Title: An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications
Citation: REMOTE SENSING OF ENVIRONMENT vol. 203 p. 152-161
Publication Year: 2017
JRC N°: JRC106495
ISSN: 0034-4257
URI: http://www.sciencedirect.com/science/article/pii/S0034425717301396?via%3Dihub
DOI: 10.1016/j.rse.2017.03.036
Type: Articles in periodicals and books
Abstract: The Ocean Colour Climate Change Initiative (OC-CCI) has produced a climate-quality, error characterised, dataset of ocean-colour products (a designated Essential Climate Variable or ‘ECV’). The OC-CCI project uses an optical classification scheme based on fuzzy logic (Moore et al. 2001), to assign product uncertainties on a pixel-by-pixel basis. In this study we show that the pre-existing set of optical water classes derived from in-water remote-sensing reflectance data are insufficient to classify all Rrs spectra present in satellite data at the global scale, particularly in oligotrophic regions. We generate a new set of optical water classes from millions of satellite-derived ocean-colour spectra, providing an improvement in distribution of cumulative class membership values. The use of these classes for uncertainty assignment are demonstrated for chlorophyll-a, utilising a large in situ database of measurements. In addition to being used for uncertainty assignment, performance of multiple chlorophyll algorithms is assessed within each of the classes and a method for blending algorithms while avoiding sharp boundaries, in order to improve final product quality, using class membership is illustrated.
JRC Directorate:Sustainable Resources

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