A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters

Abstract

In this study, a neural network-based four-band model (NNFM) for the global oceanic and coastal waters has been developed in order to retrieve the total absorption coefficients a(λ). The applicability of the quasi-analytical algorithm (QAA) and NNFM models is evaluated by five independent data sets. Based on the comparison of a(λ) predicted by these two models with the field measurements taken from the global oceanic and coastal waters, it was found that both the QAA and NNFM models had good performances in deriving a(λ), but that the NNFM model works better than the QAA model. The results of the QAA model-derived a(λ), especially in highly turbid waters with strong backscattering properties of optical activity, was found to be lower than the field measurements. The QAA and NNFM models-derived a(λ) could be obtained from the MODIS data after atmospheric corrections. When compared with the field measurements, the NNFM model decreased by a 0.86-24.15% uncertainty (root-mean-square relative error) of the estimation from the QAA model in deriving a(λ) from the Bohai, Yellow, and East China seas. Finally, the NNFM model was applied to map the global climatological seasonal mean a(443) for the time range of July 2002 to May 2014. As expected, the a(443) value around the coastal regions was always larger than the open ocean around the equator. Viewed on a global scale, the oceans at a high latitude exhibited higher a(443) values than those at a low latitude.