# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 3 Results
### Validation of the CAAW Method  
Recent studies have improved the interpolation method by considering the elevation factor, and a number of improved interpolation methods, such as kriging, have also been employed [33; 34]. Elevation chosen over all other LST-related parameters. This is because the elevation is a measurable variable that is linked to LST [35; 36]. Land surface-property-based approaches are more adaptable to a wider range of situations than elevation-based methods. Although the physical features of land surfaces are complicated to quantify, classification algorithms are able to differentiate them qualitatively. On the basis of this idea, we improve the interpolation method and propose a classification-based LST reconstruction method. We used a method based on classification and adaptive window to reconstruct the LST of the Heihe River Basin from 2016 to 2019. Figure 7 shows a comparison of reconstructed LST using classification and adaptive window with the LST measured by AWS at eight sites from 2016 to 2019. Table 5 summarizes the validation results obtained when using this method combined with the AMSR2 and ERA5 data, respectively. As can be seen from Table 5, there is no significant difference between the performances obtained when using ERA5 and AMSR2. Combining Tables 3 and 4, we compared the five reconstruction methods described above (fitting, linear temporal interpolation, HANTS, SG filter, and STARFM) with this method. Overall, the method based on classification and adaptive window has a similar accuracy to that of other methods. Taking the ERA5 data as an example, in terms of model reliability, the R\({}^{2}\) and NSE of this method were 0.79 and 0.77, respectively, second only to the values of 0.82 and 0.78 obtained for linear temporal interpolation. However, the RMSE of this method is slightly lower than that of linear temporal interpolation (6.96 K vs. 7.13 K), and the degree of overestimation is decreased (\(-\)0.20% vs. \(-\)0.76%). For each station, the accuracy of the method based on classification and adaptive window is better than that of other methods for stations with low altitudes. For the three stations at higher altitudes (Arou, Dashalong, Yakou), the accuracy of this method was slightly lower than that of linear temporal interpolation, but it was also better than the other methods. Overall, the performance of the method based on classification and adaptive window was relatively better than that of the other methods. Figure 7: Comparison of reconstructed LST using classification and adaptive window 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. We used the method based on classification and adaptive window to obtain daily LST from 2016 to 2019. Figure 8 shows the spatial distribution of the reconstructed LST based on the methods described above. To better analyze the temporal and spatiotemporal changes of LST in the Heihe River Basin, we randomly selected four days of the year, one in the spring (19 April), one in the summer (15 July), one in the autumn (18 October), and one in the winter (24 February). It can be seen from Figure 8 that the changes in surface temperature in the Heihe River Basin still conformed to those characteristic of a temperate continental climate. It is cold in winter, and the temperature reaches its lowest values, while it is hot in summer, and the temperature reaches its highest values. The distribution of temperature in the whole basin still exhibits obvious characteristics. The Qilian Mountain area in the upper reaches has greater precipitation and a glacier meltwater supply. The underlying surface is mostly vegetation and forests, and the temperature is the lowest in the whole basin. The underlying surface in the middle and lower reaches is mostly bare land, with less precipitation and strong evaporation.