期刊
REMOTE SENSING OF ENVIRONMENT
卷 203, 期 -, 页码 152-161出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2017.03.036
关键词
Ocean colour; Essential Climate Variable; ESA; Climate Change Initiative; Chlorophyll; Fuzzy classification; Optical water type; Uncertainties
资金
- NERC [nceo020006, pml010008] Funding Source: UKRI
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 R-rs 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. (C) 2017 Elsevier Inc. All rights reserved.
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