Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: Application to absorbing aerosols
This paper presents a new development of the NeuroVaria method. NeuroVaria computes relevant atmospheric and oceanic parameters by minimizing the difference between the observed satellite reflectances and those computed from radiative transfer simulations modelled by artificial neural networks. Aerosol optical properties are computed using the Junge size distribution allowing taking into account highly absorbing aerosols. The major improvement to the method has been to implement an iterative cost function formulation that makes the minimization more efficient. This implementation of NeuroVaria has been applied to sea-viewing wide field-of-view sensor (SeaWiFS) imagery. A comparison with in situ measurements and the standard SeaWiFS results for cases without absorbing aerosols shows that this version of NeuroVaria remains consistent with the former. Finally, the processing of SeaWiFS images of a plume of absorbing aerosols off the US East coast demonstrate the ability of this improved version of NeuroVaria to deal with absorbing aerosols.
satellite ocean colour absorbing aerosols neurovaria cost function variational inversion multilayer perceptron atmospheric correction