Correction of inter-mission inconsistencies in merged ocean colour satellite data

Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the 'Ocean Colour Climate Change Initiative' project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The Temporal Gap Detection Method is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series.

Automated summary

Consistency in a time series of ocean colour satellite data is crucial for determining long-term trends and statistics in Essential Climate Variables. This study addresses the issue of inter-mission inconsistency when merging data sets from different sensors due to the finite lifespan of satellites. The Temporal Gap Detection Method is introduced to minimize the magnitude of these inconsistencies and the results provide valuable insights for the interpretation of merged ocean color time series.