Functional regression on remote sensing data in oceanography

Acar-Denizli, Nihan; Delicado, Pedro; Başarır, Gülay; Caballero, Isabel

doi:10.1007/s10651-018-0405-7

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Functional regression on remote sensing data in oceanography

2018
By Acar-Denizli, Nihan ; Delicado, Pedro ; Başarır, Gülay ; ...
Source: Environmental and Ecological Statistics, 25(2), 277-304

[PDF-1.48 MB]

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Journal Title:

Environmental and Ecological Statistics
Personal Author:

Acar-Denizli, Nihan ; Delicado, Pedro ; Başarır, Gülay ; Caballero, Isabel

Acar-Denizli, Nihan ; Delicado, Pedro ; Başarır, Gülay ; Caballero, Isabel Less -
NOAA Program & Office:

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) Less -
Description:

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.
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Oceanography Remote Sensing Statistics
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Environmental and Ecological Statistics, 25(2), 277-304
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https://doi.org/10.1007/s10651-018-0405-7
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urn:sha-512:adbc4b961000315b9019afdf8501eff8886a8ce5ef13d9147dff5762477834c56b518f24e08ce69593466b94f90d7d8e58d33855e723ba5bdadd13d7ad3c533a
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Functional regression on remote sensing data in oceanography

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