Artificial-neural-network-based atmospheric correction algorithm: application to MERIS data

After the successful launch of the Medium Resolution Imaging Spectrometer (MERIS) on board of the European Space Agency (ESA) Environmental Satellite (ENVISAT) on March 1st 2002, first MERIS data are available for validation purposes. The primary goal of the MERIS mission is to measure the color of the sea with respect to oceanic biology and marine water quality. We present an atmospheric correction algorithm for case-I waters based on the inverse modeling of radiative transfer calculations by artificial neural networks. The proposed correction scheme accounts for multiple scattering and high concentrations of absorbing aerosols (e.g. desert dust). Above case-I waters, the measured near infrared path radiance at Top-Of-Atmosphere (TOA) is assumed to originate from atmospheric processes only and is used to determine the aerosol properties with the help of an additional classification test in the visible spectral region. A synthetic data set is generated from radiative transfer simulations and is subsequently used to train different Multi-Layer-Perceptrons (MLP). The atmospheric correction scheme consists of two steps. First a set of MLPs is used to derive the aerosol optical thickness (AOT) and the aerosol type for each pixel. Second these quantities are fed into a further MLP trained with simulated data for various chlorophyll concentrations to perform the radiative transfer inversion and to obtain the water-leaving radiance. In this work we apply the inversion algorithm to a MERIS Level 1b data track covering the Indian Ocean along the west coast of Madagascar.