Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf
Colored Dissolved Organic Matter, a pivotal component of aquatic biogeochemical cycles, plays a critical role in regulating water quality and ecosystem functionality. This study provides the first comprehensive assessment of CDOM dynamics in the Persian Gulf's industrialized coastal waters, foc...
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001803 |
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| author | Bonyad Ahmadi Mehdi Gholamalifard Seyed Mahmoud Ghasempouri Tiit Kutser |
| author_facet | Bonyad Ahmadi Mehdi Gholamalifard Seyed Mahmoud Ghasempouri Tiit Kutser |
| author_sort | Bonyad Ahmadi |
| collection | DOAJ |
| description | Colored Dissolved Organic Matter, a pivotal component of aquatic biogeochemical cycles, plays a critical role in regulating water quality and ecosystem functionality. This study provides the first comprehensive assessment of CDOM dynamics in the Persian Gulf's industrialized coastal waters, focusing on the Pars Special Economic Energy Zone (PSEEZ)—a global energy epicenter and the world's largest natural gas reserve. Seasonal field campaigns conducted in 2023 acquired 199 in situ samples stratified across four seasons (Spring: n = 62, Summer: n = 18, Fall: n = 55, Winter: n = 64) using a CTD-integrated Cyclops-7 fluorometer. Sampling intervals were methodologically synchronized with satellite overpasses (±3 h) to minimize temporal discrepancies between ground-truth measurements and remotely sensed data, thereby ensuring spatiotemporal coherence essential for robust algorithm calibration and validation. Contrary to expectations, CDOM concentrations in petrochemical-influenced areas (e.g., stations P7: 0.29 ppb, P13: 0.35 ppb) were markedly lower than in natural mangrove ecosystems (stations N13: 19.61 ppb, NA2: 12.91 ppb), underscoring the antagonistic effects of industrial pollutants on organic matter stability. Initial CDOM retrieval algorithms yielded suboptimal accuracy (MAE = 1.16, RMSLE = 1.2). A regionally tuned band ratio algorithm improved performance by 27 % (MAE = 0.85) and 22 % (RMSLE = 0.94). Machine learning models further enhanced retrievals, with the Mixture Density Network (MDN) emerging as the superior framework. The MDN achieved an RMSLE of 0.47 (17.5 % improvement over MLP, 14.5 % over SVM) and reduced systematic bias (SSPB) by 26.12 units compared to Bayesian Ridge Regression (BRR), outperforming conventional models like SVM (MAE = 0.61, RMSLE = 0.55). While the MDN exhibited marginally higher absolute error (MAE = 0.53) than deterministic models, its probabilistic architecture uniquely addressed the Persian Gulf's optical complexity, characterized by overlapping signals from SGD-driven organics, hydrocarbon plumes, and sediment resuspension. This study establishes MDN as a transformative tool for CDOM retrieval in optically heterogeneous, anthropogenically stressed waters, while advocating for regionally adaptive frameworks to advance precision water quality monitoring in critical marine ecosystems. |
| format | Article |
| id | doaj-art-2b5c4e76eb114817a096e6f6d02c9356 |
| institution | OA Journals |
| issn | 1574-9541 |
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| publishDate | 2025-11-01 |
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| spelling | doaj-art-2b5c4e76eb114817a096e6f6d02c93562025-08-20T02:34:40ZengElsevierEcological Informatics1574-95412025-11-018910317110.1016/j.ecoinf.2025.103171Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian GulfBonyad Ahmadi0Mehdi Gholamalifard1Seyed Mahmoud Ghasempouri2Tiit Kutser3Department of Environment, Faculty of Natural Resources & Marine Sciences (FNRMS), Tarbiat Modares University (TMU), Noor 46414-356, IranDepartment of Environment, Faculty of Natural Resources & Marine Sciences (FNRMS), Tarbiat Modares University (TMU), Noor 46414-356, Iran; Corresponding author.Department of Environment, Faculty of Natural Resources & Marine Sciences (FNRMS), Tarbiat Modares University (TMU), Noor 46414-356, IranEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaColored Dissolved Organic Matter, a pivotal component of aquatic biogeochemical cycles, plays a critical role in regulating water quality and ecosystem functionality. This study provides the first comprehensive assessment of CDOM dynamics in the Persian Gulf's industrialized coastal waters, focusing on the Pars Special Economic Energy Zone (PSEEZ)—a global energy epicenter and the world's largest natural gas reserve. Seasonal field campaigns conducted in 2023 acquired 199 in situ samples stratified across four seasons (Spring: n = 62, Summer: n = 18, Fall: n = 55, Winter: n = 64) using a CTD-integrated Cyclops-7 fluorometer. Sampling intervals were methodologically synchronized with satellite overpasses (±3 h) to minimize temporal discrepancies between ground-truth measurements and remotely sensed data, thereby ensuring spatiotemporal coherence essential for robust algorithm calibration and validation. Contrary to expectations, CDOM concentrations in petrochemical-influenced areas (e.g., stations P7: 0.29 ppb, P13: 0.35 ppb) were markedly lower than in natural mangrove ecosystems (stations N13: 19.61 ppb, NA2: 12.91 ppb), underscoring the antagonistic effects of industrial pollutants on organic matter stability. Initial CDOM retrieval algorithms yielded suboptimal accuracy (MAE = 1.16, RMSLE = 1.2). A regionally tuned band ratio algorithm improved performance by 27 % (MAE = 0.85) and 22 % (RMSLE = 0.94). Machine learning models further enhanced retrievals, with the Mixture Density Network (MDN) emerging as the superior framework. The MDN achieved an RMSLE of 0.47 (17.5 % improvement over MLP, 14.5 % over SVM) and reduced systematic bias (SSPB) by 26.12 units compared to Bayesian Ridge Regression (BRR), outperforming conventional models like SVM (MAE = 0.61, RMSLE = 0.55). While the MDN exhibited marginally higher absolute error (MAE = 0.53) than deterministic models, its probabilistic architecture uniquely addressed the Persian Gulf's optical complexity, characterized by overlapping signals from SGD-driven organics, hydrocarbon plumes, and sediment resuspension. This study establishes MDN as a transformative tool for CDOM retrieval in optically heterogeneous, anthropogenically stressed waters, while advocating for regionally adaptive frameworks to advance precision water quality monitoring in critical marine ecosystems.http://www.sciencedirect.com/science/article/pii/S1574954125001803Remote sensingSentinel-2CTDMachine learningPars special economic energy zone (PSEEZ)Persian gulf |
| spellingShingle | Bonyad Ahmadi Mehdi Gholamalifard Seyed Mahmoud Ghasempouri Tiit Kutser Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf Ecological Informatics Remote sensing Sentinel-2 CTD Machine learning Pars special economic energy zone (PSEEZ) Persian gulf |
| title | Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf |
| title_full | Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf |
| title_fullStr | Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf |
| title_full_unstemmed | Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf |
| title_short | Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf |
| title_sort | comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter cdom from sentinel 2 msi images in the coastal waters of the persian gulf |
| topic | Remote sensing Sentinel-2 CTD Machine learning Pars special economic energy zone (PSEEZ) Persian gulf |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125001803 |
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