, Volume 60, Issue 3, pp 519-530

Development of a Neural Network Algorithm for Retrieving Concentrations of Chlorophyll, Suspended Matter and Yellow Substance from Radiance Data of the Ocean Color and Temperature Scanner

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Abstract

An algorithm is presented to retrieve the concentrations of chlorophyll a, suspended pariclulate matter and yellow substance from normalized water-leaving radiances of the Ocean Color and Temperature Sensor (OCTS) of the Advanced Earth Observing Satellite (ADEOS). It is based on a neural network (NN) algorithm, which is used for the rapid inversion of a radiative transfer procedure with the goal of retrieving not only the concentrations of chlorophyll a but also the two other components that determine the water-leaving radiance spectrum. The NN algorithm was tested using the NASA's SeaBAM (SeaWiFS Bio-Optical Mini-Workshop) test data set and applied to ADEOS/OCTS data of the Northwest Pacific in the region off Sanriku, Japan. The root-mean-square error between chlorophyll a concentrations derived from the SeaBAM reflectance data and the chlorophyll a measurements is 0.62. The retrieved chlorophyll a concentrations of the OCTS data were compared with the corresponding distribution obtained by the standard OCTS algorithm. The concentrations and distribution patterns from both algorithms match for open ocean areas. Since there are no standard OCTS products available for yellow substance and suspended matter and no in situ measurements available for validation, the result of the retrieval by the NN for these two variables could only be assessed by a general knowledge of their concentrations and distribution patterns.

This revised version was published online in July 2006 with corrections to the Cover Date.