REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS

Indiana University-Purdue University Indianapolis (IUPUI) === Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global...

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Main Author: Li, Zuchuan
Other Authors: Li, Lin
Language:en_US
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/1805/3420
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spelling ndltd-IUPUI-oai-scholarworks.iupui.edu-1805-34202019-05-10T15:21:07Z REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS Li, Zuchuan Li, Lin Babbar-Sebens, Meghna Wilson, Jeffrey S. (Jeffrey Scott), 1967- Water color Remote sensing Sensitivity analysis Phytoplankton size Remote sensing -- Mathematical models Water -- Color -- Mathematical models Phytoplankton -- Size Earth sciences -- Remote sensing Earth resources technology satellites -- Measurement Inverse problems (Differential equations) -- Research Indiana University-Purdue University Indianapolis (IUPUI) Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale. 2013-08-14T16:04:23Z 2013-08-14T16:04:23Z 2013-08-14 Thesis http://hdl.handle.net/1805/3420 en_US
collection NDLTD
language en_US
sources NDLTD
topic Water color
Remote sensing
Sensitivity analysis
Phytoplankton size
Remote sensing -- Mathematical models
Water -- Color -- Mathematical models
Phytoplankton -- Size
Earth sciences -- Remote sensing
Earth resources technology satellites -- Measurement
Inverse problems (Differential equations) -- Research
spellingShingle Water color
Remote sensing
Sensitivity analysis
Phytoplankton size
Remote sensing -- Mathematical models
Water -- Color -- Mathematical models
Phytoplankton -- Size
Earth sciences -- Remote sensing
Earth resources technology satellites -- Measurement
Inverse problems (Differential equations) -- Research
Li, Zuchuan
REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
description Indiana University-Purdue University Indianapolis (IUPUI) === Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale.
author2 Li, Lin
author_facet Li, Lin
Li, Zuchuan
author Li, Zuchuan
author_sort Li, Zuchuan
title REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
title_short REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
title_full REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
title_fullStr REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
title_full_unstemmed REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
title_sort remote sensing of water color: model sensitivity analysis and estimation of phytoplankton size fractions
publishDate 2013
url http://hdl.handle.net/1805/3420
work_keys_str_mv AT lizuchuan remotesensingofwatercolormodelsensitivityanalysisandestimationofphytoplanktonsizefractions
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