dc.contributor.author | Land, PE | |
dc.contributor.author | Bailey, TC | |
dc.contributor.author | Taberner, M | |
dc.contributor.author | Pardo, S | |
dc.contributor.author | Sathyendranath, S | |
dc.contributor.author | Nejabati Zenouz, K | |
dc.contributor.author | Brammall, V | |
dc.contributor.author | Shutler, JD | |
dc.contributor.author | Quartley, G | |
dc.date.accessioned | 2018-05-08T11:04:39Z | |
dc.date.issued | 2018-05-02 | |
dc.description.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. | en_GB |
dc.description.sponsorship | This work was funded by NERC National Capability through the PML and University of Exeter
Research Collaboration Fund and the PML Research Programme and received no other external funding. | en_GB |
dc.identifier.citation | Vol. 10(5), 695 | en_GB |
dc.identifier.doi | 10.3390/rs10050695 | |
dc.identifier.uri | http://hdl.handle.net/10871/32749 | |
dc.language.iso | en | en_GB |
dc.publisher | MDPI | en_GB |
dc.relation.source | The following are available online at http://www.mdpi.com/2072-4292/10/5/695/s1,
software and data used to produce the results shown in this work. | en_GB |
dc.rights | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.subject | uncertainty | en_GB |
dc.subject | satellite | en_GB |
dc.subject | chlorophyll | en_GB |
dc.subject | statistics | en_GB |
dc.subject | bias | en_GB |
dc.subject | matchups | en_GB |
dc.subject | GAMLSS | en_GB |
dc.title | A statistical modeling framework for characterising uncertainty in large datasets: application to ocean colour | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-05-08T11:04:39Z | |
dc.identifier.issn | 2072-4292 | |
dc.description | This is the final version of the article. Available from the publisher via the DOI in this record. | en_GB |
dc.identifier.journal | Remote Sensing | en_GB |