Improved cloud detection in GOES scenes over land

• Published in: Remote sensing of environment Vol. 52; no. 1; pp. 36 - 54
• Main Authors: Simpson, James J., Gobat, Jason I.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.04.1995; Elsevier Science
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agricultural and forest meteorology; Agronomy. Soil science and plant productions; Biological and medical sciences; Earth, ocean, space; Exact sciences and technology; External geophysics; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Generalities, techniques; Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production; Meteorology; Water in the atmosphere (humidity, clouds, evaporation, precipitation); Cluster analysis; Methodology; Clouds; Multispectral remote sensing; Space remote sensing; Geostationary satellite; Computing; Algorithm; Image analysis; Meteorological satellite; Applied mathematics; Visible radiation; Infrared radiation; Detection
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/0034-4257(94)00080-7

Accurate cloud detection in satellite data over land is a difficult task complicated by spatially and temporally varying land surface reflectivities and emissivities. The GOES split-and-merge clustering (GSMC) algorithm for cloud detection in GOES scenes over land provides a computationally efficient, scene specific way to circumvent these difficulties. The algorithm consists of three steps: 1) a split-and-merge clustering of the input data which segments the scene into its natural grouping; 2) a cluster labeling procedure which uses scene specific adaptive thresholds (as opposed to constant static thresholds) to label the clusters as either cloud or cloud-free land; and 3) a post-processing step which imposes a degree of spatial uniformity on the labeled land and cloud pixels. An “a priori” mask feature also enhances cloud detection in traditionally difficult scenes (e.g., clouds over bright desert). Results show that the GSMC algorithm is neither regionally nor temporally specific and can be used over a large range of solar altitudes.