Multi-spectral images are crucial to detect and to understand phenomena in marine observation. However, in coastal areas, these phenomena are complex and their analyze requires multi-spectral images with both a high spatial and spectral resolution. Unfortunately, no satellite is able to provide both at the same time. As a consequence, multi-sharpening techniques---a.k.a. fusion or super-resolution of multi-spectral and/or hyper-spectral images---were proposed and consist of combining information from at least two multi-spectral images with different spatial and spectral resolutions. The fused image then combines their best characteristics. Various methods---based on different strategies and tools---have been proposed to solve this problem. This article presents a comparative review of fusion methods applied to Sentinel-2 MSI (13 spectral bands with a spatial resolution ranging from 10 to 60~m) and Sentinel-3 OLCI (21 spectral bands with a spatial resolution of 300~m) images. Indeed, both satellites are extensively used in marine observation and, to the best of the authors' knowledge, the fusion of their data was partially investigated (and not in the way we aim to do in this paper). To that end, we provide both a quantitative analysis of the performance of some state-of-the-art methods on simulated images, and a qualitative analysis on real images.