GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model

The Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-related products, most of which are derived from Level 1C data, which has undergone geometric and radiometric correction. For enhanced accuracy in air quality analysis, precise surface reflectance estimation is essential. However, high-reflectance elements, such as snow, interfere with the accurate estimation of radiance values, necessitating precise detection of such areas. Despite this, GEMS relies solely on the ultraviolet and partial visible bands, lacking the infrared bands crucial for snow detection, and it has no proprietary snow detection algorithm, instead utilizing near-real-time ice and snow extent data from the U.S. National Snow and Ice Data Center. Recently, deep learning techniques have shown potential in image processing, outperforming traditional algorithms, which could address these limitations. However, there is currently no deep learning training dataset available for snow detection specifically for GEMS. To address this issue, this study developed a GeoAI dataset for training a deep learning-based snow detection model for GEMS. In this research, we constructed input data using GEMS Level 1C data and generated label data based on GEMS, Advanced Meteorological Imager, and MODIS snow cover data. The snow detection dataset developed in this study is expected to address the snow detection limitations of GEMS, providing foundational data to enhance the reliability of future geostationary satellite-based air quality research.