Neural network training: Using untransformed or log‐transformed training data for the inversion of ocean colour spectra?

Abstract

A bio‐optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio‐optical model took into account the optical properties of the three oceanic constituents, chlorophyll‐a, suspended non‐chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll‐a, non‐chlorophyllous particles and CDOM initially input into the bio‐optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio‐optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed data.