Improving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning

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dc.contributor.authorYailymov, Bohdan
dc.contributor.authorKussul, Nataliia
dc.contributor.authorHenitsoi, Pavlo
dc.contributor.authorShelestov, Andrii
dc.date.accessioned2025-01-02T10:47:34Z
dc.date.available2025-01-02T10:47:34Z
dc.date.issued2024
dc.description.abstractThe objective of this study is to increase the spatial resolution of data on the level of chlorophyll-a in the Mediterranean Sea using satellite images and ground measurements. The goal of this study is to develop an information technology based on machine learning to create chlorophyll-a concentration maps with high spatial resolution for the pilot areas of the Mediterranean Sea. Traditional ground-based methods for measuring chlorophyll-a are time-consuming, expensive, and have limited spatial and temporal coverage. Therefore, satellite observations have become an effective tool for monitoring chlorophyll-a over large areas. Low spatial resolution satellite data such as GCOM-C/SGLI and Sentinel-3 OLCI allow measurements of chlorophyll-a concentration at the sea surface. However, these data have limited accuracy and spatial resolution, which creates challenges for monitoring local changes in coastal zones and small water areas. Tasks: to analyze available satellite data and ground-based measurements of chlorophyll-a for the Mediterranean Sea; to investigate the correlation between satellite data of different spatial resolutions and ground measurements; to select informative features from satellite data for building machine learning models; and to develop models for increasing the spatial resolution of chlorophyll-a based on regression and machine learning algorithms. Obtained results: information technology combining satellite data with ground measurements in the Google Earth Engine cloud platform is proposed; correlations between satellite measurements of chlorophyll-a and ground data are investigated; models based on Random Forest and Multilayer Perceptron with coefficients of determination up to 0.36 and correlation of 0.6 with test data are built; chlorophyll-a maps with a spatial resolution of 10 m are created for the pilot area near Cyprus. Conclusions. The developed information technology allows the effective combination of satellite data of different spatial resolutions and ground measurements to increase the accuracy and detail of chlorophyll-a maps in the Mediterranean Sea. Further research involves improving the preprocessing of satellite data, using more features, involving data from other regions, and applying more sophisticated machine learning models.
dc.description.sponsorshipHORIZON Europe project iMERMAID "Innovative solutions for Med-iterranean Ecosystem Remediation via Monitoring and Decontamination from Chemical Pollution", (contract number 101112824).
dc.format.pagerangeP. 52-65
dc.identifier.citationImproving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning / Bohdan Yailymov, Nataliia Kussul, Pavlo Henitsoi, Andrii Shelestov // Radioelectronic and Computer Systems. - 2024. - № 2 (110). - P. 52-65.
dc.identifier.doihttps://doi.org/10.32620/reks.2024.2.05
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/71511
dc.language.isoen
dc.publisherХАІ
dc.publisher.placeХарків
dc.relation.ispartofRadioelectronic and Computer Systems, № 2 (110), 2024
dc.subjectmachine learning
dc.subjectsatellite data
dc.subjectchlorophyll-a
dc.subjectcloud technologies
dc.subjectinformation technology
dc.subjectiMERMAID
dc.subject.udc528.854.04:004.85:581.132.1
dc.titleImproving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning
dc.typeArticle

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