3. Results

In Figure 1, we compare for each pixel of the image (March 10, 2004), the optical thickness and Chl-a concentration given by SeaWiFS algorithm (column 2) and those retrieved with SOM-NV (column 1). More than half are fully covered by Saharan dust with high optical thickness in these parts. In most standard SeaWiFS algorithm, a wide area of the image is not processed, while the satellite image can clearly observe the absorbing aerosol event. The SeaWiFS algorithm does not

Figure 1. Image of March 10, 2004; the first column represents the SOM-NV processing and the second the SeaWiFS processing. The last column represents the RGB satellite image.

generally process Saharan dust (see image in "true" colors); these absorbing Saharan dust are generally characterized by high optical thickness. The SOM-NV method permits retrieval of the aerosol type and aerosol optical properties from the statistical properties of the data. These retrievals are accurate for optical thickness values higher than 0.35. This is not the case for the standard SeaWiFS product. This permitted us to dramatically increase the number of pixels processed with respect to the standard SeaWiFS algorithm by an order of magnitude.

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

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

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

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,

In Figure 1, we compare for each pixel of the image (March 10, 2004), the optical thickness and Chl-a concentration given by SeaWiFS algorithm (column 2) and those retrieved with SOM-NV (column 1). More than half are fully covered by Saharan dust with high optical thickness in these parts. In most standard SeaWiFS algorithm, a wide area of the image is not processed, while the satellite image can clearly observe the absorbing aerosol event. The SeaWiFS algorithm does not

Figure 1. Image of March 10, 2004; the first column represents the SOM-NV processing and the second the SeaWiFS

processing. The last column represents the RGB satellite image.

transfer codes were specially designed to take African dusts into account.

depending on aridity conditions in Africa and the wind direction.

[2].

3. Results

chlorophyll-a concentration.

162 Aerosols - Science and Case Studies

Maps of average aerosol and chlorophyll-a were computed on grids of 9 km × 9 km GAC used for SeaWiFS level-3 GAC products. The averages obtained by SOM-NV seem statistically more representative than those obtained by SeaWiFS. Monthly averages of optical thickness obtained by the standard processing for the months of March and June of Figure 2 are drastic because only τ values not exceeding 0.35 are taken into account. These failures demonstrate impossibility for SeaWiFS to establish spatial and temporal global maps of aerosol particularly in absorbing area. This is due to the fact that desert aerosols frequently cross the ocean [10] preventing the standard algorithm to retrieve these aerosols and chlorophyll-a below them.

In Figure 3, average of AOT (top), Chl-a (middle) of the months of January and April 2005 is shown. As seen in Figure 2, we note also in Figure 3 failure for SeaWiFS to establish consistent spatial and temporal global maps of aerosol optical thickness. Fluctuations in the chlorophyll-a observed at Figure 3 are the same in the image for both algorithms. In terms of intensity, the values of Chl-a are higher for the standard algorithm at the continental shelves. An intercomparison with the SeaWiFS standard processing showed that the SOM-NV methodology increased the number of pixels processed of a factor until 10. This is due to the impossibility of the standard algorithm to retrieve the chlorophyll-a in the presence of Saharan dust, which frequently cross the ocean. The low number of pixels processed by SeaWiFS may bias the mean chlorophyll-a maps.

Figure 2. Spatial distribution of the aerosol optical thickness. Monthly average in March and June of the years 2001, 2002, and 2003. We note the same trends from 1 year to another. The aerosol optical thickness are strong in June and relatively low during the month of March for the SOM-NV processing (left block) and very low for the standard processing (right block).

Figure 3. Average of AOT (top), Chl-a (middle) of the months of January and April 2005 and the number of pixels used in calculating the average (bottom).

The presence of the case 2 waters explains these high values of Chl-a. The hypothesis of dark ocean is no longer valid in case of water 2. These results confirm that atmospheric corrections on the Saharan dust by SOM-NV are correct because Chl-a retrievals using OC4V4 are satisfactory spatial and in intensity.

Direct measurements of the optical thickness at 865 nm τ(865) were performed at two ground stations (Dakar-M'Bour—14°24N and 16°58W and Cabo Verde-Sal Island—16°45N and 22°57W). These measurements were made in the framework of the AErosol RObotic NETwork (AERONET) program which is a federated international network of sun/sky radiometers [11, 12]. Level 2.0 sun photometer measurements Dataphotometer (cloud screened and quality assured) at the two ground stations are available from 1998 to 2009.

The two AERONET measurements are found when at least one half of the SeaWiFS, or the SOM-NV retrievals within a 5 × 5 pixels square box containing the AERONET site are valid. The AERONET optical thickness values used for this validation were actually the mean of all the measurements made between 12:00 UT and 14:00 UT, because SeaWiFS images over the area were taken around 13:00 UT. We computed mean value for each month.

Spatial Distribution of Aerosol Optical Thickness Retrieved from SeaWiFS Images by a Neural Network Inversion... http://dx.doi.org/10.5772/65874 165

Figure 4. Scatter plot between monthly mean optical thickness measurements computed by SOM-NV (Δ) and the SeaWiFS product (\*) and the AERONET measurement at Dakar.

Figure 5. Scatter plot between monthly mean optical thickness measurements computed by SOM-NV (Δ) and the SeaWiFS product (\*) and the AERONET measurement at Cabo Verde.


Table 1. The performance indicators in Dakar site.

The presence of the case 2 waters explains these high values of Chl-a. The hypothesis of dark ocean is no longer valid in case of water 2. These results confirm that atmospheric corrections on the Saharan dust by SOM-NV are correct because Chl-a retrievals using OC4V4 are satis-

Figure 3. Average of AOT (top), Chl-a (middle) of the months of January and April 2005 and the number of pixels used in

Direct measurements of the optical thickness at 865 nm τ(865) were performed at two ground stations (Dakar-M'Bour—14°24N and 16°58W and Cabo Verde-Sal Island—16°45N and 22°57W). These measurements were made in the framework of the AErosol RObotic NETwork (AERONET) program which is a federated international network of sun/sky radiometers [11, 12]. Level 2.0 sun photometer measurements Dataphotometer (cloud screened

The two AERONET measurements are found when at least one half of the SeaWiFS, or the SOM-NV retrievals within a 5 × 5 pixels square box containing the AERONET site are valid. The AERONET optical thickness values used for this validation were actually the mean of all the measurements made between 12:00 UT and 14:00 UT, because SeaWiFS images over the area were taken around 13:00 UT. We computed mean value

and quality assured) at the two ground stations are available from 1998 to 2009.

factory spatial and in intensity.

calculating the average (bottom).

164 Aerosols - Science and Case Studies

for each month.


Table 2. The performance indicators in Cabo Verde site.

Figures 4 and 5 confirm that the mean aerosol maps shown above in Figure 2 are better for SOM-NV than SeaWiFS.

We compared the root-mean-square error (RMSE) and the mean relative error (MRE) of τSOM-NV and τSeaW with respect to the observed τAERO.

The MRE remains low for SOM-NV (38.8% at Dakar, 40.5% at Cabo Verde), the RMSE are less than 0.03, and the correlations are 80.23% at Dakar station and 82.34% at Cabo Verde. However, for SeaWiFS, the correlation is 13.45% for Dakar and 6.38% for Cabo Verde, and the MRE is 74.54% at Dakar and 60.75% at Cabo Verde. These results are resumed in Tables 1 and 2.
