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The retrieval of soil moisture in vegetated areas with active microwave remote sensing is a challenging process because scattering form the vegetated area incorporates the volume scattering from the vegetation cover and surface scattering from the underlying soil. In addition to this, vegetation provides two way attenuation for the signal scattering from the underlying soil. Therefore, retrieval of soil moisture requires such an approach that may adequately represent the scattering behavior from the vegetation covered area by defining the scattering term from the vegetation and vegetation covered soil clearly. Characterization of scattering due to vegetation is another cumbersome and complex process because it needs several vegetation parameters as input and important problem is that these vegetation parameters exhibit temporal behavior. Therefore, it is the need of present scenario to look for such an alternate approach that may not require the scattering characterization of the concerned vegetation moreover employs the ancillary information. Normalized difference vegetation index (NDVI) which can be obtained with optical data and is an indicator of vegetation, may be efficiently employed with SAR for retrieval of soil moisture in the vegetated area. With this aspect, present paper aims to fuse the information from PALSAR (Phased Array type L-band Synthetic Aperture Radar) and MODIS (Moderate Resolution Imaging Spectroradiometer) data to develop an algorithm for soil moisture retrieval in vegetation covered area. A knowledge based approach involving various polarizations (linear, circular, linear 45, co- and cross-polarized ratios for both linear and circular polarization) is used for land cover classification by which urban and water area can be masked. A normalized scattering based empirical model is developed where normalized coefficient is a function of vegetation characteristic (i.e., NDVI). The developed relationship provides the scattering coefficient of ba- e soil in HH- and VV-polarization and these values were subsequently used in Dubois Model, which has been solved with copolarization ratio approach to provide volumetric soil moisture content irrespective of roughness value. Two sets of images were used to test and validate the developed algorithm. The obtained results are in good agreement with the ground truth values and have potential to apply for soil moisture retrieval in large scale.