Sea clutter, the radar backscatter from the ocean surface, has been observed to be highly non-Gaussian. K distribution
is among the best distributions proposed to fit non-Gaussian sea clutter data. Using diffusive models, K distributed sea
clutter can be casted as a Gaussian speckle, with a de-correlation time of 0.1 s, modulated by a Gamma distribution,
with a de-correlation time of about 1 s, characterizing the large scale structures of the sea surface. Our analyses of large
amounts of real sea clutter data suggest that between the time scales for the Gaussian speckle and large scale structures
on the sea surface to de-correlate, sea clutter can be characterized as multifractal 1/f processes. This is the feature that
is not captured by diffusive models and underlies why K distribution cannot fit real sea clutter data sufficiently well.
We surmise that by combining K distribution and associated diffusive models with multifractal formalism, the many
different physical processes underlying sea clutter can be more comprehensively characterized.