## 4 Results and Analysis
### Compare of Heat Fluxes Calculated Using Different LST Datasets
#### 4.3.2 Comparison of the DisTrad-LST and GWR-LST Heat Fluxes  
Based on Table 5, the MBE ranges of H and LE associated with the DisTrad-LST method and the GWR-LST method are \(-\)13.65 W\(\cdot\)m\({}^{-2}\)\(\sim\)6.99 W\(\cdot\)m\({}^{-2}\) and \(-\)7.23 W\(\cdot\)m\({}^{-2}\)\(\sim\)16.32 W\(\cdot\)m\({}^{-2}\), respectively. The GWR-LST heat fluxes are better than the DisTrad-LST heat fluxes on 19 June, 30 June, 13 September and 14 September, whereas the DisTrad-LST heat fluxes are superior to the GWR-LST heat fluxes on 8 July, 27 July, 3 August, 15 August, 22 August, 29 August and 2 September. The results obtained here are consistent with the results of the LST comparison because LST is the only difference between the two methods used to calculate the flux. To achieve an improved understanding of the spatial distribution of the differences between the three heat fluxes, we performed a scatterplot analysis by selecting 1000 \(\times\) 1000 subpixels on 19 June as an example. The results are shown in Figure 11. Comparing the downscaled LST heat fluxes with those obtained from the MODIS-LST, it can be seen that the bias of LE estimation inside the oasis, Gobi, and desert areas is relatively small (pixels with a relatively large bias are covered with a red triangle on the left side of Figure 11, and the corresponding spatial distributions are marked in red on the right side of Figure 11). In those areas, the surface structure is very similar, resulting in a more uniform temperature distribution. However, where substantial discontinuities in land cover type, surface structure, or soil moisture occur (dry and wet areas, as well as areas with dense and sparse vegetation, appear together in many situations, such as at the boundaries between the oasis, the desert and the Gobi areas), the errors could be significant because of discontinuities in the LE model and its parameters. This phenomenon can be noted in Figure 11, where the pixels marked in red are mainly located in the boundary areas. Thus, in complex areas, we cannot directly use the MODIS-LST to calculate the regional LE, because the surface temperatures of these regions are either ignored or exaggerated in the low-resolution MODIS mixed pixels; thus, we cannot use a single value instead of the subpixel values. Comparing Figure 11b,d, we find that the red points obtained with  
Figure 10: Distribution of classes and temperatures surrounding EC04, EC 15, and EC11, (**a**) DisTrad-LST (**b**) GWR-LST. the DisTrad-LST are much greater in number than those obtained with the GWR-LST (these points mainly indicate building areas within the oasis). The reason is that the regression relationship used in the DisTrad downscaling method is based on hierarchical pure pixels. Moreover, it assumes that the LST-NDVI relationship is stationary in space, but that is not the case [19; 22]. The DisTrad downscaling method also tends to overestimate the LST of subpixels within cooler landscapes and to underestimate the LST of subpixels for warmer areas [1]. Figure 11: Comparison of LE values obtained using the three methods. (**a**) Scatterplot between GWR-LST-LE and the MODIS-LST-LE (**b**) Spatial distributions are marked in red; (**c**) Scatterplot between DisTrad-LST-LE and the MODIS-LST-LE (**d**) Spatial distribution are marked in red; (**e**) Scatterplot between DisTrad-LST-LE and the GWR-LST-LE (**f**) Spatial distribution are marked in red. The differences between the GWR-LST LE and the DisTrad-LST LE occurred mainly at the boundaries between oasis and water areas and uncultivated land (see Figure 11f). To analyze these differences further, we calculated the differences between the two methods at each site. Table 7 shows the information for sites that display differences between the two methods that are greater than 40 W\(\cdot\)m\({}^{-2}\). Table 8 shows the site information for small differences between the two methods. The difference between GWR-LST LE and DisTrad-LST LE is \(-63.51\) W\(\cdot\)m\({}^{-2}\)-79.72 W\(\cdot\)m\({}^{-2}\).