Spatial Distribution of Aerosol Optical Thickness Retrieved from SeaWiFS Images by a Neural Network Inversion over the West African Coast

• Main Authors: Diouf, Daouda, Niang, Awa, Brajard, Julien, Sawadogo, Salam, Crepon, Michel, Thiria, Sylvie
• Format: Other/Unknown Material
• Language: English
• Published: IntechOpen 2016
• Subjects: Aerosols - Science and Case Studies; Aerosol Robotic Network
• Online Access: https://mts.intechopen.com/articles/show/title/spatial-distribution-of-aerosol-optical-thickness-retrieved-from-seawifs-images-by-a-neural-network-; https://doi.org/10.5772/65874

Aerosol optical thickness (AOT) was provided by SeaWiFS over oceans from October 1997 to December 2010. Weekly, monthly, and annually maps might help scientifics to better understand climate change and its impacts. Making average of several images to get these maps is not suitable on West African coast. A particularity of this area is that it is constantly traversed by desert dust. The algorithm used by SeaWiFS inverts the reflectance measurements to retrieve the aerosol optical thickness at 865 nm. For the poorly absorbing aerosol optical thickness less than 0.35, the standard algorithm works very well. On the west African coast that is often crossed by desert aerosol plumes characterized by high optical thicknesses. In this paper we study the spatial and temporal variability of aerosols on the West African coast during the period from December 1997 to November 2009 by using neural network inversion. The neural network method we used is mixed method of neuro-variational inversion called SOM-NV. It is an evolution of NeuroVaria that is a combination of a variational inversion and multilayer perceptrons, multilayer perceptrons (MLPs). This work also enables validation of the optical thickness retrieved by SOM-NV with AOT in situ measurements collected at AErosol RObotic NETwork (AERONET) stations.