Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Uncertainty estimates are needed to assess ocean color products and qualify the agreement between missions. Comparison between field observations and satellite data, a process defined as validation, has been the traditional way to assess satellite products. However validation statistics can provide only an approximation for satellite data uncertainties as field measurements have their own uncertainties and as the validation process is imperfect, comparing data potentially differing in temporal, spatial or spectral characteristics. This study describes a method to interpret in terms of uncertainties the validation statistics obtained for ocean color remote sensing reflectance R_RS knowing the uncertainties associated with field data. This approach is applied to observations collected at sites part of the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) located in coastal regions of the European seas, and to R_RS data from the VIIRS sensors on-board the SNPP and JPSS1 platforms. Similar estimates of uncertainties σ_VRS (term accounting for non-systematic contributions to the uncertainty budget) are obtained for both missions, decreasing with wavelength from the interval 0.8–1.4 10⁻³ sr⁻¹ in the blue to a maximum of 0.24 10⁻³ sr⁻¹ in the red, values that are at least twice (but up to 8 times) the uncertainties reported for the field data. These uncertainty estimates are then used to qualify the agreement between the VIIRS products, defining the extent to which they agree within their stated uncertainty. Despite significant biases between the two missions, their R_RS products appear fairly compatible.

The quantification of uncertainties affecting satellite ocean color products is a fundamental step to ensure their compliance with mission and science requirements. This work investigated a methodology relying on the use of in situ radiometric data with known uncertainties to determine those affecting matching satellite data. By exploiting in situ radiometric data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), an advanced method was applied to radiometric data products from the Ocean and Land Color Instruments onboard the Sentinel-3A satellite (OLCI-A) and the Visible Infrared Imager Radiometer Suite onboard the Suomi National-Polar Orbiting Partnership satellite (VIIRS-S). The results from the analysis support the relevance of the method proposed.

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.