High-Spatial-Resolution Surface and Cloud-Type Classification from MODIS Multispectral Band Measurements

Abstract

A method for automated classification of surface and cloud types using Moderate Resolution Imaging Spectroradiometer (MODIS) radiance measurements has been developed. The MODIS cloud mask is used to define the training sets. Surface and cloud-type classification is based on the maximum likelihood (ML) classification method. Initial classification results define training sets for subsequent iterations. Iterations end when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The mean vector in the spectral and spatial domain within a class is used for class identification, and a final 1-km-resolution classification mask is generated for such a field of view in a MODIS granule. This automated classification refines the output of the cloud mask algorithm and enables further applications such as clear atmospheric profile or cloud parameter retrievals from MODIS and Atmospheric Infrared Sounder (AIRS) radiance measurements. The advantages of this method are that the automated surface and cloud-type classifications are independent of radiance or brightness temperature threshold criteria, and that the interpretation of each class is based on the radiative spectral characteristics of different classes. This paper describes the ML classification algorithm and presents daytime MODIS classification results. The classification results are compared with the MODIS cloud mask, visible images, infrared window images, and other observations for an initial validation.