## 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
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Station** & **Date** & \begin{tabular}{l} **EC-LST** \\ **(K)** \\ \end{tabular} & \begin{tabular}{l} **GWR-LST** \\ **(K)** \\ \end{tabular} & \begin{tabular}{l} **DisTrad-LST** \\ **(K)** \\ \end{tabular} & \begin{tabular}{l} **EC-LE** \\ **(W-m\({}^{-2}\))** \\ \end{tabular} & \begin{tabular}{l} **GWR-LST** \\ **(W-m\({}^{-3}\))** \\ \end{tabular} & \
\begin{tabular}{l} **Better** \\ **(W-m\({}^{-3}\))** \\ \end{tabular} &
\begin{tabular}{l} **Difference** \\ **Method** \\ \end{tabular} \\ \hline EC11 & 30 June & 256.98 & 300.42 & 301.82 & 635.22 & 587.54 & 538.69 & GWR-LST & \(-488.85\) & 1.13 \\ EC11 & 19 June & 256.00 & 303.12 & 304.69 & 531.46 & 449.71 & 396.21 & GWR-LST & \(-63.51\) & 1.06 \\ SD & 8 July & 366.55 & 302.32 & 301.57 & 538.51 & 560.06 & 609.94 & GWR-LST & \(-44.88\) & 0.97 \\ SD & 2 July & 279.14 & 300.27 & 301.20 & 564.5 & 526.47 & 482.10 & GWR-LST & \(-44.47\) & 0.91 \\ EC14 & 13 September & 294.87 & 295.67 & 292.38 & 202.73 & 271.51 & 316.02 & GWR-LST & \(-44.51\) & 0.81 \\ EC09 & 8 July & 207.92 & 300.22 & 298.88 & 564.90 & 601.46 & 644.33 & GWR-LST & \(42.87\) & 0.79 \\ EC15 & 8 July & 288.26 & 300.88 & 299.26 & 602.95 & 539.12 & 598.76 & DisT-LST & \(59.64\) & 0.75 \\ EC03 & 3 August & 304.06 & 304.24 & 305.91 & 590.97 & 599.10 & 584.51 & GWR-LST & \(-44.59\) & 0.75 \\ SD & 15 August & 301.37 & 302.18 & 301.42 & 477.30 & 413.73 & 462.44 & DisT-LST & \(48.71\) & 0.74 \\ EC01 & 30 June & 307.30 & 304.79 & 306.16 & 455.24 & 469.19 & 352.46 & GWR-LST & \(-57.37\) & 0.74 \\ SD & 14 September & 207.70 & 301.65 & 299.16 & 344.36 & 291.23 & 307.96 & DisT-LST & \(-79.73\) & 0.68 \\ EC11 & 13 September & 294.85 & 291.77 & 293.64 & 100.29 & 259.60 & 215.54 & DisT-LST & \(-44.46\) & 0.65 \\ EC02 & 13 September & 295.42 & 294.07 & 295.46 & 250.84 & 399.77 & 286.76 & DisT-LST & \(-53.01\) & 0.61 \\ EC13 & 13 September & 294.90 & 295.30 & 294.68 & 257.45 & 344.47 & 300.82 & DisT-LST & \(-43.48\) & 0.59 \\ EC15 & 13 September & 294.11 & 295.96 & 294.28 & 307.72 & 202.60 & 272.95 & DisT-LST & \(70.35\) & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The site information for relatively large differences between the two methods. contain buildings, and the GWR-LST LE is still better than the DisTrad-LST LE because the footprint area at EC11 on 30 June contains bare land and SD on 8 July and 27 July contains water. When the underlying surface is very complex (i.e., it contains buildings or bare land and water), the probability that the DisTrad method will choose such a pixel to establish the regression equation is very small or even zero. Thus, the downscaled LSTs obtained using the DisTrad method for these pixels are unstable. However, the GWR-based algorithm includes a spatially nonstationary relationship between the LST and other land surface parameters (NDVI, albedo and DEM) based on geographically varying regression coefficients. In contrast, the GWR-based method takes into account a greater number of independent variables that can effectively express the distribution of surface thermal dynamics. The DisTrad-LST LE is better than GWR-LST LE when the SHDI value lies between 0.59 and 0.75. In this case, the footprint area contains a mixture of three or fewer different types of vegetation, and in vegetated areas, the LSTs obtained using the DisTrad downscaling method are superior to those produced by the GWR downscaling method (see Table 3). At EC11 on 13 September, the footprint area contains two types of vegetation which is maize and other crop. However, at EC15 on 13 September, SHDI = 0 and the footprint area contains only one type of vegetation. In this case, the DisTrad-LST LE is much better than the GWR-LST LE. Because the observation system at EC15 was a superstation with a 40-m-tall tower, the sensor may receive radiation from the tower, resulting in an overestimated albedo and eventually leading to the LSTs being overestimated.