Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data

Abstract

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) has three bands in the visible and near-infrared (VNIR) with 15-m spatial resolution. The high spatial resolution has advantages for studying small aquatic areas, such as bay and lakes. Coastal areas are optically characterized by high concentrations of colored suspended matter, various phytoplankton pigments and colored dissolved organic matter (CDOM). The color ratio bio-optical algorithms often used for open sea are very difficult to apply in optically complex coastal water, since it is assumed that the constituents of seawater are mainly phytoplankton pigments. The Neural Network (NN) method, which is one of inverse modeling, has the potential to estimate chlorophyll a, suspended matter and CDOM from remotely sensed data. In the present investigation, we implemented the NN method in the analysis of ASTER data of Tokyo Bay, as a case study in the coastal waters in order to demonstrate the usefulness of remote sensing with high spatial resolution. After validation of the NN using simulated data sets and a field data set observed from a ship, estimation of the concentration of TSS and Chl-a was reasonably accurate. However, in the case of CDOM, the result is not reliable. Disadvantages of the NN method are discussed in this paper.