## 3 Results  
### Direct Authenticity Test and Evaluation of FY-4A LST Using In Situ Measurement  
Comparative analysis of the FY-4A LST hourly data for Hunan Province matched with in situ measurements from 1 October 2019 0h to 30 September 2021 23 h shows (Table 2) that the FY-4A product captured changes in the surface temperature well (R = 0.893), but that it generally underestimated LST (Bias = \(-\)6.295 \({}^{\circ}\)C) and had some deviation from in situ measurement (RMSE = 8.58 \({}^{\circ}\)C; ubRMSE = 5.842 \({}^{\circ}\)C). FY-4A was better at capturing daytime surface temperature changes than nighttime changes but also had greater Bias; ubRMSE was significantly lower than RMSE, indicating that FY-4A was more affected by random errors in observations. The Himawari-8 LST product was better than FY-4A in terms of all parameters except that there was overall less data due to the coarser resolution of the product; the deviation in LST was clearly less, as shown by Bias and RMSE. When LST was low (<25 \({}^{\circ}\)C), the accuracy of FY-4A LST was better and stable. The center of density of its scatterplot was around the \(y=x\) line, but as the temperature increased, the deviation from observation gradually increased, and the underestimation of LST became greater (Figure 3), which may be one reason for its larger overall error (Table 2); there were also some outliers in the product. In contrast, the Himawari-8 LST product was overall more stable, with the center of density of the scatterplot close to the \(y=x\) line. This product also showed less deviation from direct observation than FY-4A at higher values of LST (>25 \({}^{\circ}\)C). Table 3 shows the values of indicators when FY-4A LST was compared with in situ measurement for equivalent classification of environmental factors. It can be seen that \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Product**} & \multicolumn{1}{c}{**R**} & \multicolumn{1}{c}{**Bias**} & \multicolumn{1}{c}{**Bias\({}_{\mathbf{r}}\)**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**ubRMSE**} & \multicolumn{1}{c}{**Data Quantity**} \\ \hline \multirow{3}{*}{FY-4A} & overall & 0.893 & \(-\)6.295 & 0.303 & 8.588 & 5.842 & 5.394 \(\times\) 10\({}^{5}\) \\  & daytime & 0.920 & \(-\)9.244 & 0.336 & 11.078 & 6.105 & 2.181 \(\times\) 10\({}^{5}\) \\  & nighttime & 0.864 & \(-\)4.294 & 0.265 & 6.365 & 4.699 & 3.213 \(\times\) 10\({}^{5}\) \\ \hline \multirow{3}{*}{Himawari-8} & overall & 0.926 & \(-\)4.933 & 0.238 & 7.508 & 5.660 & 5.191 \(\times\) 10\({}^{5}\) \\  & daytime & 0.923 & \(-\)7.413 & 0.247 & 9.606 & 6.110 & 2.368 \(\times\) 10\({}^{5}\) \\ \cline{1-1}  & nighttime & 0.849 & \(-\)2.854 & 0.219 & 5.124 & 4.256 & 2.823 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular} Daytime is the local meteorological daytime in Hunan Province, i.e., UTC [0, 12), and nighttime is UTC [12; 24).  
\end{table} 
Table 2: Error parameters for comparison between FY-4A LST and Himawari-8 LST tested against in situ measurements. Figure 3: Scatterplots of FY-4A LST and Himawari-8 LST vs. in situ measurement; the straight line in the figure is the \(y=x\) line; the brighter the color in the figure, the more concentrated the data points. FY-4A LST was worst (R, Bias, RMSE were all worst) when LST was highest (49-72 \({}^{\circ}\)C). When LST was low (\(-\)20-3 \({}^{\circ}\)C), FY-4A performed better according to several indicators (Bias, RMSE, ubRMSE were all optimal), but its ability to capture changes in land surface temperature was not good (R) in this temperature range; the better indicator values may be due to the lower values of LST in this temperature range, as can be seen from the maximum value of \(\text{Bias}_{\text{r}}\). The better performance of the FY-4A LST product in autumn and winter and the worse performance in summer may be related to mean LST (minimum \(\text{Bias}_{\text{r}}\) in summer).