Compatibility Between OLCI Marine Remote-Sensing Reflectance from Sentinel-3A and -3B in European Waters

There has been an uninterrupted suite of ocean-color missions with global coverage since 1997, a continuity now supported by programs ensuring the launch of a series of platforms such as the Sentinel-3 missions hosting the Ocean and Land Color Imager (OLCI). The products derived from these missions should be consistent and allow the analysis of long-term multi-mission data records, particularly for climate science. In metrological terms, this agreement is expressed by compatibility, by which data from different sources agree within their stated uncertainties. The current study investigates the compatibility of remote-sensing reflectance products RRS derived from standard atmospheric correction algorithms applied to Sentinel-3A and -3B (S-3A and S-3B, respectively) data. For the atmospheric correction l2gen, validation results obtained with field data from the ocean-color component of the Aerosol Robotic Network (AERONET-OC) and uncertainty estimates appear consistent between S-3A and S-3B as well as with other missions processed with the same algorithm. Estimates of the error correlation between S-3A and S-3B RRS, required to evaluate their compatibility, are computed based on common matchups and indicate varying levels of correlation for the various bands and sites in the interval 0.33–0.60 between 412 and 665 nm considering matchups of all sites put together. On average, validation data associated with Camera 1 of OLCI show lower systematic differences with respect to field data. In direct comparisons between S-3A and S-3B, RRS data from S-3B appear lower than S-3A values, which is explained by the fact that a large share of these comparisons relies on S-3B data collected by Camera 1 and S-3A data collected by Cameras 3 to 5. These differences are translated into a rather low level of metrological compatibility between S-3A and S-3B RRS data when compared daily. These results suggest that the creation of OLCI climate data records is challenging, but they do not preclude the consistency of time (e.g., monthly) composites, which still needs to be evaluated.