PROJECT TITLE :

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

ABSTRACT:

Satellite remote sensing has become an essential observing system to get comprehensive info on the status of coastal habitats. But, a important challenge in remote sensing of optically shallow water is to correct the results of the water column. This challenge becomes significantly difficult due to the spatial and temporal variability of water optical properties. So as to model the sunshine distribution for optically shallow water and retrieve the underside reflectance, a parameterized model was proposed by introducing an vital adjusted factor g. The synthetic knowledge sets generated by HYDROLIGHT were utilised to coach 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 2 obvious patterns among the different benthic habitat sorts. In coral reef, seagrass, and macrophyte habitats, g exhibited a remarkable peak at regarding 550 nm. The peak encompasses a price of concerning 2.47-a pair of.49. In white sand or hardpan habitats, g spectra are relatively flat. The semi-empirical model was applied to calculate the underside reflectance from the new weighting issue, the downward diffuse attenuation coefficient, and the irradiance reflectance simply below the sea surface collected in Sanya Bay in 2008 and 2009. Sensible agreement between the anticipated and measured values demonstrated that the weighting factor g is a good tool to modify the model for decoding and predicting bottom reflectance while not the requirement for any localized input (R2 > 0.seventy nine).


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