## 1 Introduction  
Shortwave radiation emitted from the sun is a primary variable in the Earth's energy system. Shortwave radiation is a principal driving parameter of atmospheric phenomena including air-land interactions, heat transfer, and gas exchange. As climate change progresses, the precise quantification of surface solar irradiance (SSI) is being emphasized, and SSI is being used in solar energy applications [1]. Furthermore, measurements of SSI, which is considered among the most essential climate variables, have been developed and provided from diverse datasets including the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, the European Center for Medium Range Weather Forecasts (ECMWF) ERA reanalysis data, Clouds and the Earth's Radiant Energy System (CERES), the University of Maryland/MODIS (UMD/MODIS), the Climate Monitoring Satellite Application Facility (CM-SAF), and Global Land Surface Satellite (GLASS) products [2; 3; 4; 5; 6]. SSI is an important parameter in climatology and agriculture. It was reported that surface radiation was closely related to the canopy response and the normalized difference vegetation index, which is a satellite-derived parameter examining vegetation activity [7; 8]. For agricultural application, the gross primary production was estimated based on the satellite-derived radiation products in previous studies [9; 10]. [PERSON] et al. [11] investigated the effects of land-atmospheric coupling on drought prediction. In particular, the fluctuation of SSI has intensified with climate change, and the trends of changes in SSI are region-dependent. Thus, the importance of SSI has been emphasized in agricultural and climatological applications. Because SSI is affected by the absorption and scattering of aerosols and atmospheric molecules, it has fluctuated with progressing climate change [12]. In addition, precise information on SSI is essential for many climatological and agricultural applications, and it is a vital parameter for designing solar power systems and smart cities. Photovoltaic power, a renewable energy source that is an alternative to fossil fuels, could help tackle climate change. SSI directly affects climate change; hence, the importance of information on SSI is rapidly increasing over time [13]. The energy consumption in Asian countries constitutes over 40% of global energy consumption, and the energy consumption in China, India, Japan, and Korea accounts for over 70% of the energy consumption in Asian countries [14]. These Asian countries usually lack domestic fossil fuels and largely depend on imported fossil fuels; this trend is especially pronounced in Korea [15]. Interest in renewable energy has been increasing as a way to mitigate climate change in Korea; therefore, it is necessary to monitor and analyze SSI with a high degree of accuracy and spatiotemporal resolution in real time. Pyranometers, which obtain ground-based measurements of solar irradiance, provide accurate SSI measurements with high temporal resolution. However, the applicability of the ground-based measurements is limited in places where these pyranometers are installed sparsely. Furthermore, because SSI shows high spatiotemporal variability depending on the meteorological environment, pyranometers cannot represent wide spatial areas, and it is necessary to continuously inspect pyranometers for high quality in situ SSI measurements [16]. Although many mathematical and empirical methods have been developed as alternative methods, these methods show the uncertainty typical of physical models and meteorological observations [17]. To ensure the high spatiotemporal resolution and accuracy of SSI measurements, methods using remote sensing data such as split-window methods, lookup table methods, and physical model methods have been proposed [18; 19]. However, remote sensing methods remain greatly affected by the precision of the physical model.