A Satellite-Based Fog Detection Method UsingGeostationary Satellite Data For Urban Air Mobility

Fog is considered a significant threat to aircraft and Urban Air Mobility (UAM) vehicles during takeoff and landing, making accurate detection essential for safe operations. Although fog detection in Korea is commonly conducted using the Advanced Meteorological Imager (AMI) onboard the Geostationary Korea Multi-Purpose Satellite-2A (GK-2A), its spatial resolution is limited to 2 km for nighttime observations across East Asia, and 500 m for daytime. Such coarse resolution may not be sufficient to capture localized fog conditions critical for UAM operations, highlighting the need for fog detection with higher-resolution. This study presents a new method for generating a fine-resolution fog detection product. Specifically, Geostationary Korea Multi-Purpose Satellite-2A (GK-2A) Land Surface Temperature (LST) data, originally at 2 km resolution, were changed to 100 m resolution using a Taylor series expansion approach. This physics-based method overcomes the limitations of purely statistical downscaling by incorporating LST and surface characteristic indices derived from the Landsat-8 Thermal Infrared Sensor (TIRS). The downscaled LST was combined with an energy budget equation and the Crop Water Stress Index (CWSI) to derive high-resolution air temperature. By providing a high-resolution product, this method is expected to contribute significantly to safer UAM operations and advance remote sensing applications for weather-related hazard management.