The detection of clouds within a satellite image is essential for retrieving surface geophysical parameters from optical and thermal imagery. Even a small percentage of cloud cover within a radiometer pixel can adversely affect the determination of surface variables such as albedo and temperature. Thus, onboard processing of satellite data requires reliable automated cloud detection algorithms that are applicable to a wide range of surface types. Unfortunately cloud-detection, particularly over snow- and ice-covered surfaces, is a problem that plagues the field of remote sensing because of the lack of spectral contrast. This paper discusses preliminary results based on kernel methods for unsupervised discovery of snow, ice, clouds, and other geophysical processes based on data from the MODIS instrument and discusses implementation in computationally constrained environments such as those found on satellites.