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Hulley, G.C., Hook, S.J. and Schneider, P. (2011) Optimized Split-Window Coefficients for Deriving Surface Temperatures from Inland Water Bodies. Remote Sensing of Environment, 115, 3758-3769.
https://doi.org/10.1016/j.rse.2011.09.014

has been cited by the following article:

  • TITLE: Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite

    AUTHORS: Babag Purbantoro, Jamrud Aminuddin, Naohiro Manago, Koichi Toyoshima, Nofel Lagrosas, Josaphat Tetuko Sri Sumantyo, Hiroaki Kuze

    KEYWORDS: Cloud Type Detection, Himawari-8, Split Window Algorithm, Brightness Temperature

    JOURNAL NAME: Advances in Remote Sensing, Vol.7 No.3, September 21, 2018

    ABSTRACT: Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus regions