# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 5 Conclusions  
Land surface temperature (LST) is an extremely important parameter that controls the atmospheric surface water heat balance. It has an extremely wide application value in the fields of earth resources and the environment, ecosystems, hydrological cycles, and so on. However, due to the inevitable cloud pollution of satellite remote sensing data, in areas covered by clouds, there will be a large number of null and invalid values in the LST retrieved from thermal infrared remote sensing data, which will reduce its accuracy, thus affecting the wide application of LST products. Therefore, it is of great significance to be able to reconstruct the invalid value of LST. In this paper, the missing pixels of MODIS LST in the Heihe River Basin from 2016 to 2019 were reconstructed using temporal, spatial, and spatiotemporal methods, and the performance obtained when using data acquired by Terra and Aqua was compared. Three methods were used for the temporal scale: linear temporal interpolation, HANTS, and SG filter. The spatial and spatiotemporal methods used fitting and STARFM, respectively. On the basis of the results, the linear temporal interpolation performed best among these methods, and the performance when using Aqua data was better than when using Terra data, with an RMSE of 7.13 K and an R\({}^{2}\) of 0.82, and the NSE and PBias were 0.78 and \(-\)0.76%, respectively. There are no obvious differences in the performance of the two smoothing filters (HANTS and SG). The overall results combined with Aqua data were as follows: R\({}^{2}\) (0.76 vs. 0.76), RMSE (7.97 K vs. 7.98 K), NSE (0.71 vs. 0.71), and Pbias (\(-\)1.03% vs. \(-\)1.07%). The overall performance of the fitting method was the worst among these methods, with an RMSE of 10.63 K and an R\({}^{2}\) of 0.64, and the NSE and PBias were 0.64 and \(-\)1.50%, respectively. On the one hand, it is not enough to only consider NDVI in the fitting process, because LST is the result of the joint action of many factors. On the other hand, in terms of spatial scale, the number of effective pixels related to invalid pixels will directly affect the effect of reconstruction. The temporal method can be used to complete the reconstruction only by relying on the effective pixels near the invalid pixels in time, and the change in LST over time has a certain regularity. For STARFM, we used ERA5 and AMSR2 data, respectively. Although STARFM can make full use of temporal and spatial information, its performance is not as good as linear temporal interpolation, because STARFM itself has a certain uncertainty. STARFM cannot distinguish the surface types of different pixels, and the prediction uncertainty of image edge pixels is high. Furthermore,we proposed a new method based on classification and adaptive window to complete the reconstruction of LST. The process of classifying and searching for similar pixels can improve the correlation between invalid pixels and effective pixels to a certain extent. Taking ERA5 data as an example, compared with linear temporal interpolation, the overall accuracy of this method was improved to a certain extent, with an RMSE of 6.96 K, an R\({}^{2}\) of 0.79, and an NSE and Pbias of 0.77 and \(-\)0.20%, respectively. This study compared the performance of different LST reconstruction methods. Finally, a new method based on classification and adaptive window was proposed based on a spatiotemporal scale. On the basis of the results, this method can indeed improve the accuracy of interpolation to a certain extent, thus providing a new concept for MODIS LST reconstruction in the Heihe River Basin and other parts of the world. [PERSON] contributed to searching the literature, describing the model, analyzing the data and model performances, providing the figures and maps, and writing the manuscript. [PERSON] contributed to designing the research. [PERSON] and [PERSON] contributed to remote sensing, ERA5, and AMSR2 data processing. All authors have read and agreed to the published version of the manuscript.