Current Remote Sensing applications demand multi-source, multi-sensor data fusion. Multi-source, multi-sensor data fusion provides useful information integrated for quick and better interpretation, understanding and effective decision-making. Data fusion of Synthetic Aperture Radar (SAR) data of Earth Observation Satellite-04 (EOS-04) and Optical Multispectral (MX) data of Sentinel-2 are current topic of interest in this paper. SAR and Optical MX which includes active and passive remote sensing technologies belong to different mechanisms of wave interaction due to widely separated and non-overlapping regions of the electromagnetic spectrum. In this paper, a novel approach to the re-implementation of Wavelet, Brovey, Fast Intensity Hue Saturation (FIHS), Frequency filtering, and Pure pixel data fusion methods is presented. The presented novel approach emphasises modulation-based fusion technique with proper normalization and scaling of both the input datasets. Fusion results of presented fusion methods are evaluated visually as well as quantitatively with quality metrics. The quality metrics demonstrate the ability of the presented novel approach to fuse optical spectral information into SAR data effectively to generate improved high-resolution SAR-coloured fused products.