# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 4 Discussion
Kriging was developed to achieve the best linear unbiased prediction (BLUP) and to determine the appropriate weights based on the fitted semivariance-distance relationship. Kriging can also be used to calculate the kriging variance, or reduced estimation variance, which can be used as a measure of prediction uncertainty. The prediction model combined with Kriging can distinguish the areas with and without land cover change, considering the structural similarity of the whole changed land cover area and predicting the changed area more accurately in the process of searching for spectrally similar pixels. Second, because the heterogeneous portions surrounding the margins have fewer coincident identical pixels, the shapes and edges of the surface objects have higher uncertainty. In contrast, homogeneous areas with a denser distribution of similar pixels and unchanged land cover areas have lower uncertainties. As can be seen in Figure 8, the outliers in the results of STARFM are concentrated in the edge area of the image. The densities and variabilities of similar pixels in the vicinity of the prediction site determine the prediction uncertainty [40]. In general, pixels with more coincident variations and denser nearby similar pixels will have lower prediction uncertainty [41]. Finally, because of variances in land surface reflectance between two observation dates, higher changes in surface reflectance lead to larger uncertainty in the fusion results. Through the comparison of the validation statistics in the results, it can be determined that the method based on classification and adaptive window can improve the accuracy of the interpolation method to a certain extent. First, in terms of spatial scale, this method uses classification to distinguish pixels of different land cover types, avoids pixels of different land types from participating in the reconstruction of invalid values, and improves the spatial correlation between effective pixels and invalid pixels. Second, for the selection of adaptive window size, we set the maximum window to 9 \(\times\) 9. On the one hand, for the threshold K (20), its proportion in the window will not appear too low. In this way, cases where the effective pixels searched for re too far away from the invalid pixels can be avoided, reducing the spatial correlation between them. On the other hand, if the window is set too large, the calculation time will be greatly increased, which will affect the efficiency of the reconstructed method. Finally, in terms of temporal scale, we selected the image having the clearest sky pixels in 10 days near the invalid pixel as the fill LST, because for the Heihe River Basin, there are rarely clouds for 10 consecutive days. In addition, if the time series is set too large, it will also reduce the time correlation between effective pixels and invalid pixels. Because there are no obvious differences between the cloud-top temperature of thin cloud and the actual LST, the influence of thin cloud on the surface temperature can almost be ignored [42]. However, the reconstruction of LST in this study was aimed at all pixels with cloud coverage, and did not consider the impact of the extent of the cloudiness in the images on the reconstruction method. As can be seen from Figure 9, soil moisture also shows a certain regular change with changing LST. Soil moisture is an important parameter affecting LST change, especially in arid and semi-arid areas. The analysis of soil moisture on different underlying surfaces in the Heihe River basin can lead to a better understanding of the impact of LST on soil moisture change. Especially in agricultural areas, it plays a vital role in surface evapotranspiration monitoring, agricultural irrigation management, crop yield prediction, climate change, drought, and hydrological processes.