Environmental and Ecological Statistics

, Volume 25, Issue 2, pp 277–304 | Cite as

Functional regression on remote sensing data in oceanography

  • Nihan Acar-DenizliEmail author
  • Pedro Delicado
  • Gülay Başarır
  • Isabel Caballero


The aim of this study is to propose the use of a functional data analysis approach as an alternative to the classical statistical methods most commonly used in oceanography and water quality management. In particular we consider the prediction of total suspended solids (TSS) based on remote sensing (RS) data. For this purpose several functional linear regression models and classical non-functional regression models are applied to 10 years of RS data obtained from medium resolution imaging spectrometer sensor to predict the TSS concentration in the coastal zone of the Guadalquivir estuary. The results of functional and classical approaches are compared in terms of their mean square prediction error values and the superiority of the functional models is established. A simulation study has been designed in order to support these findings and to determine the best prediction model for the TSS parameter in more general contexts.


Exponential regression models Functional linear regression models Functional partial least squares Functional principal components Remote sensing data 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsMimar Sinan Güzel Sanatlar ÜniversitesiIstanbulTurkey
  2. 2.Department of Statistics and Operational ResearchUniversitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.Department of Ecology and Coastal ManagementInstitute of Marine Sciences of Andalusia (ICMAN), National Research Council (CSIC)Puerto RealSpain
  4. 4.National Centers for Coastal Ocean ScienceNOAA National Ocean ServiceSilver SpringUSA

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