Neural network approach to separate the non-algal absorption coefficient into dissolved and particulate

摘要

We present a method for the separation of the non-algal absorption coefficient into its independent components of dissolved species and non-algal particulate absorptions from remote sensing reflectance (Rrs) measurements in the visible part of the spectrum. This separation is problematic due to the similar absorption spectra of these substances. Due to this complication, we approach the problem by constructing a neural network which relates the remote sensing reflectance at the available MODIS visible wavelengths (412, 443, 488, 531, 547 and 667nm) with the ratio of the absorption coefficient of non-algal particulates to the absorption coefficient of dissolved species, thereby permitting analytical separation of the total non-algal absorption into particulate and dissolved components. The resulting synthetically trained algorithm is tested on simulated data as well as independently on the NASA Bio-Optical Marine Algorithm Data set (NOMAD). Very good agreement is obtained, with R2 values of 87% and 78% for the non-algal particulate and dissolved absorption components, respectively for the NOMAD. Finally, we apply the algorithm to MODIS data and present global distributions for these parameters.