Abstract
Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds.
Highlights
We propose a cloud detection algorithm with GK-2A multitemporal and multispectral images during the daytime
Reflectance changes greatly as time changes; (2) find and mask the remaining cloud pixels by using the difference with minimum top of atmosphere (TOA) reflectance; and (3) find and mask the remaining cloud pixels by using NIR and normalized difference vegetation index (NDVI) images applied with a dynamic threshold
We evaluated whether the proposed algorithm is very well suited to classify clouds, as validated with frequently used Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Visible Infrared Imaging Radiometer Suite (VIIRS) cloud products
Summary
The Global Climate Observing System (GCOS) has selected cloud properties, which are considered suitable for global climate observation and have a significant impact on the needs of the United Nations Framework Convention on Climate Change (UNFCCC)
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