## 3 Estimation of Surface Emissivity  
Most types of SW LST algorithms are based on the assumption that the surface emissivity of the two SW channels is known _a priori_. In this study, the vegetation cover method (VCM) presented by [PERSON] and [PERSON] [14] was used to generate surface emissivity data for the two SW channels. This method is based on the assumption that each pixel is composed of soil and vegetation [10,46]. The spatiotemporal variations of surface emissivity were generated by the VCM method through the use of a spatially varying land cover map and a temporally varying normalized difference vegetation index (NDVI), as shown in Equation (3):  
\[[\varepsilon_{i}=\varepsilon_{i,v}\times FVC+\varepsilon_{i,g}\times(1-FVC)\ ] \tag{3}\]  
where \(\varepsilon_{i}\) is the emissivity of two SW channels (IR1, IR2) and \(\varepsilon_{i,v}\) and \(\varepsilon_{i,g}\) are the maximum emissivity of pixels according to the land cover type when the pixels are fully covered by vegetation and ground, respectively. We used the look-up table provided by [PERSON] and [PERSON] [46] for MSG/SEVIRI data to look up values of \(\varepsilon_{i,v}\) and \(\varepsilon_{i,g}\). As shown in Figure 2, the Spectral Response Functions (SRFs) for COMS IR1 and IR2 were very similar to those from MSG2/SEVIRI. Hence, we used the tables provided for SEVIRI without any further modifications to acquire the emissivity data. The International Geosphere Biosphere Programme (IGBP) land-cover map was used in this study. This map classifies the land surface into 17 different types [22,24]. The fraction of vegetation coverage (FVC) for a given pixel was calculated using the method described by [PERSON] _et al_. [10] using 15-day MODIS NDVI data (MOD13, MYD13; Version 5):  
\[[\ \mathrm{FVC}=\frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}}\ ] \tag{4}\]  
where \(NDVI_{max}\) and \(NDVI_{min}\) represent the NDVI values when the pixels are fully covered by vegetation and soil, respectively. There was no established rule used for selecting \(NDVI_{max}\) and \(NDVI_{min}\) values, because these values depend on the sensor characteristics, level of correction, type of vegetation and spatial resolution [47,48]. In this study, the values of \(NDVI_{min}\) = 0.156 and \(NDVI_{max}\) = 0.461 were used, as was done by [PERSON] and [PERSON] [49].  
Figure 6 shows a sample of surface emissivity data for IR1 and IR2 and their differences for the first half of January and July. In general, the spatiotemporal variations of surface emissivity were captured well. Variability was greater in vegetated regions than in bare-soil regions irrespective of what channel was used. As a result, surface emissivity values of IR1 and IR2 were drastically increased during July as compared to January, except in high elevations and desert regions. Furthermore, the emissivity of IR2 was slightly greater than that of IR1 over the whole observational area; this was especially pronounced over desert and sparsely vegetated regions. Furthermore, [PERSON] and [PERSON] [50] inter-compared the emissivity data generated in this study with the Cooperative Institute for Meteorological Satellite Studies (CIMSS) and MODIS emissivity data to investigate the strength and weakness of the emissivity data. The comparison results showed that there was too much discrepancy among the three emissivity data sets. It indicates that more work is needed for the improvement of the emissivity of this region.  
Figure 6: Spatial distribution of surface emissivity and differences in surface emissivity for COMS IR1 and IR2 during the first half of January and July. Values were derived using the vegetation cover method (VCM).