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Remote Sens. 2018, 10(5), 695; https://doi.org/10.3390/rs10050695

A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour

1
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
2
17 The Glebe, Thorverton, Exeter EX55LS, UK
3
EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany
4
School of Mathematics, University of Edinburgh, 5605 JCMB, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
5
Centrica, Millstream, Maidenhead Road, Windsor SL4 5GD, UK
6
College of Life and Environmental Sciences, University of Exeter, Penryn TR10 9FE, UK
*
Author to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 20 April 2018 / Accepted: 27 April 2018 / Published: 2 May 2018
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Abstract

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data. View Full-Text
Keywords: uncertainty; satellite; chlorophyll; statistics; bias; matchups; GAMLSS uncertainty; satellite; chlorophyll; statistics; bias; matchups; GAMLSS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Land, P.E.; Bailey, T.C.; Taberner, M.; Pardo, S.; Sathyendranath, S.; Nejabati Zenouz, K.; Brammall, V.; Shutler, J.D.; Quartly, G.D. A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour. Remote Sens. 2018, 10, 695.

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