## 3 Results
### FY-4A LST Authenticity Test Using TCA  
Using TCA can overcome the problem created by the difficulty of obtaining the standard reference dataset of quantitative remote sensing parameters on the surface at a regional scale with high spatial and temporal resolution; the error indicators for comparisons between remote sensing products and the unknown ground truth are computed in the analysis [21]. The matched hourly data triplets formed in this study, the 2 y FY-4A-CLDAS-ERA5-Land dataset with a total of 65.3 million triplets of data that we created, and the sufficiency of the quantity of data ensure the generality of the study results. The results of the analysis showed that the correlation coefficient (Rho) between FY-4A LST and true surface temperature was greater in central-eastern, northern, and south-central Hunan Province and that areas with very high correlation coefficients were found mostly in the Hengyang basin in central Hunan. Rho decreased in the western and southern mountainous areas and the northern Dongting Lake area; values for the southern border mountainous areas and the northwestern border mountainous areas were significantly lower (Figure 4). Rho for Himawari-8 LST was greater than for FY-4A overall; the spatial distribution trended similarly. Analysis of the other error indicators (Figure 5) showed that the sensitivity of FY-4A LST to the true surface temperature was high relative to other remote sensing parameters [38]. The distribution of Sensitivity was similar to that of Rho, and it was greater in the central-eastern, south-central and northern parts of Hunan Province except for the Dongting Lake area and some areas in the west; Stderr, which indicates the degree of error, was between 3 and 4 for most grids and was greater in most areas in the west and the Dongting Lake area in the north and very high in the high elevation mountainous areas in the northwest. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factors** & **Classification** & **R** & **Bias** & \(\text{Bias}_{\text{r}}\) & **RMSE** & **ubRMSE** & \
\begin{tabular}{c} **Data** \\ **Quantity** \\ \end{tabular} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Land surface \\ temperature \\ (\({}^{\circ}\)C) \\ \end{tabular} } & \(-\)20–3 & 0.333 & \(-\)0.276 & 0.937 & 3.179 & 3.167 & 0.316 \(\times\) 10\({}^{5}\) \\  & 3–26 & 0.778 & \(-\)4.791 & 0.316 & 6.779 & 4.795 & 3.301 \(\times\) 10\({}^{5}\) \\  & 26–49 & 0.673 & \(-\)9.031 & 0.274 & 10.141 & 4.613 & 1.617 \(\times\) 10\({}^{5}\) \\  & 49–72 & 0.333 & \(-\)21.512 & 0.401 & 21.868 & 3.929 & 0.16 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & Spring & 0.849 & \(-\)6.446 & 0.306 & 8.630 & 5.737 & 0.972 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & Summer & 0.756 & \(-\)8.137 & 0.261 & 10.160 & 6.084 & 1.277 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & Autumn & 0.854 & \(-\)6.573 & 0.290 & 8.829 & 5.895 & 1.624 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & Winter & 0.819 & \(-\)4.354 & 0.444 & 6.637 & 5.009 & 1.521 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & 32–379 & 0.895 & \(-\)6.378 & 0.303 & 8.652 & 5.845 & 5.011 \(\times\) 10\({}^{5}\) \\ \cline{2-7}  & 379–726 & 0.874 & \(-\)6.348 & 0.344 & 8.182 & 5.163 & 0.233 \(\times\) 10\({}^{5}\) \\ (m) & 726–1073 & 0.848 & \(-\)5.954 & 0.362 & 8.297 & 5.779 & 0.052 \(\times\) 10\({}^{5}\) \\  & 1073–1420 & 0.847 & \(-\)2.125 & 0.150 & 6.051 & 5.665 & 0.099 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}  
* Data in red are the optimal parameters of each classification, and data in yellow are the worst parameters.  
\end{table} 
Table 3: Error indicators for comparison between FY-4A LST and in situ measurement of classifications of different factors. The FY-4A LST product showed stable deviation and weak signal interference. The value of fRMSE, which indicates variation in error, was greater in the central-western, northwestern, and southern border areas, and the greatest values were found mostly in the northwestern, southern, and southeastern borders.