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July 24, 2026Frontiers in Remote Sensing1 citationsOpen Access

Global assessment of merged multi-sensor ocean-colour chlorophyll-a products

SPSilvia PardoGTGavin H. TilstoneGDGiorgio Dall’Olmo

Key Points

  • Assess the performance of various merged ocean-colour chlorophyll-a products against in situ measurements.
  • Used > 13000 in situ chlorophyll-a-satellite matchup data points for validation.
  • Assessed multiple merged ocean-colour products including OC-CCI and CMEMS data streams.
  • Analyzed consistency of results with in situ data via rigorous validation procedures.
  • All assessed ocean-colour products showed consistent results with in situ data, achieving an RPD of 15%.
  • One dataset consistently overestimated chlorophyll-a, leading to its removal from the analysis.

Abstract

Chlorophyll a (Chl a), a proxy for phytoplankton biomass, is an important variable used to assess the health and state of the oceans, which are under increasing anthropogenic pressure.With the availability of global ocean-colour Chl a that now spans 25 years, there has been a concerted effort to produce merged data products to assess changes in Chl a over the global ocean as a result of both natural cycles and anthropogenic effects. To date, there has been no independent and rigorous assessment of the performance of these Chl a products to allow users to select the most suitable product, which is the objective of this paper. For this, we assembled a large global in situ dataset resulting in > 13000 in situ Chl a-satellite matchup data points. A comprehensive suite of merged ocean-colour products were assessed, which include two OC-CCI data streams (OC-CCI v5 and OC-CCI v6), two GlobColour data streams (using weighted averaging AVW and the GSM model) and the Copernicus Marine Environment Monitoring Service (CMEMS) GlobColour L3 and L4 and CMEMS-CCI products. The validation analysis showed that all these ocean-colour merged data products exhibited consistent results with the in situ Chl a data with an RPD of 15% (after removal of one dataset that was consistently overestimated by all products).

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Cite This Study

Pardo et al. (2026) studied this question.

synapsesocial.com/papers/6a62ffef395161722cd1509fhttps://doi.org/10.3389/frsen.2026.1825086
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