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Functional linear regression models for scalar responses on remote sensing data: an application to Oceanography

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Remote Sensing (RS) data obtained from satellites are a type of spectral data which consist of reflectance values recorded at different wavelengths. This type of data can be considered as a functional data due to the continous structure of the spectrum. The aim of this study is to propose Functional Linear Regression Models (FLRMs) to analyze the turbidity in the coastal zone of Guadalquivir estuary from satellite data. With this aim different types of FLRMs for scalar response have been used to predict the amount of Total Suspended Solids (TSS) on RS data and their results have been compared.

Keywords

  • Total Suspend Solid
  • Total Suspended Matter
  • Remote Sensing Data
  • Functional Data Analysis
  • Total Suspend Solid Concentration

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Nihan Acar-Denizli .

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Acar-Denizli, N., Delicado, P., Başarır, G., Caballero, I. (2017). Functional linear regression models for scalar responses on remote sensing data: an application to Oceanography. In: Aneiros, G., G. Bongiorno, E., Cao, R., Vieu, P. (eds) Functional Statistics and Related Fields. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55846-2_3

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