Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea
Chlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors inc...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1412 |
| _version_ | 1827717699108601856 |
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| author | Hamid Mohebzadeh Junho Yeom Taesam Lee |
| author_facet | Hamid Mohebzadeh Junho Yeom Taesam Lee |
| author_sort | Hamid Mohebzadeh |
| collection | DOAJ |
| description | Chlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), especially in monitoring the Chl-a concentrations in coastal regions. To improve its spatial resolution, downscaling techniques have been suggested with polynomial regression models. Nevertheless, polynomial regression has some restrictions, including sensitivity to outliers and fixed mathematical forms. Therefore, the current study applied genetic programming (GP) for downscaling Chl-a. The proposed GP model in the current study was compared with multiple polynomial regression (MPR) to different degrees (2<sup>nd</sup>-, 3<sup>rd</sup>-, and 4<sup>th</sup>-degree) to illustrate their performances for downscaling MODIS Chl-a. The obtained results indicate that GP with R<sup>2</sup> = 0.927 and RMSE = 0.1642 on the winter day and R<sup>2</sup> = 0.763 and RMSE = 0.5274 on the summer day provides higher accuracy on both winter and summer days than all the applied MPR models because the GP model can automatically produce appropriate mathematical equations without any restrictions. In addition, the GP model is the least sensitive model to the changes in the input parameters. The improved downscaling data provide better information to monitor the status of oceanic and coastal marine ecosystems that are also critical for fisheries and fishing farming. |
| first_indexed | 2024-03-10T20:08:04Z |
| format | Article |
| id | doaj.art-07e3de57f0b44290972e195ced0593be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:08:04Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-07e3de57f0b44290972e195ced0593be2023-11-19T23:07:06ZengMDPI AGRemote Sensing2072-42922020-04-01129141210.3390/rs12091412Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South KoreaHamid Mohebzadeh0Junho Yeom1Taesam Lee2Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam 52828, KoreaDepartment of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam 52828, KoreaDepartment of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam 52828, KoreaChlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), especially in monitoring the Chl-a concentrations in coastal regions. To improve its spatial resolution, downscaling techniques have been suggested with polynomial regression models. Nevertheless, polynomial regression has some restrictions, including sensitivity to outliers and fixed mathematical forms. Therefore, the current study applied genetic programming (GP) for downscaling Chl-a. The proposed GP model in the current study was compared with multiple polynomial regression (MPR) to different degrees (2<sup>nd</sup>-, 3<sup>rd</sup>-, and 4<sup>th</sup>-degree) to illustrate their performances for downscaling MODIS Chl-a. The obtained results indicate that GP with R<sup>2</sup> = 0.927 and RMSE = 0.1642 on the winter day and R<sup>2</sup> = 0.763 and RMSE = 0.5274 on the summer day provides higher accuracy on both winter and summer days than all the applied MPR models because the GP model can automatically produce appropriate mathematical equations without any restrictions. In addition, the GP model is the least sensitive model to the changes in the input parameters. The improved downscaling data provide better information to monitor the status of oceanic and coastal marine ecosystems that are also critical for fisheries and fishing farming.https://www.mdpi.com/2072-4292/12/9/1412spatial downscalingMODIS chlorophyll-asentinel-2A MSImultiple polynomial regressiongenetic programming |
| spellingShingle | Hamid Mohebzadeh Junho Yeom Taesam Lee Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea Remote Sensing spatial downscaling MODIS chlorophyll-a sentinel-2A MSI multiple polynomial regression genetic programming |
| title | Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea |
| title_full | Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea |
| title_fullStr | Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea |
| title_full_unstemmed | Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea |
| title_short | Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea |
| title_sort | spatial downscaling of modis chlorophyll a with genetic programming in south korea |
| topic | spatial downscaling MODIS chlorophyll-a sentinel-2A MSI multiple polynomial regression genetic programming |
| url | https://www.mdpi.com/2072-4292/12/9/1412 |
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