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Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea
•Satellite-based DO model based on multiple regression analysis.•Strong correlation between water temperature and dissolved oxygen was found.•Spatio-temporal structures of coastal surface DO in high resolution.•DO change between 2003 and 2012 possibly due to dike construction.•Potential capability o...
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Published in: | Environment international 2020-01, Vol.134, p.105301, Article 105301 |
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creator | Kim, Yong Hoon Son, Seunghyun Kim, Hae-Cheol Kim, Bora Park, Young-Gyu Nam, Jungho Ryu, Jongseong |
description | •Satellite-based DO model based on multiple regression analysis.•Strong correlation between water temperature and dissolved oxygen was found.•Spatio-temporal structures of coastal surface DO in high resolution.•DO change between 2003 and 2012 possibly due to dike construction.•Potential capability of satellite remote sensing in estimating in situ DO.
Dissolved oxygen (DO) is one of the critical parameters representing water quality in coastal environments. However, it is labor- and cost-intensive to maintain monitoring systems of DO since in situ measurements of DO are needed in high spatial and temporal resolution to establish proper management plans of coastal regions. In this study, we applied statistical analyses between long-term monitoring datasets and satellite remote sensing datasets in the eastern coastal region of the Yellow Sea. Pearson correlation analysis of long-term water quality monitoring datasets shows that water temperature and DO are highly correlated. Stepwise multiple regression analysis among DO and satellite-derived environmental variables shows that the in situ DO can be estimated by the combination of the present sea surface temperature (SST), the chlorophyll-a, and the SST in the month prior. The high skill score of our proposed model to derive DO is validated by two error measures, the Absolute Relative Error, 1-ARE (89.2%), and Index of Agreement, IOA (78.6%). By applying the developed model to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) products, spatial and temporal changes in satellite-derived DO can be observed in Saemangeum offshore in the Yellow Sea. The analysis results show that there is a significant decrease in estimated DO between summer of 2003 versus 2012 indicating summer coastal deoxygenation due probably to the Saemangeum reclamation. This study shows the potential capability of satellite remote sensing in monitoring in situ DO in both high temporal and spatial resolution, which will be beneficial for effective and efficient management of coastal environments. |
doi_str_mv | 10.1016/j.envint.2019.105301 |
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Dissolved oxygen (DO) is one of the critical parameters representing water quality in coastal environments. However, it is labor- and cost-intensive to maintain monitoring systems of DO since in situ measurements of DO are needed in high spatial and temporal resolution to establish proper management plans of coastal regions. In this study, we applied statistical analyses between long-term monitoring datasets and satellite remote sensing datasets in the eastern coastal region of the Yellow Sea. Pearson correlation analysis of long-term water quality monitoring datasets shows that water temperature and DO are highly correlated. Stepwise multiple regression analysis among DO and satellite-derived environmental variables shows that the in situ DO can be estimated by the combination of the present sea surface temperature (SST), the chlorophyll-a, and the SST in the month prior. The high skill score of our proposed model to derive DO is validated by two error measures, the Absolute Relative Error, 1-ARE (89.2%), and Index of Agreement, IOA (78.6%). By applying the developed model to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) products, spatial and temporal changes in satellite-derived DO can be observed in Saemangeum offshore in the Yellow Sea. The analysis results show that there is a significant decrease in estimated DO between summer of 2003 versus 2012 indicating summer coastal deoxygenation due probably to the Saemangeum reclamation. This study shows the potential capability of satellite remote sensing in monitoring in situ DO in both high temporal and spatial resolution, which will be beneficial for effective and efficient management of coastal environments.</description><identifier>ISSN: 0160-4120</identifier><identifier>EISSN: 1873-6750</identifier><identifier>DOI: 10.1016/j.envint.2019.105301</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Dissolved oxygen ; Multiple regression ; Remote sensing ; Satellite ; Yellow Sea</subject><ispartof>Environment international, 2020-01, Vol.134, p.105301, Article 105301</ispartof><rights>2019 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-585bdf5d609a1c5ad8c560584c412734bf377276d831ffd321173783641b20773</citedby><cites>FETCH-LOGICAL-c451t-585bdf5d609a1c5ad8c560584c412734bf377276d831ffd321173783641b20773</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Kim, Yong Hoon</creatorcontrib><creatorcontrib>Son, Seunghyun</creatorcontrib><creatorcontrib>Kim, Hae-Cheol</creatorcontrib><creatorcontrib>Kim, Bora</creatorcontrib><creatorcontrib>Park, Young-Gyu</creatorcontrib><creatorcontrib>Nam, Jungho</creatorcontrib><creatorcontrib>Ryu, Jongseong</creatorcontrib><title>Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea</title><title>Environment international</title><description>•Satellite-based DO model based on multiple regression analysis.•Strong correlation between water temperature and dissolved oxygen was found.•Spatio-temporal structures of coastal surface DO in high resolution.•DO change between 2003 and 2012 possibly due to dike construction.•Potential capability of satellite remote sensing in estimating in situ DO.
Dissolved oxygen (DO) is one of the critical parameters representing water quality in coastal environments. However, it is labor- and cost-intensive to maintain monitoring systems of DO since in situ measurements of DO are needed in high spatial and temporal resolution to establish proper management plans of coastal regions. In this study, we applied statistical analyses between long-term monitoring datasets and satellite remote sensing datasets in the eastern coastal region of the Yellow Sea. Pearson correlation analysis of long-term water quality monitoring datasets shows that water temperature and DO are highly correlated. Stepwise multiple regression analysis among DO and satellite-derived environmental variables shows that the in situ DO can be estimated by the combination of the present sea surface temperature (SST), the chlorophyll-a, and the SST in the month prior. The high skill score of our proposed model to derive DO is validated by two error measures, the Absolute Relative Error, 1-ARE (89.2%), and Index of Agreement, IOA (78.6%). By applying the developed model to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) products, spatial and temporal changes in satellite-derived DO can be observed in Saemangeum offshore in the Yellow Sea. The analysis results show that there is a significant decrease in estimated DO between summer of 2003 versus 2012 indicating summer coastal deoxygenation due probably to the Saemangeum reclamation. This study shows the potential capability of satellite remote sensing in monitoring in situ DO in both high temporal and spatial resolution, which will be beneficial for effective and efficient management of coastal environments.</description><subject>Dissolved oxygen</subject><subject>Multiple regression</subject><subject>Remote sensing</subject><subject>Satellite</subject><subject>Yellow Sea</subject><issn>0160-4120</issn><issn>1873-6750</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kc-KFDEQxoMoOK77Bh7yAj0mnX_dHoRhUXdxwYueQzqpDDX0dIYkjs7FZ9-MvXj0VFRR34_66iPkHWdbzrh-f9jCcsalbnvGxzZSgvEXZMMHIzptFHtJNm2NdZL37DV5U8qBMdbLQW3In93pNKN3FdNCU6TFVZhnrEAzHFMrBZaCy57iQo9pwZrytQtYSprPEGj6fdnDQs8uo5uwKRHKB7qj3pUmrj_DhcaUqU-uVDfTX42fy5X2NWVwb8mr6OYCt8_1hvz4_On73X33-O3Lw93usfNS8dqpQU0hqqDZ6LhXLgxeaaYG6ZslI-QUhTG90WEQPMYges6NMIPQkk89M0bckIeVG5I72FPGo8sXmxzav4OU99blin4GG3o-iEl7Hj1IJv2oo2wfHTWMvRaaN5ZcWT6nUjLEfzzO7DUPe7BrHvaah13zaLKPqwyazzNCtsUjLB4CZvC1HYL_BzwBBjmWaQ</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Kim, Yong Hoon</creator><creator>Son, Seunghyun</creator><creator>Kim, Hae-Cheol</creator><creator>Kim, Bora</creator><creator>Park, Young-Gyu</creator><creator>Nam, Jungho</creator><creator>Ryu, Jongseong</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>202001</creationdate><title>Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea</title><author>Kim, Yong Hoon ; Son, Seunghyun ; Kim, Hae-Cheol ; Kim, Bora ; Park, Young-Gyu ; Nam, Jungho ; Ryu, Jongseong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-585bdf5d609a1c5ad8c560584c412734bf377276d831ffd321173783641b20773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Dissolved oxygen</topic><topic>Multiple regression</topic><topic>Remote sensing</topic><topic>Satellite</topic><topic>Yellow Sea</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Yong Hoon</creatorcontrib><creatorcontrib>Son, Seunghyun</creatorcontrib><creatorcontrib>Kim, Hae-Cheol</creatorcontrib><creatorcontrib>Kim, Bora</creatorcontrib><creatorcontrib>Park, Young-Gyu</creatorcontrib><creatorcontrib>Nam, Jungho</creatorcontrib><creatorcontrib>Ryu, Jongseong</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Environment international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Yong Hoon</au><au>Son, Seunghyun</au><au>Kim, Hae-Cheol</au><au>Kim, Bora</au><au>Park, Young-Gyu</au><au>Nam, Jungho</au><au>Ryu, Jongseong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea</atitle><jtitle>Environment international</jtitle><date>2020-01</date><risdate>2020</risdate><volume>134</volume><spage>105301</spage><pages>105301-</pages><artnum>105301</artnum><issn>0160-4120</issn><eissn>1873-6750</eissn><abstract>•Satellite-based DO model based on multiple regression analysis.•Strong correlation between water temperature and dissolved oxygen was found.•Spatio-temporal structures of coastal surface DO in high resolution.•DO change between 2003 and 2012 possibly due to dike construction.•Potential capability of satellite remote sensing in estimating in situ DO.
Dissolved oxygen (DO) is one of the critical parameters representing water quality in coastal environments. However, it is labor- and cost-intensive to maintain monitoring systems of DO since in situ measurements of DO are needed in high spatial and temporal resolution to establish proper management plans of coastal regions. In this study, we applied statistical analyses between long-term monitoring datasets and satellite remote sensing datasets in the eastern coastal region of the Yellow Sea. Pearson correlation analysis of long-term water quality monitoring datasets shows that water temperature and DO are highly correlated. Stepwise multiple regression analysis among DO and satellite-derived environmental variables shows that the in situ DO can be estimated by the combination of the present sea surface temperature (SST), the chlorophyll-a, and the SST in the month prior. The high skill score of our proposed model to derive DO is validated by two error measures, the Absolute Relative Error, 1-ARE (89.2%), and Index of Agreement, IOA (78.6%). By applying the developed model to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) products, spatial and temporal changes in satellite-derived DO can be observed in Saemangeum offshore in the Yellow Sea. The analysis results show that there is a significant decrease in estimated DO between summer of 2003 versus 2012 indicating summer coastal deoxygenation due probably to the Saemangeum reclamation. This study shows the potential capability of satellite remote sensing in monitoring in situ DO in both high temporal and spatial resolution, which will be beneficial for effective and efficient management of coastal environments.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.envint.2019.105301</doi><oa>free_for_read</oa></addata></record> |
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subjects | Dissolved oxygen Multiple regression Remote sensing Satellite Yellow Sea |
title | Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea |
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