Generation of super-resolution gap-free ocean colour satellite products using DINEOF

Abstract. In this work we present a super-resolution approach to derive high spatial and temporal resolution ocean colour satellite datasets. The technique is based on DINEOF (Data Interpolating Empirircal Orthogonal Functions), a data-driven method that uses the spatio-temporal coherence of the analysed datasets to infer missing information. DINEOF is now used to effectively increase the spatial resolution of satellite data, and is applied to a combination of Sentinel-2 and Sentinel-3 datasets. The results show that DINEOF is able to infer the spatial variability observed in the Sentinel-2 data into the Sentinel-3 data, while reconstructing missing information due to clouds and reducing the amount of noise in the initial dataset. In order to achieve this, both Sentinel-2 and Sentinel-3 datasets have undergo the same preprocessing, including a comprehensive, region-independent, and pixel-based automatic switching scheme for choosing the most appropriate atmospheric correction and ocean colour algorithm to derive the in-water products. The super-resolution DINEOF has been applied to two different variables (turbidity and chlorophyll) and two different domains (Belgian coastal zone and the whole North Sea), and the submesoscale variability of the turbidity along the Belgian coastal zone has been studied.