An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

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.

Determining the optimal spectral sampling frequency and uncertainty thresholds for hyperspectral remote sensing of ocean color.

Using a modified geostatistical technique, empirical variograms were constructed from the first derivative of several diverse Remote Sensing Reflectance and Phytoplankton Absorbance spectra to describe how data points are correlated with "distance" across the spectra. The maximum rate of information gain is measured as a function of the kurtosis associated with the Gaussian structure of the output, and is determined for discrete segments of spectra obtained from a variety of water types (turbid river filaments, coastal waters, shelf waters, a dense Microcystis bloom, and oligotrophic waters), as well as individual and mixed phytoplankton functional types (PFTs; diatoms, eustigmatophytes, cyanobacteria, coccolithophores). Results show that a continuous spectrum of 5 to 7 nm spectral resolution is optimal to resolve the variability across mixed reflectance and absorbance spectra. In addition, the impact of uncertainty on subsequent derivative analysis is assessed, showing that a 3% Gaussian noise (SNR ~66) addition compromises data quality without smoothing the spectrum, and a 13% noise (SNR ~15) addition compromises data with smoothing.

Vector radiative transfer model for coupled atmosphere and ocean systems including inelastic sources in ocean waters.

Inelastic scattering plays an important role in ocean optics. The main inelastic scattering mechanisms include Raman scattering, fluorescence by colored dissolved organic matter (FDOM), and fluorescence by chlorophyll. This paper reports an implementation of all three inelastic scattering mechanisms in the exact vector radiative transfer model for coupled atmosphere and ocean Systems (CAOS). Simulation shows that FDOM contributes to the water radiation field in the broad visible spectral region, while chlorophyll fluorescence is limited in a narrow band centered at 685 nm. This is consistent with previous findings in the literature. The fluorescence distribution as a function of depth and viewing angle is presented. The impacts of fluorescence to the degree of linear polarization (DoLP) and orientation of the polarization ellipse (OPE) are studied. The DoLP is strongly influenced by inelastic scattering at wavelengths with strong inelastic scattering contribution. The OPE is less affected by inelastic scattering but it has a noticeable impact, in terms of the angular region of positive polarization, in the backward direction. This effect is more apparent for deeper water depth.

New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans.

We describe the development of a new suite of aerosol models for the retrieval of atmospheric and oceanic optical properties from the SeaWiFS and MODIS sensors, including aerosol optical thickness (τ), angstrom coefficient (α), and water-leaving radiance (L(w)). The new aerosol models are derived from Aerosol Robotic Network (AERONET) observations and have bimodal lognormal distributions that are narrower than previous models used by the Ocean Biology Processing Group. We analyzed AERONET data over open ocean and coastal regions and found that the seasonal variability in the modal radii, particularly in the coastal region, was related to the relative humidity. These findings were incorporated into the models by making the modal radii, as well as the refractive indices, explicitly dependent on relative humidity. From these findings, we constructed a new suite of aerosol models. We considered eight relative humidity values (30%, 50%, 70%, 75%, 80%, 85%, 90%, and 95%) and, for each relative humidity value, we constructed ten distributions by varying the fine-mode fraction from zero to 1. In all, 80 distributions (8 Rh×10 fine-mode fractions) were created to process the satellite data. We also assumed that the coarse-mode particles were nonabsorbing (sea salt) and that all observed absorptions were entirely due to fine-mode particles. The composition of the fine mode was varied to ensure that the new models exhibited the same spectral dependence of single scattering albedo as observed in the AERONET data. The reprocessing of the SeaWiFS data show that, over deep ocean, the average τ(865) values retrieved from the new aerosol models was 0.100±0.004, which was closer to the average AERONET value of 0.086±0.066 for τ(870) for the eight open-ocean sites used in this study. The average τ(865) value from the old models was 0.131±0.005. The comparison of monthly mean aerosol optical thickness retrieved from the SeaWiFS sensor with AERONET data over Bermuda and Wallops Island show very good agreement with one another. In fact, 81% of the data points over Bermuda and 78% of the data points over Wallops Island fall within an uncertainty of ±0.02 in optical thickness. As a part of the reprocessing effort of the SeaWiFS data, we also revised the vicarious calibration gain factors, which resulted in significant improvement in angstrom coefficient (α) retrievals. The average value of α from the new models over Bermuda is 0.841±0.171, which is in good agreement with the AERONET value of 0.891±0.211. The average value of α retrieved using old models is 0.394±0.087, which is significantly lower than the AERONET value.