IMPROVED CLOUD DETECTION IN GOES SCENES OVER LAND

Accurate cloud detection in satellite data over land is a difficult ta sk complicated by spatially and temporally varying land surface reflec tivities and emissivities. The GOES split-and-merge clustering (GSMC) algorithm for cloud detection in GOES scenes over land provides a comp utationally efficient, scene specific way to circumvent these difficul ties. The algorithm consists of three steps: 1) a split-and-merge clus tering of the input data which segments the scene into its natural gro uping; 2) a cluster labeling procedure which uses scene specific adapt ive 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 de tection in traditionally difficult scenes (e.g., clouds over bright de sert). Results show that the GSMC algorithm is neither regionally nor temporally specific and can be used over a large range of solar altitu des.