Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients

The spectroscopic capability of the satellite observation of ocean colour contributes to the estimation of the concentration of Chlorophyll-a (Chl-a) on the ocean surface. Chl-a can be a proxy in the determination of the phytoplankton biomass distribution, which indicates the trophic status of the w...

Full description

Saved in:

Bibliographic Details
Main Author:	Ridzuan, Fatin Nabihah Syahira
Format:	Thesis
Language:	English
Published:	2022
Subjects:	G70.39-70.6 Remote sensing
Online Access:	http://eprints.utm.my/id/eprint/99907/1/FatinNabihahSyahiraMFABU2022.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.99907
record_format	uketd_dc
spelling	my-utm-ep.999072023-03-29T06:02:13Z Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients 2022 Ridzuan, Fatin Nabihah Syahira G70.39-70.6 Remote sensing The spectroscopic capability of the satellite observation of ocean colour contributes to the estimation of the concentration of Chlorophyll-a (Chl-a) on the ocean surface. Chl-a can be a proxy in the determination of the phytoplankton biomass distribution, which indicates the trophic status of the water body. Long-term records of ocean colour data at lower spatial resolution of 1 km has been widely used in the derivation of various ocean colour algorithms. Although most of the algorithms perform well in clear water state, the significant uncertainty is evident when algaeprone areas near the coast and shallow water are mapped at the 1-km resolution. Therefore, the current study designed a methodology for new estimation of Chl-a and nutrient concentration in coastal water from multi-platform satellite imageries at medium spatial resolution (10 to 30 m) with systematic accuracy assessment using collocated sea-truth. In particular, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) techniques were used to establish the complex relationships of collocated remote sensing reflectance from the consecutive Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Instrument (MSI) images and in-situ parameters. Using these machine learning methods, this study also demonstrated the estimation of nutrients (nitrate and phosphate). The radiometric resolution of OLI in this study allowed higher overall accuracy of Chl-a estimates in the West Johor Straits (WJS) water. Meanwhile, the ANN recorded higher accuracy of Chl-a and nitrate estimates than that of the SVM and RF variants. Using the ANN, the Chl-a estimates at lower root-mean-square error (RMSE < 6 mg/m3) and APD of lower than 35% were mapped. The regression between Chl-a and nutrients was remarkably low (R2 < 0.2) on OLI and MSI. However, Fine Tree RF and ANN models improved the precision (RMSE) of nitrate (< 12 µmol/L) and phosphate (< 3 µmol/L). The absence of direct relationships of optical properties and spectral characteristics with nutrients led to higher uncertainties (> 100%), and this made phosphate content estimates in shallow water dubious, resulting in the need for extensive in-situ validation. Machine learning offers powerful estimation capability on Chl-a and nutrient concentration, especially for the higher spatio-temporal variability optical parameter of coastal waters, which was successfully demonstrated in this study through the discussed application in WJS. 2022 Thesis http://eprints.utm.my/id/eprint/99907/ http://eprints.utm.my/id/eprint/99907/1/FatinNabihahSyahiraMFABU2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150106 masters Universiti Teknologi Malaysia, Faculty of Built Environment & Surveying Faculty of Built Environment & Surveying
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	G70.39-70.6 Remote sensing
spellingShingle	G70.39-70.6 Remote sensing Ridzuan, Fatin Nabihah Syahira Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
description	The spectroscopic capability of the satellite observation of ocean colour contributes to the estimation of the concentration of Chlorophyll-a (Chl-a) on the ocean surface. Chl-a can be a proxy in the determination of the phytoplankton biomass distribution, which indicates the trophic status of the water body. Long-term records of ocean colour data at lower spatial resolution of 1 km has been widely used in the derivation of various ocean colour algorithms. Although most of the algorithms perform well in clear water state, the significant uncertainty is evident when algaeprone areas near the coast and shallow water are mapped at the 1-km resolution. Therefore, the current study designed a methodology for new estimation of Chl-a and nutrient concentration in coastal water from multi-platform satellite imageries at medium spatial resolution (10 to 30 m) with systematic accuracy assessment using collocated sea-truth. In particular, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) techniques were used to establish the complex relationships of collocated remote sensing reflectance from the consecutive Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Instrument (MSI) images and in-situ parameters. Using these machine learning methods, this study also demonstrated the estimation of nutrients (nitrate and phosphate). The radiometric resolution of OLI in this study allowed higher overall accuracy of Chl-a estimates in the West Johor Straits (WJS) water. Meanwhile, the ANN recorded higher accuracy of Chl-a and nitrate estimates than that of the SVM and RF variants. Using the ANN, the Chl-a estimates at lower root-mean-square error (RMSE < 6 mg/m3) and APD of lower than 35% were mapped. The regression between Chl-a and nutrients was remarkably low (R2 < 0.2) on OLI and MSI. However, Fine Tree RF and ANN models improved the precision (RMSE) of nitrate (< 12 µmol/L) and phosphate (< 3 µmol/L). The absence of direct relationships of optical properties and spectral characteristics with nutrients led to higher uncertainties (> 100%), and this made phosphate content estimates in shallow water dubious, resulting in the need for extensive in-situ validation. Machine learning offers powerful estimation capability on Chl-a and nutrient concentration, especially for the higher spatio-temporal variability optical parameter of coastal waters, which was successfully demonstrated in this study through the discussed application in WJS.
format	Thesis
qualification_level	Master's degree
author	Ridzuan, Fatin Nabihah Syahira
author_facet	Ridzuan, Fatin Nabihah Syahira
author_sort	Ridzuan, Fatin Nabihah Syahira
title	Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
title_short	Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
title_full	Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
title_fullStr	Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
title_full_unstemmed	Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients
title_sort	application of machine learning algorithms for estimating oceanic chlorophyll-a and nutrients
granting_institution	Universiti Teknologi Malaysia, Faculty of Built Environment & Surveying
granting_department	Faculty of Built Environment & Surveying
publishDate	2022
url	http://eprints.utm.my/id/eprint/99907/1/FatinNabihahSyahiraMFABU2022.pdf
_version_	1776100653806911488

Application of machine learning algorithms for estimating oceanic Chlorophyll-A and nutrients

Similar Items