Sentinel-3 Image Super-Resolution Using Data Fusion and Convolutional Neural Networks.

With the increasing availability of Sentinel-2 (S2) and Sentinel-3 (S3) data, developing higher-level data products becomes a very attractive option to relieve the spatial limitations of the Ocean and Land Colour Instrument (OLCI) of S3. In this context, this paper investigates the suitability of super-resolving operational OLCI products using the Multi-Spectral Instrument (MSI) of S2 as an offline spatial reference. Specifically, the proposed approach assembles a multi -spectral data fusion scheme together with a convolutinal neural network (CNN) mapping function to project the OLCI sensor onto its corresponding spatial reference which is synthetically generated by the OLCI/MSI fusion. In this way, the trained model is able to super-resolve operational OLCI products under demand without the need of using MSI data. The experimental part of the work shows the suitability of the proposed approach in the context of the Copernicus programme.