## 4 Results  
### Annual Variation Analysis of Model Accuracy at Different Sites  
By comparing and analyzing the fused surface temperature (FM-LST) data from 2017 to 2019 with the in situ measured surface temperature (Site LST) at each station, the impact of different surface types on model accuracy can be observed, as shown in Figures 5-7. The data for each year exhibit some fluctuation, but overall, the model successfully captures the seasonal variation trend of surface temperature.  
In the 2019 data, the Sidaoqiao station exhibited the best prediction accuracy (R\({}^{2}\) = 0.89), indicating that the model performs with high reliability in predicting surface temperature in this region. This result is likely related to the region's geographical features and relatively uniform land cover types, such as poplar forests, which provide favorable environmental conditions, allowing the model to predict surface temperature more accurately.  
For some stations with lower prediction accuracy, such as the Dashalong station, the 2019 data showed an R\({}^{2}\) of 0.67, indicating relatively lower accuracy. The Dashalong station is located in an alpine meadow, where surface types may be influenced by elevation heterogeneity and seasonal variations in cover, potentially affecting the model's accuracy. Additionally, changes in cloud cover may also impact the quality of thermal infrared data, further influencing the prediction accuracy of FMLST.  
Figure 5: Scatter plot comparison of LST reconstructed using the RFRR machine learning model in 2017 with LST measured by AWS at 8 sites. FM-LST represents the fused LST predicted by the model; Site-LST represents the LST measured by the observation station.  
Figure 6: Scatter plot comparison of LST reconstructed using the RFRR machine learning model in 2018 with LST measured by AWS at 8 sites. FM-LST represents the fused LST predicted by the model; Site-LST represents the LST measured by the observation station.  
Figure 7: Scatter plot comparison of LST reconstructed using the RFRR machine learning model in 2019 with LST measured by AWS at 8 sites. FM-LST represents the fused LST predicted by the model; Site-LST represents the LST measured by the observation station.  
When comparing data from the three years, it was found that the R\({}^{2}\) values for most stations remained above 0.70, indicating that the model was consistently able to capture the trend of surface temperature changes. Although some stations, such as Huazhaizi, showed slightly lower R\({}^{2}\) values in certain years, this phenomenon may be related to the unique topography and climate conditions of the desert area where the station is located. In this environment, surface temperature is influenced by multiple factors, such as wind erosion, and significant variations in surface moisture, which may lead to substantial discrepancies between the measured values and the model predictions.  
The experimental results show that the model can effectively predict surface temperature under various surface conditions. Although there is some fluctuation in prediction accuracy at certain sites with complex topography, the model overall successfully captures the dynamic trend of surface temperature changes over time. This demonstrates the significant potential of machine learning methods in the analysis of surface temperature remote sensing data and provides a feasible solution for accurate remote sensing monitoring under complex cloud cover conditions.