The number of hyperspectral sensors and data grows systematically and dynamically. Hyperspectral data delivers huge number of precise information but character of data requires specific methods of processing. One of these methods can be the use of the fractal description. In literature there are only a few attempts to analyze hyperspectral images using fractal parameters. Most of them concern on measuring fractal dimension of spectral signatures corresponding to the single pixels. The second group focuses on describing every hyperspectral image channel by fractal parameter. In this research we verify the fractal dimension usability for hyperspectral channels description. In order to calculate the fractal dimension we use the Differential Box- Counting method which is accurate and computationally efficient. We analyze the variability of fractal dimension calculated for hyperspectral image channels for two types of data – the radiance and the reflectance. The data we use has been acquired by AVIRIS sensor; designed and developed in Jet Propulsion Laboratory. Analyzed data include 224 channel images of solar reflected spectrum from 400nm to 2500nm and present homogenous land cover classes (water, mountains, agriculture and urban areas), as well as heterogeneous samples. Conducted analysis show advantages and limitations of the fractal parameter as a channel content descriptor. In particular, we observe that different land cover classes may be categorized basing on fractal dimension values of channels from specific parts of electromagnetic spectrum where values fluctuations are observed. Only water sample presents stable behavior of fractal dimension. For individual land cover class we observe differences between fractal dimension of radiance and reflectance data mainly for visible part of electromagnetic spectrum. Moreover, in cases of radiance data, irregularities of fractal parameter values indicate noisy channels. Summarizing, fractal dimension values seem to be useful as the channel quality measure and information capacity measure. Moreover, it could be used for noisy channels discrimination, usability estimation, different land cover classes categorization or atmospheric correction quality estimation.