Mr. Glenn Campbell Profile

Glenn Campbell

Engineer at Univ of Southern Queensland

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Proceedings Article | 10 September 2009 Paper

Accuracy and precisions of water quality parameters retrieved from particle swarm optimisation in a sub-tropical lake

Glenn Campbell, Stuart Phinn

Proceedings Volume 7473, 74730G (2009) https://doi.org/10.1117/12.829737

KEYWORDS: Reflectivity, Absorption, Particle swarm optimization, Backscatter, Particle filters, Particles, Atmospheric corrections, Scattering, Remote sensing, Sun

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Optical remote sensing has been used to map and monitor water quality parameters such as the concentrations of hydrosols (chlorophyll and other pigments, total suspended material, and coloured dissolved organic matter). In the inversion / optimisation approach a forward model is used to simulate the water reflectance spectra from a set of parameters and the set that gives the closest match is selected as the solution. The accuracy of the hydrosol retrieval is dependent on an efficient search of the solution space and the reliability of the similarity measure. In this paper the Particle Swarm Optimisation (PSO) was used to search the solution space and seven similarity measures were trialled. The accuracy and precision of this method depends on the inherent noise in the spectral bands of the sensor being employed, as well as the radiometric corrections applied to images to calculate the subsurface reflectance. Using the Hydrolight® radiative transfer model and typical hydrosol concentrations from Lake Wivenhoe, Australia, MERIS reflectance spectra were simulated. The accuracy and precision of hydrosol concentrations derived from each similarity measure were evaluated after errors associated with the air-water interface correction, atmospheric correction and the IOP measurement were modelled and applied to the simulated reflectance spectra. The use of band specific empirically estimated values for the anisotropy value in the forward model improved the accuracy of hydrosol retrieval. The results of this study will be used to improve an algorithm for the remote sensing of water quality for freshwater impoundments.

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