Ocean-color inversion: a combined approach by analytical solution and neural networks

In an earlier ocean-color algorithm, water's optical properties are classified into two categories. The major properties, such as the absorption and backscattering properties, vary widely and have significant influence on ocean color. The minor properties, such as the spectral slope of the gelbstoff absorption and the spectral power of particle backscattering, affect the ocean color modestly. The main objective of ocean-color remote sensing is to derive the major properties from water color. In model-based inversion algorithms, it is required to know the values of the minor properties. In this study, neural networks (NN) are used to estimate the minor properties. The NN-estimated minor properties are further used in a quasi-analytical algorithm to analytically derive the major properties. Significant improvements are found in the derivation of absorption and backscattering coefficients of coastal waters. The results here indicate an advantage of the neural network approach in inexplicitly linking a water property with water color, especially when there is no apparent relationship that can be explicitly expressed. The results further demonstrate the capability of the quasi-analytical algorithm to analytically derive major water properties from water color.