Prioritizing ocean colour channels by neural network input reflectance perturbation

The radiative transfer model Hydrolight was used to produce 18000 artificial reflectance spectra representing case 1 and case 2 water conditions. Remote sensing reflectances were generated at the MERIS wavebands 412, 442, 490, 510, 560, 620, 665 and 682nm from randomly generated triplet combinations of chlorophyll a, non-chlorophyllous particles and coloured dissolved organic matter concentrations. These spectra were used to train multilayer perceptron neural network algorithms to perform the inversion from input reflectances to these three optically active substances. A method is proposed that establishes the neural network output error sensitivity towards changes in the individual input reflectance channels. From the output error produced for each reflectance change, a hypothesis about the importance of each band can be made. Results suggest a strong weight associated to the 620nm band for the estimation of all three substances