The Independent Component Analysis (ICA) aims, based on higher order statistical moments, in recovering statistical independent sources and the mixing mechanism, without having any physical background of the latter. Recently proposed as an alternative to Eigenvector decomposition in the analysis of Polarimetric SAR (PolSAR) data, it proved itself to be a very promising tool to better interpret non-Gaussian heterogeneous clutter, being employed in both urban area analysis as well as in snow monitoring applications. In this paper we intend to extend the range of applications of ICA based ICTD by investigating the results and the algorithm performance under tropical forest scenarios. Data from the P-band airborne dataset acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR is taken into consideration to analyse the potential of supplementary information introduced by the ICA approach.