Data fusion is used in remote sensing to increase the spatial and spectral resolution of an image. A common fusion method is to combine multispectral images with the panchromatic band from the same satellite with a higher spatial resolution, to create high-resolution multispectral imagery. Research has been undertaken to analyze and develop a suitable fusion method that can combine medium resolution satellite imagery having a sufficiently varied spectral range with high-resolution imagery having a sufficiently high spatial resolution. The basic fusion methods used in this study include Wavelet-PCA and High Pass Filtering which are usually used to combine multispectral images with panchromatic bands from the same satellite and use a spatial and temporal adaptive reflectance fusion model (STARFM) which is generally only for combines Landsat-8 data with MODIS. Combining data from different satellites allows us to increase not only the spatial and spectral resolution but also the temporal resolution which is important for many remote sensing applications such as soil monitoring and phenology. The dataset we used in the experiment was imaging from Pleaides-1B and Landsat-8. To measure the performance of the proposed method, we conducted an evaluation using several measurements such as peak signal-to-noise ratio (PSNR), universal quality image index (UQI), spectral angle mapper (SAM), visual information fidelity (VIF), and block sensitive PSNR (PSNRB). From this study, data on Fusion Pleiades-1B and Landsat-8 with a spatial resolution of 0.5m, which equivalent to the resolution of Pleiades-1B, with the visible spectral range VIS, NIR, and SWIR.