Snow and sea ice roughness characterization from Quad-Pol H-A-α classes relative distribution

At spring when solar irradiance and air temperature cause snow and ice into an array of surface scatterers, point scatterers become the main signature of ridged environment. This information is better retained in unfiltered T3 matrixes. On an other hand Lee filtered T3 matrix gives information about the diversity of a distributed target which is equally indicative of the higher variability within ridges. In order to keep both in a single set of data, we computed an hybrid matrix composed of the alpha parameter from a single look T3 matrix and the entropy of the same matrix after a Lee 3×3 matrix. As revealed running a Wishart classification algorithm, this approach increases greatly contrast between ridged and flat areas. While this couldn't be used during most o the winter season, this may present a great potential to extract narrow linear structures when snow wetness increase surface scattering and therefore single and double bounce scattering mechanisms.