## 4 Retrieval of LST Data and Evaluation of the Results
When we consider that the time difference between the two satellite observations was short (less than \(\pm\)5 min), the large spatial and temporal variations observed for LST differences might have been caused by differences in the viewing geometry of the two satellites, COMS and Terra/MODIS. Furthermore, navigation errors of the satellites and differences in surface emissivities may have contributed to the large LST differences. In the future, it would be valuable to conduct more research to establish what leads to the large LST differences, particularly during the daytime hours of the summer months. Figure 7: Spatial distribution of COMS LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST data and their differences for two select days during autumn and spring (15 October 2011 and 15 April 2011). Figure 8: Spatial distribution of COMS LST and MODIS LST data and their differences for two select days during summer (30 August 2011 and 30 July 2011). Figure 10 shows a scatter plot of MODIS LST and COMS LST data for selected months. In general, the COMS LST data agreed well with the MODIS LST data without regard to the LST values ranging from 250 K to 330 K, and we observed no obvious systematic patterns in the LST data differences. However, a number of pixels showed a large discrepancy between the two LST datasets, which may have been caused by the differences in the SZA of two satellites, cloud masking or navigation accuracy issues. Figure 9: Spatial distribution of COMS LST and MODIS LST data and their differences for two select days during winter (15 December 2011 and 28 February 2012). A summary of the evaluation results obtained from comparing the COMS LST data to the MODIS LST data is presented in Table 3. Although the estimation capabilities of the CSW algorithm clearly depended on what month and time of day the data were collected, the monthly mean correlation between COMS LST and MODIS LST data was very high (average value of 0.988) and stable (the  
Figure 10: Scatter plot of COMS LST _vs._ MODIS LST for the selected months: (**a**) April and September 2011; (**b**) July and August 2011; (**c**) December 2011 and February 2012.  
correlation coefficient ranged from 0.920 to 0.996). In general, the monthly mean biases were less than \(\pm\)2.4 km except for the daytime of July. Additionally, the monthly mean RMSEs were less than \(\pm\)3.5 km except for the months of May (daytime) and January (nighttime) irrespective of months and time. In general, the correlation coefficient and RMSE for nighttime data were systematically better than data collected during the daytime. However, the CSW algorithm systematically underestimated the nighttime LST values as compared to those taken during the daytime. These evaluation results show that the CSW algorithm can retrieve accurate LST data that would be appropriate for use in various applications irrespective of what months the data are collected in. However, attempts should be made to minimize the dependency of the estimation capabilities on the time of day that the data are collected to ensure the consistent retrieval of accurate LST data in the future.