## 6 Conclusions  
In this paper, by using remote sensing data from multiple sources (i.e., MODIS and HJ-1B) and combining the advantages of different data types in terms of their spatial and temporal resolution, we enhance the accuracy of the estimated LE. The DisTrad downscaling method and the GWR-based downscaling algorithm have been shown to be effective in obtaining the surface temperatures within \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **EC-LE** & **LE Obtained Using the** & **LE-I** & **LE Obtained Using the** & **LE-C Relative** \\  & **(W-m\({}^{-2}\))** & **Incorrect Canopy** & **Relative** & **Correct Canopy Height** & **Error (\%)** \\  & **Height (W-m\({}^{-2}\))** & **Error (\%)** & **(W-m\({}^{-2}\))** & **Error (\%)** \\ \hline
8 July & 567.64 & 626.41 & 10.35 & 554.08 & \(-\)2.39 \\
27 July & 541.33 & 614.41 & 13.50 & 529.03 & \(-\)2.27 \\
15 August & 499.62 & 587.00 & 17.49 & 538.24 & 7.73 \\
2 September & 377.96 & 514.52 & 36.13 & 449.05 & 18.81 \\ \hline \end{tabular} Notes: The correct orchard canopy heights are obtained from the actual measurement.  
\end{table} 
Table 10: The results of the canopy height error analyses at EC17. MODIS subpixels. However, the two methods have different advantages that depend on the descriptive ability of the independent variables at different times. Compared to the turbulent heat fluxes calculated using different LST datasets, the direct use of MODIS LST approach does not consider LST heterogeneity at all, which causes significant errors in the latent heat flux (i.e., the mean bias error is \(-59.78\) W\(\cdot\)m\({}^{-2}\)), especially in the boundary areas and for mixed pixels. The errors obtained using DisTrad-LST LE and GWR-LST LE were \(-23.17\) W\(\cdot\)m\({}^{-2}\) and \(-26.52\) W\(\cdot\)m\({}^{-2}\), respectively, and are less than that of MODIS LST based on the footprint validation results. However, this error is non-negligible. Additionally, it also shows that both LST downscaling methods can decreases the thermodynamic uncertainty caused by the land surface. As we discussed, although there are a lot of uncertainties in this paper, the differences between the turbulence heat fluxes calculated using two downscaling methods is only LST. Compared to DisTrad-LST LE and GWR-LST LE, the differences between the two methods occur in areas where the pattern of the surface types within the flux contribution source area is complex. The SHDI value can reflect the landscape heterogeneity, but in actual applications, the specific type of the underlying surface must be considered to distinguish between the advantages and disadvantages of the two methods. That is, when the SHDI value is high (>0.74) and the footprint area contains buildings or bare land and water, the GWR-LST LE outperforms the DisTrad-LST LE. On the other hand, when the SHDI value is between 0.59-0.75 and the footprint area contains three or fewer different types of vegetation, DisTrad-LST LE is better. When the SHDI value is low (<0.2) and the underlying surface of the footprint area contains a single type of vegetation, the difference between the two methods is less than 10 W\(\cdot\)m\({}^{-2}\). As a sensitive variable of the ET model, canopy height is mainly determined by classification in this paper, and the application of classification at a 30 m resolution can improve the accuracy of the canopy height. The HJ-1B satellite data have considerable advantages because of their high spatial resolution, and the MODIS data have fine temporal resolution. Because both satellites are still in operation and the data can be freely accessed, the long-term data are promising for applications involving the monitoring of energy budgets using a combination of data from the two satellites. We are grateful to [PERSON] and [PERSON] for providing the footprint calculation tool. In addition, we thank all of the scientists and engineers who took part in the HiWATER experiment. This study was jointly supported by the Chinese Natural Science Foundation Project (grant No.