## 4 Retrieval of LST Data and Evaluation of the Results  
LST was only retrieved when the pixel was masked clear from COMS cloud mask data given by KMA's National Meteorological Satellite Center. The preliminary quality of the COMS cloud masking algorithm was given by [PERSON] _et al_. [51]. Additionally, some improvements were applied to the cloud mask algorithm in recent years after operational retrieval from COMS data [52]. As ground observations of LST were very limited, the collocated MODIS LST data were used to estimate the precision of the LST retrieved from COMS data as in [PERSON] _et al_. [53]. [PERSON] _et al_. [53] also cross-compared the four-year LST from AVHRR and MODIS for the pixels stuck to certain homogeneity criteria. They found that differences between two LST showed both a diurnal and an annual pattern, with some irregular patterns. Furthermore, mean annual absolute differences were relatively low: only 2.2 K for the daytime and 1.4 K for the night-time scenes, with a 0.99 coefficient of determination (\(R^{2}\)). The quality of MODIS LST data is well documented in the literature [16, 27, 28, 29]. The bias and RMSE of MODIS LST Version 5 are 1 K and less than 0.7 K when the LST range from \(-\)10 \({}^{\circ}\)C to 58 \({}^{\circ}\)C and atmospheric column water vapor ranges from 0.4 to 3.5 cm [16]. However, it was reported that the accuracy of LST was degraded in the case of heavy aerosol loadings and in the barren region in summer months [48]. Because temporal variation of LST can be substantial, especially during the daytime under dry conditions, only comparable MODIS LST data with time differences less than \(\pm\)5 min were used in this study (MOD11_L2, MYD11_L2; Version5). Although \(\pm\)5 min is not a short time-frame to use in LST comparisons, particularly during the daytime, we used this threshold for data inclusion to allow for the acquisition of an adequate number of evaluation samples. Furthermore, evaluation was only applied to MODIS LST data, where all the 25 (5 \(\times\) 5) MODIS pixels were clear, based on the MODIS quality flag (mandatory-QA-flag is \"00\" or \"01\" and data-quality-flag is \"00\") given at the each pixel, because spatial resolutions for COMS IR1 and IR2 data were 4 km at the corresponding nadir points. Although [PERSON] and [PERSON] [54] and [PERSON] _et al_. [8] showed that the evaluation could be improved if the point spread function was considered instead of just averaging the pixels, we simply averaged the 25 pixels of MODIS LST data. Figures 7-9 show the spatial distribution of COMS LST and MODIS LST data and their differences for a few selected days during each season (three days and three nights). The images were constructed by compositing the collocated LST data for the selected days, because the observational area of MODIS changes according to its orbital path. The white color indicates pixels that were covered by clouds based on the flag data. The COMS-derived LST data showed a very similar spatial distribution to the LST data derived by MODIS, irrespective of what month or time of day the data were collected. As a result, differences between the two LST datasets were generally \(<\)5 K, irrespective of the geographic location and time except for the case of southern China. Although there were no obvious systematic patterns in the LST data differences, a closer analysis showed that negative differences were more prevalent during the nighttime than daytime. The latitudinal gradient for LST was weak or reversed during the daytime of the summer season, unlike in other seasons. This may have been caused by different thermal capacities of the soil surface, whereby there were more rapid and larger responses of LST in dry or sparsely vegetated regions than in wet or vegetated regions for similar insolation levels. Furthermore, the surface emissivity is impacted by the level of soil moisture [5, 19]. The CSW algorithm overestimated the LST by more than +5 K when compared to MODIS LSTs for southwest China.