innovative method to generate fused surface temperature (LST) data, which relies solely on observations from the Fengyun-3C satellite (FY-3C) without using MODIS LST data. By carefully selecting days with minimal cloud cover each month for LST image generation and validation, we ensured that the annual density scatter plots included a large number of valid comparison points, providing sufficient data support for an error analysis. Despite this strategy, the original MODIS images still exhibited significant cloud contamination and blank areas, which contrast sharply with the completeness of the fused images, as shown in Figures 10-12.  
Figure 9: Validation density scatter plot of site-observed LST compared with three types of LST—MWRIX LST, VIRR LST, and FM LST from 2017 to 2019.  
In the comparison of the three consecutive years from 2017 to 2019, the FM-LST images demonstrated excellent spatial continuity and completeness, particularly during the spring and summer seasons when the surface was covered by clouds. In these conditions, the FM-LST images provided a more coherent and detailed distribution of surface temperature, which could not be achieved in the MODIS images due to data gaps caused by cloud cover.  
For the autumn months with fewer clouds, both the FM-LST and MODIS images displayed richer and more consistent surface temperature information. However, the  
Figure 11: Spatial layout comparison of 2018 (**a**) MODIS and (**b**) FMLST.  
Figure 12: Spatial layout comparison of 2019 (**a**) MODIS and (**b**) FMLST.  
Figure 10: Spatial layout comparison of 2017 (**a**) MODIS and (**b**) FMLST.  
FM-LST images exhibited smoother temperature transitions and fewer data anomalies in some details, reflecting the advantages of the fusion algorithm in handling boundaries and temperature gradients.  
Further analysis, as shown in Figure 13, clearly demonstrates the high correlation between the FM-LST and MODIS LST data in the annual density scatter plots, which is particularly significant in the large sample datasets (1,666,037 valid points in 2017, 1,652,949 in 2018, and 1,578,885 in 2019). Statistical error parameters, particularly the coefficient of determination (\(\tau^{2}\) = 0.88) for the 2019 data, reveal a strong positive correlation between the two data sources.  
Although the RMSE for 2019 showed a slight increase, this may be due to systematic errors introduced by cloud cover or other environmental factors. Furthermore, the areas with the highest density in the scatter plots, typically represented by the brightest colors, closely align with the best fit line. This further emphasizes that, in these large sample datasets, the consistency between the fused data and the MODIS data is higher than the overall trend.  
These results not only demonstrate that our fusion technology can provide continuous and consistent data in cloud-covered areas, but also highlight its potential to generate high-precision surface temperature information under various meteorological conditions. The large number of valid data points provides a solid foundation for our error analysis, enhancing the credibility and persuasiveness of the experimental results.  
Figure 13: The 2017–2019 Annual Density Scatter Plots for MODIS and FMLST.