The Great Lakes, as the largest fresh surface water system on earth, greatly affects the quality of life and many aspects of the natural environment from weather and climate to wildlife and habitat. Lake Erie was declared as the first Great Lake that demonstrated a lake-wide eutrophic imbalance problem. Lake Erie has experienced increasingly frequent blooms of the toxic colonial cyanobacteria - Microcystis - in recent years. Lake Erie is also the Great Lake that is most subject to sediment loading. Continuing water quality monitoring in Lake Erie is needed to understand the complex variation of water quality and to support management decisions for water quality improvement.
This study investigated remote sensing techniques for quantifying chlorophyll-a concentrations and turbidity information for Lake Erie using MODIS imagery data. An accurate atmospheric correction is very important for remote sensing applications of imagery for water quality analysis, since the spectral energy from the water surface is much less than from the land surface. The existing four atmospheric correction methods were applied to MODIS imagery and evaluated for Lake Erie. The MUMM atmospheric correction method showed the best results for MODIS data of Lake Erie. The MUMM atmospheric correction method was chosen for developing models to estimate water quality in Lake Erie.
Remote sensing of water quality requires simultaneous or near-simultaneous collection of remotely sensed data and in situ ground truth data to develop and validate any model for quantifying water quality parameters using remote sensing images. Remote sensing water quality models derived using acquired ground truth data can be site specific, especially for turbid water bodies (Case 2 waters) where the bio-optical constituents vary considerably. This study evaluated the existing MODIS algorithms for chlorophyll-a concentration, including the revised OC2, the OC3M, and the Carder algorithms, combined with the most accurate atmospheric correction method. New models were developed and evaluated to improve the accuracy of estimation of the chlorophyll-a concentration and Secchi depth. These models included the general linear regression models, the Bayesian hierarchical regression model, and the nonlinear neural network model. The ground water quality measurements in Lake Erie used in this study included both chlorophyll-a concentration for measuring algae level and Secchi depth data to provide information on turbidity levels during the time period of May 7, 2002 to October 1, 2007, which were provided by Dr. David Culver (Department of Zoology, The Ohio State University).
This study has demonstrated the usefulness of MODIS data to quantify water quality in Lake Erie. A general linear regression model using the MODIS band ratio of 488/555 nm with a logarithm transformation can be used for estimating Secchi depth in Lake Erie. The comparisons of the three models for estimating chlorophyll-a concentration showed that the neural network model appear to provide the best estimates of chlorophyll-a concentration in Lake Erie using MODIS Aqua images.
","abstract_html":"<p>The Great Lakes, as the largest fresh surface water system on earth, greatly affects the quality of life and many aspects of the natural environment from weather and climate to wildlife and habitat. Lake Erie was declared as the first Great Lake that demonstrated a lake-wide eutrophic imbalance problem. Lake Erie has experienced increasingly frequent blooms of the toxic colonial cyanobacteria - Microcystis - in recent years. Lake Erie is also the Great Lake that is most subject to sediment loading. Continuing water quality monitoring in Lake Erie is needed to understand the complex variation of water quality and to support management decisions for water quality improvement. </p><p>This study investigated remote sensing techniques for quantifying chlorophyll-a concentrations and turbidity information for Lake Erie using MODIS imagery data. An accurate atmospheric correction is very important for remote sensing applications of imagery for water quality analysis, since the spectral energy from the water surface is much less than from the land surface. The existing four atmospheric correction methods were applied to MODIS imagery and evaluated for Lake Erie. The MUMM atmospheric correction method showed the best results for MODIS data of Lake Erie. The MUMM atmospheric correction method was chosen for developing models to estimate water quality in Lake Erie. </p><p>Remote sensing of water quality requires simultaneous or near-simultaneous collection of remotely sensed data and in situ ground truth data to develop and validate any model for quantifying water quality parameters using remote sensing images. Remote sensing water quality models derived using acquired ground truth data can be site specific, especially for turbid water bodies (Case 2 waters) where the bio-optical constituents vary considerably. This study evaluated the existing MODIS algorithms for chlorophyll-a concentration, including the revised OC2, the OC3M, and the Carder algorithms, combined with the most accurate atmospheric correction method. New models were developed and evaluated to improve the accuracy of estimation of the chlorophyll-a concentration and Secchi depth. These models included the general linear regression models, the Bayesian hierarchical regression model, and the nonlinear neural network model. The ground water quality measurements in Lake Erie used in this study included both chlorophyll-a concentration for measuring algae level and Secchi depth data to provide information on turbidity levels during the time period of May 7, 2002 to October 1, 2007, which were provided by Dr. David Culver (Department of Zoology, The Ohio State University).</p><p>This study has demonstrated the usefulness of MODIS data to quantify water quality in Lake Erie. A general linear regression model using the MODIS band ratio of 488/555 nm with a logarithm transformation can be used for estimating Secchi depth in Lake Erie. The comparisons of the three models for estimating chlorophyll-a concentration showed that the neural network model appear to provide the best estimates of chlorophyll-a concentration in Lake Erie using MODIS Aqua images.</p>","abstract_has_math":false,"creators":["Zhang, Li"],"institution":"The Ohio State University","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Merry, Carolyn"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-21","date_published":"2013-05-21","updated_at":"2026-07-24T03:36:08Z","subjects":["Civil Engineering"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=osu1357232811","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Merry, Carolyn"]},{"key":"dc:creator","label":"Author","values":["Zhang, Li"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-21"]},{"key":"dc:publisher","label":"Institution","values":["The Ohio State University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Ohio State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Civil Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=osu1357232811"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The Great Lakes, as the largest fresh surface water system on earth, greatly affects the quality of life and many aspects of the natural environment from weather and climate to wildlife and habitat. Lake Erie was declared as the first Great Lake that demonstrated a lake-wide eutrophic imbalance problem. Lake Erie has experienced increasingly frequent blooms of the toxic colonial cyanobacteria - Microcystis - in recent years. Lake Erie is also the Great Lake that is most subject to sediment loading. Continuing water quality monitoring in Lake Erie is needed to understand the complex variation of water quality and to support management decisions for water quality improvement.
This study investigated remote sensing techniques for quantifying chlorophyll-a concentrations and turbidity information for Lake Erie using MODIS imagery data. An accurate atmospheric correction is very important for remote sensing applications of imagery for water quality analysis, since the spectral energy from the water surface is much less than from the land surface. The existing four atmospheric correction methods were applied to MODIS imagery and evaluated for Lake Erie. The MUMM atmospheric correction method showed the best results for MODIS data of Lake Erie. The MUMM atmospheric correction method was chosen for developing models to estimate water quality in Lake Erie.
Remote sensing of water quality requires simultaneous or near-simultaneous collection of remotely sensed data and in situ ground truth data to develop and validate any model for quantifying water quality parameters using remote sensing images. Remote sensing water quality models derived using acquired ground truth data can be site specific, especially for turbid water bodies (Case 2 waters) where the bio-optical constituents vary considerably. This study evaluated the existing MODIS algorithms for chlorophyll-a concentration, including the revised OC2, the OC3M, and the Carder algorithms, combined with the most accurate atmospheric correction method. New models were developed and evaluated to improve the accuracy of estimation of the chlorophyll-a concentration and Secchi depth. These models included the general linear regression models, the Bayesian hierarchical regression model, and the nonlinear neural network model. The ground water quality measurements in Lake Erie used in this study included both chlorophyll-a concentration for measuring algae level and Secchi depth data to provide information on turbidity levels during the time period of May 7, 2002 to October 1, 2007, which were provided by Dr. David Culver (Department of Zoology, The Ohio State University).
This study has demonstrated the usefulness of MODIS data to quantify water quality in Lake Erie. A general linear regression model using the MODIS band ratio of 488/555 nm with a logarithm transformation can be used for estimating Secchi depth in Lake Erie. The comparisons of the three models for estimating chlorophyll-a concentration showed that the neural network model appear to provide the best estimates of chlorophyll-a concentration in Lake Erie using MODIS Aqua images.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","6.69 MB"]},{"key":"dc:title","label":"Title","values":["REMOTE SENSING OF WATER QUALITY IN LAKE ERIE USING MODIS IMAGERY DATA"]}]}],"canonical_facts":{"dc:contributor":["Merry, Carolyn"],"dc:creator":["Zhang, Li"],"dc:date":["2013-05-21"],"dc:description":["The Great Lakes, as the largest fresh surface water system on earth, greatly affects the quality of life and many aspects of the natural environment from weather and climate to wildlife and habitat. Lake Erie was declared as the first Great Lake that demonstrated a lake-wide eutrophic imbalance problem. Lake Erie has experienced increasingly frequent blooms of the toxic colonial cyanobacteria - Microcystis - in recent years. Lake Erie is also the Great Lake that is most subject to sediment loading. Continuing water quality monitoring in Lake Erie is needed to understand the complex variation of water quality and to support management decisions for water quality improvement.
This study investigated remote sensing techniques for quantifying chlorophyll-a concentrations and turbidity information for Lake Erie using MODIS imagery data. An accurate atmospheric correction is very important for remote sensing applications of imagery for water quality analysis, since the spectral energy from the water surface is much less than from the land surface. The existing four atmospheric correction methods were applied to MODIS imagery and evaluated for Lake Erie. The MUMM atmospheric correction method showed the best results for MODIS data of Lake Erie. The MUMM atmospheric correction method was chosen for developing models to estimate water quality in Lake Erie.
Remote sensing of water quality requires simultaneous or near-simultaneous collection of remotely sensed data and in situ ground truth data to develop and validate any model for quantifying water quality parameters using remote sensing images. Remote sensing water quality models derived using acquired ground truth data can be site specific, especially for turbid water bodies (Case 2 waters) where the bio-optical constituents vary considerably. This study evaluated the existing MODIS algorithms for chlorophyll-a concentration, including the revised OC2, the OC3M, and the Carder algorithms, combined with the most accurate atmospheric correction method. New models were developed and evaluated to improve the accuracy of estimation of the chlorophyll-a concentration and Secchi depth. These models included the general linear regression models, the Bayesian hierarchical regression model, and the nonlinear neural network model. The ground water quality measurements in Lake Erie used in this study included both chlorophyll-a concentration for measuring algae level and Secchi depth data to provide information on turbidity levels during the time period of May 7, 2002 to October 1, 2007, which were provided by Dr. David Culver (Department of Zoology, The Ohio State University).
This study has demonstrated the usefulness of MODIS data to quantify water quality in Lake Erie. A general linear regression model using the MODIS band ratio of 488/555 nm with a logarithm transformation can be used for estimating Secchi depth in Lake Erie. The comparisons of the three models for estimating chlorophyll-a concentration showed that the neural network model appear to provide the best estimates of chlorophyll-a concentration in Lake Erie using MODIS Aqua images.
"],"dc:format":["application/pdf","6.69 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=osu1357232811"],"dc:language":["English"],"dc:publisher":["The Ohio State University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Civil Engineering"],"dc:title":["REMOTE SENSING OF WATER QUALITY IN LAKE ERIE USING MODIS IMAGERY DATA"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The Ohio State University"]},"updated_at":"2026-07-24T03:36:08Z"}