A Daytime Cloud Detection Method for Advanced Meteorological Imager Using Visible and Near-Infrared Bands

Accurate cloud mask (CM) information is essential for distinguishing between cloud-free and cloudy pixels in various satellite remote sensing applications. This study presents a daytime cloud detection method for the Advanced Meteorological Imager (AMI) sensor onboard the Geo-Kompsat 2A satellite. The proposed cloud detection algorithm utilizes the AMI's four bands (0.51, 0.86, 1.38, and 1.61~\mu \text{m} ) in combination with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations. Thick clouds are detected using a conversion relationship between the green band ( 0.51~\mu \text{m} ) and the normalized difference water index (NDWI) at the 0.51- and 0.86- \mu \text{m} bands, while thin clouds are detected using the 1.38- and 1.61- \mu \text{m} bands with empirically determined threshold values between the collocated AMI and CALIPSO observations. A few overestimated cloud pixels are corrected using the normalized difference snow index (NDSI), which consists of reflectance values at 0.51 and 1.61~\mu \text{m} . Case studies were performed in the East Asia region, including Korea, Japan, and the southeastern part of China, for the four seasons from 2020 to 2021. The proposed cloud detection method was validated using the CALIPSO Vertical Feature Mask (VFM) data. Results showed excellent statistical scores: probability of detection (POD) = 0.92, false alarm ratio (FAR) = 0.11, and proportion correct (PC) = 0.87 for 2020 cases, and POD = 0.92, FAR = 0.11, and PC = 0.86 for 2021 cases. Moreover, the proposed method demonstrated the significant benefits of distinguishing clouds from sea ice and snow over land in winter.