This study aims to present the atmospheric CO₂ and CH₄ levels and analyze their source characteristics at an observation station in a northeastern highland area of Korea for the 2012–2014 period. We summarized the measured CO₂ and CH₄ concentrations for the 2012–2014 period. In addition, we characterized the major source of the rise of CO₂ and CH₄ in Ganseong (GS) by employing bivariate polar plots (BPP) and the concentration weighted trajectory (CWT) method together with currently available information on emission sources. For the three years, CO₂ was generally high in the order of winter, spring, autumn and summer and CH₄ high in the order of winter, autumn, spring and summer. The observed positive correlations between the hourly CO₂ and CH₄ in every season suggested the possibility of shared common emission sources, but there is a necessity for elucidation on this in the future. The BPP analysis indicated the local sources that are likely to be associated with the rise of greenhouse gases (GHGs) observed at GS (combustion in the village, plant respirations nearby GS, and mobile emissions on the nearby road for CO₂ and leakages from the gas stations along the road and agricultural activities for CH₄). Synthesizing the CWT results together with emission source information from national and global emission inventories, we identified likely major source areas and characterized major emission sources. For example, the identified major sources for the winter CO₂ are coal combustion, coal washing and industrial activities in Inner Mongolia, northern and the northeastern China, fuel burning for the energy for the infrastructure of a northwestern city in South Korea, and the manufacturing industry and fuel combustion in the northern parts of North Korea. Hopefully, these kinds of results will aid environmental researchers and decision-makers in performing more in-depth studies for GHG sources in order to derive effective mitigation strategies.

Geometric accuracy of satellite imagery is a critical factor that determines the reliability of image analysis. Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary hyperspectral environmental satellite launched in 2020. Unique geometric and radiometric characteristics of GEMS make conventional techniques difficult to be applied for automated geometric accuracy evaluation of GEMS images. This study aims to handle this problem by matching GEMS images with images from Advanced Meteorological Imager (AMI), a meteorological geostationary satellite. Since GEMS and AMI differ in scan patterns and spectral responses, their spatiotemporal and spectral characteristics are different. For accurate matching, we apply collocation techniques to make these characteristics conform to each other. Geometric accuracy was evaluated through global and local matching. Global matching results showed that geometric accuracy of GEMS images exhibited a repetitive error trend of about 1 pixel in the north–south direction over time. Local matching enabled high-density analysis of detailed error distribution and direction across the entire image. Furthermore, comparison of results with and without collocation revealed that excluding spectral collocation increased irregular errors across the image. Without strict temporal collocation, geometric errors grew in regions strongly affected by observation time differences. In contrast, when spatial, spectral, and strict temporal collocation were all applied, the average root mean square error remained stable at about 1–1.5 pixels. These findings demonstrate that the collocation process is an essential preprocessing step for evaluating geometric accuracy of geostationary hyperspectral satellites such as GEMS. Future research will expand toward developing algorithms to improve geometric accuracy of the GEMS based on these results.