Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Environmental and Earth Sciences

    Abstract
2013(Vol.5, Issue:12)
Article Information:

Multivariate Analysis of the Senegalo-Mauritanian Area by Merging Satellite Remote Sensing Ocean Color and SST Observations

O. Farikou, S. Sawadogo, A. Niang, J. Brajard, C. Mejia, M. Crépon and S. Thiria
Corresponding Author:  M. Crépon 
Submitted: September 20, 2013
Accepted: October 04, 2013
Published: December 20, 2013
Abstract:
The Senegalo-Mauritanian upwelling is a very productive upwelling occurring along the West coast of Africa. The seasonal and inter-annual variability of the upwelling region between 9° and 22°N and 14° and 25°W was studied by merging monthly ocean color data and sea surface temperature provided by satellite sensors during twelve years from 1998 up to 2010. We combined these two parameters to obtain a unique index describing the spatio-temporal variability of the upwelling. We used a classification methodology consisting in a neural network topological map and a hierarchical ascendant classification. Six classes can explain most of the variability of this region, one of them (class 6) being dedicated to the coastal upwelling water, another being the signature of the Gulf of Guinea dome water (class 2), a third one to case 2 water (class 5). The classes can be considered as multi-factorial statistical indices allowing us to characterize the different water types of this region and to investigate their variability. It is shown that the upwelling extent is maximum in February-March, minimum in August-September. Its variability is linked to that of the wind and to the ITCZ position. The Gulf of Guinea waters moves northward in June and relaxes to their southward position in December. During the twelve years of observation, we were not able to evidence climatic trends of the SST and Chl-a concentration. The methodology we have developed can be used in a large variety of problems implying multi sensor measurements.

Key words:  Data fusion, machine learning, oceanography, phytoplankton, remote sensing, ,
Abstract PDF HTML
Cite this Reference:
O. Farikou, S. Sawadogo, A. Niang, J. Brajard, C. Mejia, M. Crépon and S. Thiria, . Multivariate Analysis of the Senegalo-Mauritanian Area by Merging Satellite Remote Sensing Ocean Color and SST Observations. Research Journal of Environmental and Earth Sciences, (12): 756-768.
ISSN (Online):  2041-0492
ISSN (Print):   2041-0484
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved