## 6 Conclusions  
For producing an SSI distribution with high accuracy, we developed a model estimating SSI from the GK2A/AMI. We used sixteen channel data and two background-channel data for 30 days from the GK2A/AMI, SZA, and ESR as input data for the ML model. The in situ SSI measurements from 44 ASOS stations operated by KMA were used as reference data. Because the SSI indicates the global solar radiance, including the direct component and the diffuse component of solar radiance, in order to obtain the optimal model from the GK2A/AMI over Korea, we used the CNN model characterizing the surrounding environmental conditions based on neighboring pixels. We trained the model based on the data for the period from 25 July 2019 to 31 July 2020 and assessed the model based on the data after 1 August 2020. As a result of the statistical verification, the CNN model was the model that most accurately estimated the SSI, and the accuracy had an RMSE of 0.202 MJ m\({}^{-2}\), a bias of 0.002 MJ m\({}^{-2}\), and a Pearson's \(R\) of 0.979. To investigate the efficiency of the  
Figure 15: The distribution of SSI observing grid points with (**a**) minimum resolution of 100 km and (**b**) ideal resolution of 25 km from the World Meteorological Organization (WMO), where red circles indicate the ASOS stations operated by the KMA, and coral and green rectangles represent the missed and observed grid points, respectively. estimated CNN SSI from the GK2A/AMI, it was compared with the ground-based SSI from the KHOA IORS and NIFoS flux towers and indicated a good agreement with the in situ SSI. The CNN SSI showed an evident tendency to underestimate under an in situ SSI of more than 2.0 MJ m\({}^{-2}\). As the SZA increased, it was found that the RMSE decreased and the nRMSE increased, and underestimation under an SZA of more than 60 degrees was observed. As the visibility increased, the bias and nRMSE decreased. In particular, the tendency to overestimate was more pronounced as the visibility decreased, and a visibility of lower than 2 km showed a clear positive bias of 0.07 MJ m\({}^{-2}\) and a high nRMSE of 0.74. Furthermore, as the cloud amount increased, the nRMSE increased, and the nRMSE was 0.37 at a cloud amount of 10. The ESR was the most important feature for training the model. The CNN model was trained by focusing on infrared channels and those closely related to ESR and other features. Considering the local characteristics, a high monthly mean daily SSI was observed from April to June due to the Korean Peninsula's monsoon climate. Furthermore, because the Korean summer rainfall system, Changma, lasted for a longer period in 2020 than in 2021, the annual mean daily SSI was higher in 2021 than in 2020 due to the fluctuation of the SSI depending on the cloud conditions. The Korean Peninsula has a complex topography and land type, which causes difficulty in providing spatial information for SSI using only in situ measurements. Conversely, because the GK2A/AMI can monitor the environment with a high spatiotemporal resolution, the GK2A/AMI is a suitable tool for monitoring SSI in real time and complementing the gap in in situ measurements. It is thought that two-dimensional SSI information derived from the GK2A/AMI is a useful parameter for climatological and agricultural applications and also for designing solar power systems and smart cities. Future studies will attempt to examine the climatological SSI with other satellite-derived SSI measurements and estimate solar power production using GK2A/AMI-derived SSI. In addition, if it could reflect the seasonal effect by standardizing each spectral channel using ESR, it is possible to simplify the model and accelerate the testing and running of the model. Further studies to simplify the model are necessary, such as by reducing the complexity of the model and the number of input variables.