Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/27649
Title: Inverse modeling for retrieval of optical properties of sea water and atmospheric aerosols from remote sensing reflectance
Authors: CHANG CHEW WAI
Keywords: Ocean Colour atmospheric correction
Issue Date: 12-Aug-2008
Source: CHANG CHEW WAI (2008-08-12). Inverse modeling for retrieval of optical properties of sea water and atmospheric aerosols from remote sensing reflectance. ScholarBank@NUS Repository.
Abstract: In this study algorithms were developed to retrieve the optical properties of the atmosphere and water in the region of Singapore. They were developed for various satellite imageries.An algorithm was developed to correct for atmospheric effects in satellite imagery using cloud and shadow image features without the assumption of atmospheric optical properties as input for the visible bands. The method was able to retrieve optical properties of the atmosphere from hyperspectral satellite imagery. The atmospheric correction scheme was also able to perform atmospheric correction on high spatial resolution satellite (IKONOS) and high spectral resolution satellite (HYPERION). An inversion algorithm was also developed to retrieve optical properties of both shallow and deep turbid waters in Singapore. The inversion algorithm uses spectral windows where light has the least transmittance in water to minimize the influence from the sea bottom. This algorithm was validated by in-situ measurements of absorption and scattering coefficients performed in several sampling locations in the coastal waters of Singapore. It was also applied to retrieve optical properties from a dataset of simulated reflectance, which covers a wide range of optical properties and remote sensing reflectance of water. This dataset was compiled by an international working group to be used as a benchmark for evaluating algorithms for water optical properties retrieval.
URI: http://scholarbank.nus.edu.sg/handle/10635/27649
Appears in Collections:Ph.D Theses (Open)

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