# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 4 Discussion  
The reconstruction of land surface temperature was carried out based on different scales, and the reconstruction performance was also different. The reconstruction methods used in this study were based on three different scales: temporal, spatial, and spatiotemporal scales. For the temporal scale, we compared three methods: linear temporal interpolation, HANTS, and SG filter. Among them, the performance of linear temporal interpolation was the best, because it constructs a function in the period of valid data, and invalid values are simulated by the valid value closest to the invalid pixel. Therefore, linear temporal interpolation has a higher correlation with time compared with the other two methods. As for smoothing filters, there is no significant difference between HANTS and SG filters. From Figure 3, although the accuracy of these three methods does not exhibit much difference, from a temporal perspective, linear temporal interpolation can keep the details of LST changes, the smoothing filter will eliminate the details of LST change, only retaining the overall change trend. The results of linear temporal interpolation will be more in line with  
Figure 9: Comparison of soil moisture with LST reconstructed by classification and adaptive window at different sites from 2016 to 2019.  
the actual situation of LST change. However, a smoothing filter is not necessarily worse than linear temporal interpolation. In some specific cases, such as in a period of effective data, there are a large number of polluted images, and the effective data available for linear temporal interpolation are limited. At this point, we can use a smoothing filter to simulate invalid values with limited effective values. In other words, when the number of days of effective images is very small, the performance of the smoothing filter may be better than that of linear temporal interpolation. For the spatial scale, we used a simple fitting method based on NDVI, but the performance of this method was not satisfactory. On the one hand, for the data required for fitting, only using NDVI is not enough, because LST is the result of the joint action of many factors (such as evapotranspiration, soil moisture, etc. ), and more factors should be considered in the fitting. On the other hand, from a spatial perspective, the correlation between valid and invalid values may not be as direct as the temporal method, because fitting is performed based on the number of effective values in a certain area centered on invalid values. For example, in a \(3\times 3\) window, there are eight pixels directly related to an invalid pixel. There may be invalid values in these eight pixels. Meanwhile, surface reflectance in a remote sensing image is spatially dependent, meaning that pixel locations that are close together are more comparable than pixel sites separated by greater distances. The fewer effective pixels available, the worse the reconstruction effect. However, the temporal method only needs to find a certain number of valid values near the invalid values on the time axis of a section of valid data. Pixels that have a direct impact on the reconstruction effect will not be like that of the fitting. Moreover, the change in LST itself over time has a certain regularity. Therefore, without considering other factors, only depending on temporal or spatial information, the temporal method performs better than the spatial method in reconstructing invalid pixels. For the spatiotemporal scale, we used STARFM to reconstruct LST. Although STARFM makes full use of temporal and spatial information, it has a certain uncertainty. From Figure 8, it can be seen that there are some outliers in the results of reconstruction using the STARFM. [PERSON] and [PERSON] [39] analyzed the uncertainty of the STARFM algorithm and proposed a novel model to improve this algorithm. First, in the process of searching similar pixels, STARFM does not consider the land cover type of the pixel.