Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60014
Title: Chlorophyll-A estimation from remote sensing data of sea surface temperature and aerosol optical thickness through a shallow neural network
Authors: Rodríguez Esparragón, Dionisio 
Marrero Betancort, Nerea
Marcello, Javier 
Hernandez-Leon, Santiago 
UNESCO Clasification: 250616 Teledetección (Geología)
251001 Oceanografía biológica
Keywords: Ocean temperature
Neural networks
Sea surface
Temperature sensors
Time series analysis
Correlation
Issue Date: 2019
Project: Análisis de Series Temporales de Parámetros Atmosféricos de Teledetección Por 
Abstract: The oceans cover most of the Earth surface, being therefore essential elements of the environmental balance of our planet. In this sense, the prediction of global change scenarios that may affect them is an issue of high scientific and social relevance. One of the elements that indicates the quality of the water is the concentration of Chlorophyll-a. It is well known that Chlorophyll-a is related to the sea surface temperature and other variables such as the presence of nutrients and wind. All of them have been monitored with remote sensing satellites for more than a decade ago. Thus, researchers have available temporary series of these data. In this work, the prediction of Chlorophyll-a concentration is addressed from data on sea surface temperature and the aerosol optical thickness. For this, a shallow neuronal network is designed and trained, whose performance is contrasted with other approaches. The results show that the tested methodology can be used to model predictors with the discussed climate variables.
URI: http://hdl.handle.net/10553/60014
DOI: 10.1109/CEAP.2019.8883506
Source: 2019 International Conference on Engineering Applications, ICEA 2019 - Proceedings (8883506)
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