Chlorophyll profile estimation in ocean waters by a set of artificial neural networks

  • Fabio Dall Cortivo
  • Ezzat S. Chalhoub
  • Haroldo F. Campos Velho
  • Milton Kampel

Abstract

In this work, we propose a methodology to estimate the profile of chlorophyll concentration from the upwelling radiation at the ocean surface, using a system of artificial neural networks (ANNs). The input patterns to train the networks are obtained from the resolution of the radiative transfer equation, where the absorption and scattering coefficients are represented by bio-optical models, with the profile of chlorophyll concentrations based on a shifted-Gaussian model. In the performed analysis, we used 14 720 profiles of chlorophyll that were generated by attributing two values to the biomass quantity, and by considering two sets of wavelengths and three sets containing the directions in which the radiation emitted at the surface is measured. To be able to recover the chlorophyll profile, we need to use a system of networks that works in a "cascade mode". The first one performs an analysis on the features of the chlorophyll profile from the upwelling radiation and determines which profiles can be recovered. The second and third ANNs act only on those profiles that can be recovered. The second ANN performs estimation of the standard deviation from the upwelling radiation and the chlorophyll concentration at the surface. Finally, the third ANN performs an estimation of the peak depth from the upwelling radiation, the chlorophyll concentration at the surface and the standard deviation estimated by second network. The stopping criteria we adopted was the cross-validation process. The obtained results show that the proposed methodology is quite promising.

Keywords

radiative transfer equation, inverse problems, artificial neural networks, chlorophyll profile concentration, bio-optics,

References

Published
Jan 25, 2017
How to Cite
DALL CORTIVO, Fabio et al. Chlorophyll profile estimation in ocean waters by a set of artificial neural networks. Computer Assisted Methods in Engineering and Science, [S.l.], v. 22, n. 1, p. 63-88, jan. 2017. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/15>. Date accessed: 26 jan. 2022.
Section
Articles