MEETC2: Ocean color atmospheric corrections in coastal complex waters using a Bayesian latent class model and potential for the incoming sentinel 3-OLCI mission

REMOTE SENSING OF ENVIRONMENT

卷:172

Article

Saulquin, Bertrand^1,2  Fablet, Ronan^2,3  Bourg, Ludovic¹  Mercier, Gregoire^2,3  d'Andon, Odile Fanton¹ 

[1] ACRI ST, F-06904 Sophia Antipolis, France

[2] Inst Mines Telecom, Telecom Bretagne, CNRS, UMR 3192,Lab STICC, F-29238 Brest, France

[3] Univ Europeenne Bretagne, F-35000 Rennes, France

关键词: Atmospheric corrections;    Ocean color;    Bayesian inversion;    Gaussian mixture model;   

DOI  :  10.1016/j.rse.2015.10.035

来源: Elsevier

【 摘 要 】

From top-of-atmosphere (TOA) observations, atmospheric correction for ocean color inversion aims at distinguishing atmosphere and water contributions. From a methodological point of view, our approach relies on a Bayesian inference using Gaussian Mixture Model prior distributions on reference spectra of aerosol and water reflectance. A reference spectrum for the aerosol characterizes the specific signature of the aerosols on the observed aerosol reflectance. A reference spectrum for the water characterizes the specific signature of chlorophyll-a, suspended particulate matters and colored dissolved organic matters on the observed sea surface reflectance. In our Bayesian inversion scheme, prior distributions of the marine and aerosol variables are set conditionally to the observed values of covariates, typically acquisition geometry acquisition conditions and preestimates of the aerosol and water reflectance in the near-infrared part of the spectrum. The numerical inversion exploits a gradient-based optimization from quasi-randomized initializations. We evaluate our estimates of the sea surface reflectance from the MERIS TOA observations. Using the MERMAID radiometric in-situ dataset, we obtain significant improvements in the estimation of the sea surface reflectance, especially for the 412, 442, 490 and 510 nm bands, compared with the standard ESA MEGS algorithm and the a state-of-the-art neural network approach (OR). The mean gain value on the relative error for the 13 bands between 412 and 885 nm is of 57% compared with MEGS algorithm and 10% compared with the C2R. The water leaving reflectances are used in Ocean Color for the estimation of the chl-a concentration, the colored dissolved organic matters absorption and the suspended particulate matters concentration underlying the potential of such approach to improve the standard level 2 products in coastal areas. We further discuss the potential of MEETC2 for the incoming OLCl/Sentinel 3 mission that will be launched in December 2015. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free