Retrieving Aerosol Characteristics from Satellite Ocean Color Multi-spectral Sensors using a Neural-variational Method

We present a new algorithm suitable for retrieving and monitoring Saharan dusts from satellite ocean-color multi-spectral observations. This algorithm comprises two steps. The first step consists in classifying the TOA spectra using a neuronal classifier, which provides the aerosol type and a first guess value of the aerosol parameters. The second step retrieves accurate aerosol parameters by using a variational optimization method. We have analyzed 13 years of SeaWiFS images (September 1997-December 2009) in an Atlantic Ocean area off the coast of West Africa. As the method takes into account Saharan dusts, the number of pixels processed is an order of magnitude higher than that processed by the standard SeaWiFS algorithm. We note a strong seasonal variability. The Saharan dust concentration is maximal in summer during the rainy season and minimal in autumn when the vegetation bloom due to the rainy season prevents soil erosion by the wind.

[1]  S. Thiria,et al.  Estimating aerosol parameters above the ocean from MERIS observations using topological maps , 2007 .

[2]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[3]  D. Antoine,et al.  Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations , 2005 .

[4]  P. Deschamps,et al.  Description of a computer code to simulate the satellite signal in the solar spectrum : the 5S code , 1990 .

[5]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[6]  François Dulac,et al.  Control of atmospheric export of dust from North Africa by the North Atlantic Oscillation , 1997, Nature.

[7]  Fouad Badran,et al.  Retrieval of aerosol type and optical thickness over the Mediterranean from SeaWiFS images using an automatic neural classification method , 2006 .

[8]  Robert H. Evans,et al.  Assessment of Saharan dust absorption in the visible from SeaWiFS imagery , 2001 .

[9]  Sylvie Thiria,et al.  Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: Application to absorbing aerosols , 2006, Neural Networks.

[10]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[11]  S. Thiria,et al.  Use of a Neurovariational Inversion for Retrieving Oceanic and Atmospheric Constituents from Ocean Color Imagery: A Feasibility Study , 2005 .

[12]  Larry L. Stowe,et al.  Characterization of tropospheric aerosols over the oceans with the NOAA advanced very high resolution radiometer optical thickness operational product , 1997 .