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首页> 外文期刊>Research journal of environmental and earth sciences >Multivariate Analysis of the Senegalo-Mauritanian Area by Merging Satellite Remote Sensing Ocean Color and SST Observations
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Multivariate Analysis of the Senegalo-Mauritanian Area by Merging Satellite Remote Sensing Ocean Color and SST Observations

机译:通过合并卫星遥感海洋颜色和SST观测值对塞内加洛-毛里塔尼亚地区进行多元分析

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摘要

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.
机译:Senegalo-Mauritanian上升流是发生在非洲西海岸的非常有生产力的上升流。通过合并从1998年到2010年的十二年中月度海洋颜色数据和卫星传感器提供的海面温度,研究了9°N和22°N以及14°和25°W之间上升区的季节性和年际变化。我们结合这两个参数,获得描述上升流的时空变化的唯一索引。我们使用的分类方法包括神经网络拓扑图和分层的上升分类。六类可以解释该地区的大部分变化,其中一类(第6类)专门用于沿海上升水,另一类是几内亚湾圆顶水的标志(第2类),第三类是针对案例2的水(第5类)。可以将这些类别视为多因素统计指标,从而使我们能够表征该地区的不同水类型并研究其变异性。结果表明,上升幅度在2月至3月最大,在8月至9月最小。它的可变性与风和ITCZ位置有关。几内亚湾水域在6月向北移动,在12月向南放松。在观察的十二年中,我们无法证明SST和Chl-a浓度的气候趋势。我们开发的方法可用于涉及多传感器测量的各种问题。

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  • 作者单位

    Ecole Superieure Polytechnique, Universite Cheikh Anta Diop (Dakar), BP 5085 Dakar Fann, Senegal, Tchad,Institut Universitaire des Sciences et Techniques d'Abeche (IUSTA), Tchad;

    Ecole Polytechnique de Thies, BP A10 Thies, Seneegal Tchad;

    Ecole Superieure Polytechnique, Universite Cheikh Anta Diop (Dakar), BP 5085 Dakar Fann, Senegal, Tchad;

    IPSL/LOCEAN, unite mixte CNRS-IRD-UPMC-MNHN, Case 100, 4 Place Jussieu, 75005 Paris France;

    IPSL/LOCEAN, unite mixte CNRS-IRD-UPMC-MNHN, Case 100, 4 Place Jussieu, 75005 Paris France;

    IPSL/LOCEAN, unite mixte CNRS-IRD-UPMC-MNHN, Case 100, 4 Place Jussieu, 75005 Paris France;

    IPSL/LOCEAN, unite mixte CNRS-IRD-UPMC-MNHN, Case 100, 4 Place Jussieu, 75005 Paris France;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Data fusion; machine learning; oceanography; phytoplankton; remote sensing;

    机译:数据融合;机器学习海洋学;浮游植物遥感;

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