A new approach to monitor water quality in the Menor Sea (Spain) using satellite data and machine learning methods

The Menor sea is a coastal lagoon declared by the European Union as a sensitive area to eutrophication due to human activities. To control the deterioration of its water quality, it is necessary to monitor some parameters such as chlorophyll-a (chl-a), which indicates phytoplankton biomass in the water. In the study area, current efforts focus on in-situ measurements to estimate chl-a by means of a few permanent stations and seasonal oceanographic campaigns, however they are expensive and time consuming. In this work, we proposed a machine learning approach based on Sentinel-2 data to estimate chl-a content on the upper part of the water column. Random forest (rf), support vector machine (svmRadial), Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms were utilized under three feature selection scenarios, and several spectral indices were used in combination with Sentinel 2 bands. Rf, svmRadial and DNN performed better when all the available predictors were included in the models (RMSE = 0.82, 0.82 and 1.76 mg/m3 respectively), whereas ANN achieved better results under scenario c (principal components). Our results demonstrate the possibility to estimate chl-a concentration in a cost-effective manner and thereby provide near-real time information to monitor the water quality of the Menor sea, what can be of great interest for local authorities, tourism and fishing industry.

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Gómez D. Salvador P. Sanz J. y Casanova J.L. A new approach to monitor water quality in the Menor Sea (Spain) using satellite data and machine learning methods. Elsevier B.V., 2021. https://doi.org/10.1016/j.envpol.2021.117489

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Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 19-01-2025
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Identificador de los metadatos d8ae6b28-75a0-51e8-abf6-ae292c4fbe5d
Idioma de los metadatos Español
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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 Gómez, D., Salvador, P., Sanz J. y Casanova, J.L.
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Identificador alternativo DOI: 10.1016/j.envpol.2021.117489
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Email del autor diego@latuv.uva.es
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