# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 3 Results
### Validation of the Spatiotemporal Method
Overall, the performance when using Aqua data was better than that when using Terra data. This can be attributed to the fact that, during the day, the temperature reaches its highest point at around 2 p.m., which is the transit time of the Aqua satellite. As the temperature increases, the air density and humidity decrease. The factors that weaken solar radiation, such as water vapor and aerosol in the air, will be reduced. This is particularly evident in arid inland areas far from the sea. In addition, we also used AMSR2 data to verify the reconstruction performance of the STARFM algorithm. AMSR2 data is carried on the GCOM-W1 satellite, and its transit time is close to that of Aqua. Therefore, we only validated the data for the afternoon. Figure 6 shows the results of this. On the basis of the results, the performance of the three sites (Arou, Dashalong, and Yakou) with high altitudes was still relatively poor. However, compared with each site, the performance when using AMSR2 data was relatively better than that when using ERA5 data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Satellite**} & \multicolumn{6}{c}{**Sites**} \\ \cline{3-6}  & & **Statistics** & **Arou** & **Daman** & **Dashalong** & **Huazhaizi** \\ \hline \multirow{8}{*}{**Tera**} & \multirow{8}{*}{**Tera**} & R\({}^{2}\) & 0.68 & 0.79 & 0.12 & 0.74 \\  & & RMSE & 12.24 & 11.52 & 48.38 & 16.2 \\  & & NSE & 0.08 & 0.2 & \(-17.19\) & \(-0.43\) \\  & & PBIAS & \(-3.33\%\) & \(-3.33\%\) & \(-2.56\%\) & \(-4.91\%\) \\ \cline{2-6}  & \multicolumn{6}{c}{**Sites**} \\ \cline{3-6}  & & **Statistics** & **Hunhelin** & **Sidaoqiao** & **Yakou** & **Zhangye** & **Overall** \\ \cline{2-6}  & & R\({}^{2}\) & 0.9 & 0.93 & 0.71 & 0.87 & 0.4 \\  & & RMSE & 15.2 & 11.41 & 14.59 & 10.51 & 21.28 \\  & & NSE & 0.16 & 0.5 & \(-0.9\) & 0.32 & \(-1.1\) \\  & & PBIAS & \(-4.84\%\) & \(-3.71\%\) & \(-4.73\%\) & \(-3.30\%\) & \(-3.83\%\) \\ \cline{2-6}  & \multicolumn{6}{c}{**Sites**} \\ \cline{3-6}  & \multicolumn{6}{c}{**Sites**} \\ \cline{3-6}  & **Satellite** & **Arou** & **Daman** & **Dashalong** & **Huazhaizi** \\ \hline \multirow{8}{*}{**Aqua**} & R\({}^{2}\) & 0.53 & 0.74 & 0.55 & 0.8 & \\  & RMSE & 8.34 & 7.81 & 9.14 & 7.67 & \\ \cline{1-1}  & NSE & 0.42 & 0.6 & 0.36 & 0.76 & \\ \cline{1-1}  & PBIAS & \(-0.04\%\) & \(-0.85\%\) & \(-1.27\%\) & \(-0.98\%\) & \\ \cline{1-1} \cline{2-6}  & \multicolumn{6}{c}{**Sites**} \\ \cline{1-1} \cline{2-6}  & **Statistics** & **Hunhelin** & **Sidaoqiao** & **Yakou** & **Zhangye** & **Overall** \\ \cline{1-1} \cline{2-6}  & R\({}^{2}\) & 0.91 & 0.91 & 0.64 & 0.84 & 0.79 \\ \cline{1-1}  & RMSE & 10.37 & 6.34 & 8.83 & 7.74 & 8.34 \\ \cline{1-1}  & NSE & 0.61 & 0.82 & 0.3 & 0.55 & 0.68 \\ \cline{1-1}  & PBIAS & \(-2.95\%\) & \(-1.28\%\) & \(-2.14\%\) & \(-1.83\%\) & \(-1.40\%\) \\ \hline \end{tabular}
\end{table} 
Table 4: The general performance results of reconstructed LST based on ERA5 data using STARFM from 2016 to 2019. Figure 5: Comparison of reconstructed LST using STARFM with LST measured by AWS at 8 sites from 2016 to 2019. in situ (n) indicates the measured LST with cloudy skies at the observation station; in situ (c) indicates the measured LST with clear skies at the observation station. Figure 6: Validation of LST fused by STARFM based on AMSR2 from 2016 to 2019.