## 4 Results and Analysis
### Accuracy Assessment of the LST Downscaling Results
Therefore, the method of joining albedo and DEM used in GWR is better than the DisTrad method. However, as the vegetation grows (see Figure 6 where the mean FVC starts to increase), NDVI plays an increasingly important role. NDVI explains over 53.67% of the LSTs on 8 July, 27 July, 3 August, 15 August, 22 August and 29 August, whereas the total contribution from albedo and DEM is only 15.20% to 46.33%. Thus, on these days, the downscaled LSTs produced by the DisTrad method are more accurate. Afterwards, while the mature crops (in our study area, maize is the main crop) gradually wither, the albedo and DEM explain over 66.61% of the LSTs. Therefore, the accuracy of the GWR downscaled LSTs is better at this time. As seen in Figure 6, the percentage contribution of albedo is higher on 3 August and 22 August (i.e., 26.03% and 26.89%, respectively) because irrigation occurred on these two days. Irrigation increases soil moisture and cools the land surface. Because albedo is mostly negatively related with soil moisture within barren agricultural areas [19], it displays a larger influence over the results. To further compare the downscaling results of these two methods for different objects, the downscaling results were verified separately according to the dominant surface type at each site. Here, we use the stations SSW and GB to validate the downscaling results for bare land; station EC04 was used to validate the downscaling results for buildings; station SD was used to validate the downscaling results for swampland; and the other stations were used to validate the downscaling results for vegetated areas. The assessments for the different land cover types are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Land Cover**} & \multicolumn{4}{c}{**GWR**} & \multicolumn{4}{c}{**DisTrad**} \\ \cline{2-7}  & \(R^{2}\) & **RMSE (K)** & **MBE (K)** & \(R^{2}\) & **RMSE (K)** & **MBE (K)** \\ \hline Swampland & 0.81 & 2.62 & \(-\)0.08 & 0.80 & 2.70 & 0.77 \\ Buildings & 0.85 & 1.91 & 0.08 & 0.78 & 2.43 & \(-\)0.56 \\ Bare land & 0.88 & 1.95 & 0.06 & 0.87 & 2.11 & \(-\)0.26 \\ Vegetated areas & 0.84 & 2.05 & 1.44 & 0.86 & 2.05 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical measures of downscaling result of different land cover. Figure 6: Percentage contribution of different independent variables. Overall, the results of the DisTrad downscaling method for areas covered with swampland, buildings and bare land are inferior to those of GWR, whereas the results in the vegetated areas are the opposite. The reason may be that NDVI is most suitable for vegetation monitoring within areas with moderate vegetation coverage ([PERSON] and [PERSON], 2017), and it is considered unsuitable for studying areas with low vegetation coverage, such as urban build-up areas, where the detection capacity is only 30% ([PERSON] and [PERSON], 2017). The DisTrad downscaling results represent overestimates in areas covered with swampland (MBE \(>\) 0), whereas they are underestimates (MBE \(<\) 0) in areas covered by buildings and bare land. On the other hand, GWR displays higher accuracy (\(\,\)1 MBE \(|\) \(<\) 0.1 K) in both regions. Two possible reasons can explain this phenomenon. First, swampland contain water bodies. However, the DisTrad downscaling model eliminates water bodies when constructing the regression function, and the presence of the secondary term can cause considerable interference in the downscaling results for areas covered by water, buildings and bare land ([PERSON], 2017). Furthermore, the GWR-based algorithm includes a spatially nonstationary relationship between LST and the other land surface parameters (NDVI, albedo and DEM) that is based on geographically varying regression coefficients ([PERSON] and [PERSON], 2017).