## 3 Results
### Analysis of the Mechanism of the Influence of Environmental Factors on the Performance of FY-4A/LST  
In this study, environmental factor parameters and remote sensing LST error parameters were combined for analysis of the mechanisms through which environmental factors influence FY-4A LST data. The results of the analysis confirmed the results in Sections 3.1 and 3.2 that described the performance of FY-4A LST products as being strongly influenced by topographic factors. The accuracy of remote sensing LST products varied significantly for different mean DEM classifications (Figure 6). Most indicators showed that the accuracy of remote sensing LST products gradually decreased as elevation increased (Rho, Sensitivity, Snr_db gradually decreased; RMSE gradually increased). Only the parameter Stderr, which exists as a filtering process in the algorithm (Equation (9)), showed a trend of initially increasing and then decreasing as elevation increased, which indicated that the error of FY-4A LST was larger at medium elevations (436-1269 m). Rho for Himawari-8 LST was greater than for FY-4A LST, but the change trend was consistent between the two. Thus, the accuracy of FY4A LST gradually decreased, and the distortion of the detection signal gradually increased as elevation increased. The results of elevation analysis using TCA differed from the results based on in situ measurement (Table 3). In the latter, the maximum station elevation category was also worst in capturing changes in LST but had the least deviation (as indicated by Bias, Biasr, and RMSE). However, we think the results of TCA in our study were more dependable for the following two reasons. 1. There were only two in situ measurement stations in the highest elevation category, so the amount of data provided was limited. 2. The least values of Bias and RMSE may be related to the lowest LST value range at high elevation; Stderr in TCA for areas above 436 m shows a similar trend to Bias and RMSE, and Biasr shows a gradual increase as elevation increases in all categories except the highest elevation category (Table 3 and Figure 6). The performance of FY-4A LST varies with land cover. FY-4A data were more accurate for Cultivated land and Artificial surfaces and less accurate for Forests and Water bodies. Variation was greater for Forests and Wetlands (Figure 7). The accuracy of FY-4A LST gradually decreased as land cover information entropy increased, indicating that as land cover became more inhomogeneous, FY-4A LST decreased in accuracy. The trend of variation in accuracy for Himawari-8 LST with land cover was generally similar to that of FY-4A LST, but Himawari-8 was significantly more accurate when the surface water content was greater (accuracy was relatively higher for Water body and there was less variation for Wetland). Landform, slope, soil moisture, and NDVI all affected the accuracy of FY-4A LST (Figure 8). For landform, the accuracy of FY-4A LST was best for Platform, followed by Hill and Plain, and it decreased for Mountain as relief increased, which is consistent with the trend of accuracy decreasing as slope increased. For soil moisture, the accuracy of FY-4A LST increased as surface soil moisture increased. For NDVI, the trend of variation in FY-4A LST was more complex. When NDVI was low (\(<\)0.6), FY-4A LST was uniformly accurate; however, when NDVI was high (\(>\)0.6), the accuracy of FY-4A LST decreased sharply as NDVI increased. This behavior indicates that lush vegetation on the ground surface has a greater influence on FY-4A LST accuracy than other factors. Figure 6: Error indicators of TCA for different mean DEM classifications; (**A**): Rho vs. DEM for LST of FY-4A and Himawari-8; (**B**–**E**) are Sensitivity, Stderr, fRMSE, and Snr_db vs. DEM for FY-4A LST; the number of FY-4A grids in each mean DEM classification was 7094, 2845, 542 and 98.