Improved cloud detection for daytime AVHRR scenes over land

Accurate cloud detection, in Advanced Very High Resolution Radiometer (AVHRR) data over land is a difficult task complicated by spatially and temporally varying land surface reflectances and emissivities. The AVHRR Split-and-Merge Clustering (ASMC) algorithm for cloud detection in AVHRR scenes over land provides a computationally efficient, scene-specific, objective way to circumvent these difficulties. The algorithm consists of two steps: 1) a split-and-merge clustering of the input data (calibrated channel 2 albedo, calibrated channel 4 temperature, and a channel 3 - channel 4 temperature difference), which segments the scene into its natural groupings; and 2) a cluster-labelling procedure that uses scene-specific, joint three-dimensional adaptive labelling thresholds (as opposed to constant static thresholds) to label the clusters as either cloud, cloud-free land, or uncertain. The uncertain class is used for those pixels whose signature is not clearly cloud-free land or cloud (e.g., pixels at cloud boundaries that often contain subpixel cloud and land information that has been averaged together by the integrating grating aperture function of the AVHRR instrument). Results show that the ASMC algorithm is neither regionally nor temporally specific and can be used over a large range of solar altitudes. Sensitivity of the segmentation and labelling steps to the choice of input variables also was studied. Results obtained with the ASMC algorithm also compare favorably with those obtained from a wide range of currently used algorithms to detect cloud over land in AVHRR data. Moreov;er, the ASMC algorithm can be adapted for use with data to be taken by the Moderate Resolution Imaging Spectrometer-Nadir (MODIS-N).