Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

The Mar Menor coastal lagoon in southeastern Spain has experienced a decline in water quality due to increased nutrient input, leading to the eutrophication of the lagoon and the occurrence of microalgal and macroalgal blooms. This study analyzes the macroalgal bloom that occurred in the lagoon during the spring-summer of 2022. A set of machine learning techniques are applied to Sentinel-2 satellite imagery in order to obtain indicators of the presence of macroalgae in specific locations within the lagoon. This is supported by in situ observations of the blooming process in different areas of the Mar Menor. Our methodology successfully identifies the macroalgal bloom locations (accuracies above 98%, and Matthew's Correlation Coefficients above 78% in all cases), and provides a probabilistic approach to understand the likelihood of occurrence of this event in given pixels. The analysis also identifies the key parameters contributing to the classification of pixels as algae, which could be used to develop future algorithms for detecting macroalgal blooms. This information can be used by environmental managers to implement early warning and mitigation strategies to prevent water quality deterioration in the lagoon. The usefulness of satellite observations for ecological and crisis management at local and regional scales is also highlighted.

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Medina-Lopez E. Navarro G. Santos-Echeandia J. Bernardez P. y Caballero I. Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2. MDPI, 2023. https://doi.org/10.3390/rs15051208

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Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 20-01-2025
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Identificador de los metadatos 17645b5b-9efd-538a-b80a-046a6fe4a91c
Idioma de los metadatos Español
Temáticas (NTI-RISP)
Categoría del conjunto de alto valor (HVD) Observación de la Tierra y medio ambiente
Categoría temática ISO 19115 biota
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Nombre del autor Medina-Lopez, E., Navarro, G., Santos-Echeandia, J., Bernardez, P. y Caballero, I.
Nombre del editor MDPI
Identificador alternativo DOI: 10.3390/rs15051208
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Email del autor isabel.caballero@icman.csic.es
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