1. Introduction

The sensor SeaWiFS ocean color provides daily luminance measurements of ocean-atmosphere system in the visible and near infrared since October 1997. Luminances are at wavelengths 412,

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

443, 490, 510, 555, 670, 765, and 865 nm. For each wavelength λ, the TOA reflectance ρ is computed as

$$\rho\left(\lambda,\theta\_V,\phi\right) = \frac{\pi \cdot L(\lambda,\theta\_V,\Delta\Phi)}{E\_0(\lambda)\cdot\cos\left(\theta\_S\right)}\tag{1}$$

where E0(λ) is the extraterrestrial solar irradiance (in Wm−<sup>2</sup> nm−<sup>1</sup> , varying with the sun-earth distance), θ<sup>s</sup> and θ<sup>v</sup> are the sun- and satellite-viewing zenith angles, respectively, and ΔΦ = φ<sup>o</sup> − φ<sup>v</sup> is the azimuth angle difference between the satellite and the sun.

SeaWiFS aerosol products are generated, validated, and made available by NASA. These aerosols from the standard atmospheric correction algorithm can hardly be used for global aerosol studies because of aerosol optical thickness greater than about 0.35.

Plumes of absorbing aerosols are observed regularly on the West African coast near the Sahara, which prevents spatial and temporal operation of atmospheric and oceanic parameters.

This paper presents a new method to retrieve the aerosol parameters from ocean color satellite radiometer and is able to give information on absorbing aerosols.

#### 2. Data and method

For this study, we use daily luminance measurements made by the SeaWiFS sensor off the West African coast in an area between 8°–24°N and 14°–30°W. These measures extend the period of 1997–2009. The TOA reflectance ρ is the sum of contributions to the signal of each constituent of the atmosphere and ocean. The contribution of Rayleigh scattering, specular reflection, and absorption gas is known a priori with accuracy and has been removed from the signal. The pixels of clouds have been removed using the cloud detection algorithm [1]. Thus, the corrected reflectance is

$$
\mathfrak{p}\_{usel} = \mathfrak{p}\_a + \mathfrak{p}\_{ru} + t\mathfrak{p}\_w \tag{2}
$$

where ρ<sup>a</sup> + ρra is the atmospheric contribution, ρ<sup>w</sup> is the contribution of the water, and t is the transmittance of the atmosphere at a given wavelength (λ).

We used satellite data sets comprising ten-dimensional vectors, whose components are eight wavelengths measured by the radiometer and two viewing angles since the reflectance spectra depend on the geometry of the measurement. These angles are the sun zenith angle θs, and the scattering angle γ is defined as

$$\gamma = \arccos(-\cos\theta\_v \cos\theta\_\delta + \sin\theta\_v \sin\theta\_\delta \cos\Delta\Phi) \tag{3}$$

Each vector, whose components correspond to the SeaWiFS wavelengths, represents a ρused spectrum.

The learning dataset Dataobs consists of ρobs used observed at eight wavelengths (412, 443, 490, 510, 555, 670, 765, and 865 nm) extracted from pixels of SeaWiFS images during the year 2003 and two associated viewing angles (i.e., the sun zenith angle θ<sup>s</sup> and the scattering angle γ). Dataobs is thus composed of ten-component vectors.

443, 490, 510, 555, 670, 765, and 865 nm. For each wavelength λ, the TOA reflectance ρ is

ρ λ; <sup>θ</sup>V; <sup>φ</sup> <sup>¼</sup> <sup>π</sup> � <sup>L</sup>ð Þ <sup>λ</sup>; <sup>θ</sup>V; ΔΦ

distance), θ<sup>s</sup> and θ<sup>v</sup> are the sun- and satellite-viewing zenith angles, respectively, and

SeaWiFS aerosol products are generated, validated, and made available by NASA. These aerosols from the standard atmospheric correction algorithm can hardly be used for global

Plumes of absorbing aerosols are observed regularly on the West African coast near the Sahara, which prevents spatial and temporal operation of atmospheric and oceanic parameters.

This paper presents a new method to retrieve the aerosol parameters from ocean color satellite

For this study, we use daily luminance measurements made by the SeaWiFS sensor off the West African coast in an area between 8°–24°N and 14°–30°W. These measures extend the period of 1997–2009. The TOA reflectance ρ is the sum of contributions to the signal of each constituent of the atmosphere and ocean. The contribution of Rayleigh scattering, specular reflection, and absorption gas is known a priori with accuracy and has been removed from the signal. The pixels of clouds have been removed using the cloud detection algorithm [1]. Thus,

where ρ<sup>a</sup> + ρra is the atmospheric contribution, ρ<sup>w</sup> is the contribution of the water, and t is the

We used satellite data sets comprising ten-dimensional vectors, whose components are eight wavelengths measured by the radiometer and two viewing angles since the reflectance spectra depend on the geometry of the measurement. These angles are the sun zenith angle θs, and the

Each vector, whose components correspond to the SeaWiFS wavelengths, represents a ρused

ρused ¼ ρ<sup>a</sup> þ ρra þ tρ<sup>w</sup> (2)

γ ¼ arccosð Þ − cos θ<sup>v</sup> cos θ<sup>s</sup> þ sin θ<sup>v</sup> sin θ<sup>s</sup> cos ΔΦ (3)

where E0(λ) is the extraterrestrial solar irradiance (in Wm−<sup>2</sup> nm−<sup>1</sup>

radiometer and is able to give information on absorbing aerosols.

transmittance of the atmosphere at a given wavelength (λ).

ΔΦ = φ<sup>o</sup> − φ<sup>v</sup> is the azimuth angle difference between the satellite and the sun.

aerosol studies because of aerosol optical thickness greater than about 0.35.

E0ð Þ� λ cos ð Þ θ<sup>S</sup>

(1)

, varying with the sun-earth

computed as

160 Aerosols - Science and Case Studies

2. Data and method

the corrected reflectance is

scattering angle γ is defined as

spectrum.

We also used theoretical database Dataexpert that consists of the ρexpert used computed at eight wavelengths with a 2-layer radiative transfer model [2] for various optical thickness values, chlorophyll content, and geometry of the measurement and for five aerosol models.

Each Dataexpert vector comprises eight spectral components (ρexpert used ) and two geometry components which are the sun zenith angle θ<sup>s</sup> and the scattering angle γ. Dataexpert comprises 12,000,000 simulated vectors using four aerosol models (maritime, oceanic, coastal, and tropospheric) [3] and one absorbing aerosol (African dusts) [1]. The five aerosol models were computed at four different relative humidity (70%, 80%, 90%, and 99%).

Dataexpert was used in order to introduce the expertise and to retrieve the aerosol type and the optical thickness values.

In this study, we used SOM-NV [2], a two successive statistical models for analyzing the Dataobs images: the self-organizing map (SOM) [4] model and the NeuroVaria method [5, 6]. We first processed the images with a SOM model, which is well suited for visualizing and clustering a high-dimensional data set. We denoted this topological map as SOM-Angle-Spectrum (SOM-A-S). In the light of the results obtained by Niang et al. [7], we chose a similar architecture for SOM-A-S, a two-dimensional array with a large number of neurons (20 × 30 = 600). SOM-A-S was learned on the Dataobs of the year 2003. The vectors of the learning data set were thus clustered into 600 groups, allowing a highly discriminative representation of Dataobs. The second dataset, Dataexpert, representing the expertise, was used to decode the SeaWiFS images. The principle of the method is to compare the ten-component vectors of Dataexpert whose associated parameters are known, with those of the neurons of SOM-A-S according to a distance. At the end of the labeling, each neuron of SOM-A-S map has captured a set of ρexpert and takes a label, which is extracted from that set according to the procedure described in [7]. Each neuron is therefore associated with an atmospheric and ocean physical parameters (τ (AOT: aerosol optical thickness), C (chlorophyll-a pigment)), and an aerosol type. The SOM-A-S map being labeled, we are able to analyze a satellite image by projecting the ten-component vector (reflectances and viewing angles) associated with each pixel on the SOM-A-S map. Pixels captured by a neuron are assigned to the aerosol type and optical thickness associated with this neuron. For monthly climatology images, the aerosol type is estimated as the median of the types of the images considered.

The second statistical model improves the retrieval of the optical thickness. We used a neurovariational algorithm called NeuroVaria that is able to provide accurate atmospheric corrections for inverting satellite ocean color measurements. The algorithm minimizes a weighted quadratic cost function, J, by adjusting control parameters (atmospheric and oceanic) such as τ and C [8]. J describes the difference between the satellite measurement ρmes toa ð Þ <sup>λ</sup> <sup>r</sup>obs and a simulated reflectance ρsim toa ð Þ <sup>λ</sup> <sup>ρ</sup>sim computed using radiative transfer codes modeled by supervised neural networks (the so-called multilayer perceptrons, MLPs). The minimization implies the computation of the gradient of J with respect to the control parameters and consequently of the derivatives of the MLPs, which is done by the classical gradient backpropagation algorithm [9]. On this version of SOM-NV, the MLPs modeling the radiative transfer codes were specially designed to take African dusts into account.

A major advantage of the method is a gain in number of processed pixels from SeaWiFS. This work also enables validation of the optical thickness retrieved by SOM-NV with in situ measurements of optical thicknesses AERONET collected at stations in Dakar and Cabo Verde [2].

The complete methodology was applied to SeaWiFS images of the ocean off the West African coast from 1997 to 2009 to produce the type of aerosol, the aerosol optical thickness, and the chlorophyll-a concentration.

Monthly mean map aerosols and chlorophyll-a were calculated on 9 km × 9 km used for SeaWiFS GAC product level 3. Seasonality strong τ(865) is characterized by a strong invasion of dust into the months of June, July, and August. Their intensities vary from year to year, depending on aridity conditions in Africa and the wind direction.
