MEETC2: Ocean color atmospheric corrections in coastal complex waters using a Bayesian latent class model and potential for the incoming sentinel 3 — OLCI mission
Introduction
The inversion of Ocean Color signal in coastal areas from top-of-atmosphere (TOA) measurements remains a scientific challenge. This is a crucial point for the ocean color community as many governmental policies such as the European Water Framework directive (WFD) rely on estimation of coastal water quality, itself possibly derived from space-based ocean color measurements (http://ec.europa.eu/environment/water/water-framework/index_en.html). Hence, ocean color inversion is certainly among the highest priority research topics for ocean color community. Different aspects may explain the difficulties encountered in this inversion process. Firstly, the contribution of suspended matters to the reflectance in the near infrared range (700–900 nm) is an issue as many algorithms expect these reflectances to be null. This assumption is called the black pixel hypothesis and relies on the strong natural absorption of the water in this domain (Antoine et al., 2006, be Gordon & Wang, 1994). Secondly, bio-optical modeling, i.e. the estimation of the water-leaving reflectance from the Inherent Optical Properties (IOPs, namely the absorption and backscattering of the sea water constituents) in complex coastal waters is also challenging. Despite accurate physical models exist for open clear waters that cover 85% of the oceans, their derivation for coastal waters is more complex (IOCCG, 2000, Maritorena et al., 2002). Lastly, aerosol and water reflectance spectra may show important correlation in the near infrared, a spectral domain typically used by the standard algorithms to distinguish the two contributions.
As a consequence, available operational standard level-2 reflectance products may perform poorly in coastal areas, and consequently these products are often flagged as anomalous values for such areas (MERIS DPM, 2005). The result for end users is typically that very few observations are available in coastal areas if the standard flags are applied. For available pixels in coastal turbid waters, reflectances in the blue and green bands are often underestimated and may involve physically-meaningless negative values (Goyens et al., 2013, Jamet et al., 2011). Park, De Cauwer, Nechad, and Ruddick (2004) show this strongly affects the relevance of level-2 products for the end users, which typically use water-reflectance spectra as inputs to estimate the chlorophyll-a and the suspended particulate matter concentrations (SPM, Doxaran, Froidefond, Lavender, & Castaing, 2002), or the vertical light attenuation (Jamet et al., 2012, Morel et al., 2006, Saulquin et al., 2013, Wang et al., 2009).
Over the last fifteen years, many coastal water algorithms have been developed to address user's needs for reliable water-reflectance data in coastal areas. Among them, the Schiller and Doerffer (1999) MERIS Case2-Regional (C2R) is based on a non-linear machine learning model, namely a Neural Networks (NN, Krasnopolsky and Schiller, 2003, Schiller and Doerffer, 1999), and estimates water reflectance over turbid areas. The learning paradigm relies on the calibration of a non-linear model to relate the available satellite-derived observations to the geophysical quantity of interest from a training dataset. This training dataset typically consists of a collection of in-situ measurements along with the satellite-derived measurements. This learning-based strategy may suffer from two major drawbacks: weak geophysical and biological interpretability of this ‘black-box’ model and the assumption on the representativity of the training dataset. They may restrict the applicability of the model to a specific region and question its validity with respect to the generally unknown variability of the atmospheric and water conditions.
Here, we develop a Bayesian latent class approach to address these limitations. To our knowledge, Bayesian model mixtures have been seldom explored for ocean color inversion (Frouin & Pelletier, 2014). The key feature of our model is the inversion of water and atmospheric signals from TOA observations using a multi-hypothesis setting. Rather than considering a single model, linear or not, we develop a Bayesian framework where the priors stated as mixture of models. Mixture models are trained both for water and aerosol contributions and lead to the identification of the reference spectrum families characterized by their mean spectrum and the associated covariance matrices. This training phase exploits in-situ data or radiative transfer simulations in the atmosphere and the water (Barker et al., 2008, Berk et al., 1999, Deuzé et al., 1989). Contrary to the machine learning approaches (NN, or Support Vector Regressions, SVR; Burges, 1998), the identified a priori distributions of the water and aerosol variables are directly linked to interpretable water types or atmospheric spectra.
Our inversion scheme, referred to hereafter as MEETC2, is applied to the estimation of water reflectances from the MEdium Resolution Imaging Spectrometer (MERIS) TOA observations. Nevertheless the methodology is generic and may be directly applied to other sensors such as the incoming OLCI sensor embedded onto the sentinel 3 platform. Model calibration and validation involve the MEris MAtchup In-situ Database (MERMAID) radiometric in-situ dataset (Barker et al., 2008). Quantitative comparisons with the standard MEGS v8 (Antoine et al., 2006) and the MERIS C2R Neural Network outputs clearly demonstrate the relevance of our approach.
Section snippets
Atmospheric correction principles
Ocean color sensor measures at TOA the upwelling radiance (Lu) in mW.m− 2.sr− 1 backscattered by the ocean–atmosphere system. This radiance originates from photons scattered by air molecules and/or aerosols, which may also have been reflected directly at the sea surface (glint effect, Cox and Munk, 1954a, Cox and Munk, 1954b), and may potentially have penetrated into the ocean. The measured TOA reflectance (ρTOA) is the ratio between the upwelling radiance Lu and the downwelling irradiance Ed,
Spectral reference signatures of the sea water using Non-negative matrix factorization
Given the spectral overlap of water and aerosol contributions especially in coastal areas, inversion of (Eq. (1)) requires some prior knowledge on water contributions. We propose here to determine from the training dataset a parametric spectral representation of water contributions. We use here a Non-Negative Matrix Factorization (NNMF) with projected gradients (Lin, 2007). Similarly to PCA, it relies on an additive decomposition on a basis learnt from the data. In contrast to PCA, it does not
Numerical Experiments
To validate the proposed methodology, the 5976 radiometric in-situ profiles have been randomly split into two sets of equal size: a training and a validation dataset. Model parameters are estimated using the training dataset. The optimal number of clusters, k, used in the GMM to estimate Xa and Xw a priori distributions, is determined using the Bayes Information Criterion (BIC) (Bhat & Kumar, 2010) and the explained variance criterion (Saulquin et al., 2015). Validation is performed with the
A significant improvement of ocean color inversion in coastal waters
Retrieving reliable Ocean Color reflectances from space in coastal areas remains a major challenge for a number of operational and scientific issues, including for instance the delivery of reliable satellite-derived products in coastal areas for the space agencies, bio-optical and biological modeling, as well as environmental monitoring policies such as the WFD. Using the MERMAID satellite/in-situ collocated observation database, a Bayesian latent class model was shown to significantly enhance
Acknowledgements
This work has been supported by the preparation and operations of the mission performance center (MPC) project for the Copernicus Sentinel-3 mission (ESA contract no. 4000111836) and the Copernicus - Marine environment monitoring service (CMEMS) ocean color thematic assembly center project (contract no. 2015/S 009-011214) and the French scientific interest group for ocean color GIS COOC.
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