# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 3 Results  
### Validation of the Temporal and Spatial Methods  
Ground measurement validation is essential for confirming the accuracy and applicability of different reconstruction methods. The land surface temperature can be obtained from AWS ground observation or calculated on the basis of four-component radiation data. Because there may be some uncertainty in the calculation process, AWS observation data was selected for the validation of LST reconstruction in this paper. Figures 3 and 4 present a comparison of the LST results obtained using the temporal and spatial methods, as well as the AWS ground measurement results, for the eight sites from 2016 to 2019. Each site validated the reconstruction results obtained from the Terra and Aqua satellites. Taking the Terra satellite as an example, the overall root mean square error (RMSE) of the estimated cloudy-sky LST of the fitting algorithm was 13.12 K, with a percentage bias (Pbias) of \(-\)3.76%, and a determination coefficient (R\({}^{2}\)) of 0.76, based on the ground measurements from the eight sites from 2016 to 2019. However, the validation results of LST estimated using linear temporal interpolation, HANTS and SG filter, and ground measurements showed that the deviation between them was not large. Among them, the overall RMSE of the three (10.70 K vs. 10.93 K vs. 11.04 K), R\({}^{2}\) (0.87 vs. 0.87 vs. 0.86), and Pbias (\(-\)3.24% vs. \(-\)3.34% vs. \(-\)3.39%) did not exhibit much difference. The negative Pbias values indicated that these methods overestimated LST. This illustrates that for the cloudy-sky pixels, part of the solar radiation will be absorbed by the clouds before reaching the land's surface, causing the temperature of the cloud-covered pixels to be lower than the surrounding clear-sky pixels. However, whether the spatial method or the temporal method is used, the reconstruction of the cloudy-sky pixels is dependent on the value of nearby clear-sky pixels. As a result, the estimated LST will be slightly higher than the actual LST. In addition, the overall Nash-Sutcliffe efficiency (NSE) of the fitting algorithm was not high, at only 0.22. This value is close to 0, which means that the fitting estimation value is close to the average value of the observation value, that is, the overall result is credible, but the simulation error of the process is high. In other words, simply borrowing the information from spatially neighboring pixels based on the vegetation indices may not be accurate for reconstruction. Because LST is the result of the comprehensive action of multiple factors, such as soil moisture, albedo, evapotranspiration, etc., there is no completely decisive role or influence. However, the NSE when using linear temporal interpolation, HANTS, and SG filters remained between 0.44 and 0.48. Compared with the fitting algorithm, they had higher reliability, but this was not satisfactory. Figure 3: Comparison of reconstructed LST using the temporal method 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. At the same time, similar conclusions can be obtained from the data of the Aqua satellite after validation. However, compared with the Terra data, the NSE values for linear temporal interpolation, HANTS, and SG filter using the Aqua data were significantly improved (between 0.71 and 0.78, where values close to 1 mean that the algorithm has high reliability). On the other hand, the overall RMSE of these algorithms decreased significantly (between 7.13 K and 7.98 K), and so did that of the fitting algorithm. Tables 2 and 3 summarize the statistical validation results of the two methods combined with Terra and Aqua data, respectively.