An Improved Empirical Model for Retrieving Bottom Reflectance in Optically Shallow Water

Abstract

Satellite remote sensing has become an essential observing system to obtain comprehensive information on the status of coastal habitats. However, a significant challenge in remote sensing of optically shallow water is to correct the effects of the water column. This challenge becomes particularly difficult due to the spatial and temporal variability of water optical properties. In order to model the light distribution for optically shallow water and retrieve the bottom reflectance, a parameterized model was proposed by introducing an important adjusted factor g. The synthetic data sets generated by HYDROLIGHT were utilized to train a neural network (NN) and then to derive the adjustable parameter values. The parameter g was found to vary with water depth, water optical properties, and bottom reflectance. Specifically, it revealed two obvious patterns among the different benthic habitat types. In coral reef, seagrass, and macrophyte habitats, g exhibited a remarkable peak at about 550 nm. The peak has a value of about 2.47-2.49. In white sand or hardpan habitats, g spectra are relatively flat. The semi-empirical model was applied to calculate the bottom reflectance from the new weighting factor, the downward diffuse attenuation coefficient, and the irradiance reflectance just below the sea surface collected in Sanya Bay in 2008 and 2009. Good agreement between the predicted and measured values demonstrated that the weighting factor g is an effective tool to modify the model for interpreting and predicting bottom reflectance without the need for any localized input (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.79).