Spatial variability assessment of local chlorophyll-A estimation using satellite data

The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in...

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Main Author:	Ab. Lah, Nor Zafirah
Format:	Thesis
Language:	English
Published:	2016
Subjects:	G70.212-70.215 Geographic information system
Online Access:	http://eprints.utm.my/id/eprint/78363/1/NorZafirahAbMFGHT2016.pdf
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spelling	my-utm-ep.783632018-08-26T04:53:30Z Spatial variability assessment of local chlorophyll-A estimation using satellite data 2016-10 Ab. Lah, Nor Zafirah G70.212-70.215 Geographic information system The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in Chl-a retrieval using local ocean colour algorithm. Studies on Chl-a variation, assessment of MODIS data and development of local ocean colour algorithm are less for Malacca Straits water. The aim of this study is to locally calibrate and validate the Chl-a derived from MODIS standard Chl-a algorithm (OC3M) on the latest R2013 data within the acceptable error tolerance at the Absolute Percentage Difference (APD) below 35% and to test the algorithm’s applicability. Iterative regression method with weighted function (WFd) namely Iterative Conditional Regression Model (ICRM) is introduced to reduce the spatial bias in the Chl-a estimate. Locally calibrated OC3M algorithm with in-situ data taken at two static stations and kernel 7×7 size named as OCms1 (calibrated with in-situ Case-1 water) and OCms2 (calibrated with in-situ Case-2 water) remarkably reduced the Chl-a bias with APD of 37% and 30% from 54% and 116% respectively. Then, using the ICRM, the APD of OCms1 WFd and OCms2 WFd is 26% and 29% respectively. Results of OCms WFd and OCms (with and without weighted function respectively) are combined for mapping the Chl-a in Case-1 and Case-2 waters. Result of applicability test and statistical analysis shows that OCms WFd ocean colour algorithm provides statistically highest accuracy for Chl-a estimation. The development of local Chl-a algorithm is essential for accurate Chl-a retrieval and it is significant to other marine studies such as in primary production and algal bloom in Malacca Strait water. 2016-10 Thesis http://eprints.utm.my/id/eprint/78363/ http://eprints.utm.my/id/eprint/78363/1/NorZafirahAbMFGHT2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95195 masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.212-70.215 Geographic information system
spellingShingle	G70.212-70.215 Geographic information system Ab. Lah, Nor Zafirah Spatial variability assessment of local chlorophyll-A estimation using satellite data
description	The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in Chl-a retrieval using local ocean colour algorithm. Studies on Chl-a variation, assessment of MODIS data and development of local ocean colour algorithm are less for Malacca Straits water. The aim of this study is to locally calibrate and validate the Chl-a derived from MODIS standard Chl-a algorithm (OC3M) on the latest R2013 data within the acceptable error tolerance at the Absolute Percentage Difference (APD) below 35% and to test the algorithm’s applicability. Iterative regression method with weighted function (WFd) namely Iterative Conditional Regression Model (ICRM) is introduced to reduce the spatial bias in the Chl-a estimate. Locally calibrated OC3M algorithm with in-situ data taken at two static stations and kernel 7×7 size named as OCms1 (calibrated with in-situ Case-1 water) and OCms2 (calibrated with in-situ Case-2 water) remarkably reduced the Chl-a bias with APD of 37% and 30% from 54% and 116% respectively. Then, using the ICRM, the APD of OCms1 WFd and OCms2 WFd is 26% and 29% respectively. Results of OCms WFd and OCms (with and without weighted function respectively) are combined for mapping the Chl-a in Case-1 and Case-2 waters. Result of applicability test and statistical analysis shows that OCms WFd ocean colour algorithm provides statistically highest accuracy for Chl-a estimation. The development of local Chl-a algorithm is essential for accurate Chl-a retrieval and it is significant to other marine studies such as in primary production and algal bloom in Malacca Strait water.
format	Thesis
qualification_level	Master's degree
author	Ab. Lah, Nor Zafirah
author_facet	Ab. Lah, Nor Zafirah
author_sort	Ab. Lah, Nor Zafirah
title	Spatial variability assessment of local chlorophyll-A estimation using satellite data
title_short	Spatial variability assessment of local chlorophyll-A estimation using satellite data
title_full	Spatial variability assessment of local chlorophyll-A estimation using satellite data
title_fullStr	Spatial variability assessment of local chlorophyll-A estimation using satellite data
title_full_unstemmed	Spatial variability assessment of local chlorophyll-A estimation using satellite data
title_sort	spatial variability assessment of local chlorophyll-a estimation using satellite data
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2016
url	http://eprints.utm.my/id/eprint/78363/1/NorZafirahAbMFGHT2016.pdf
_version_	1747817970937102336

Spatial variability assessment of local chlorophyll-A estimation using satellite data

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