期刊
REMOTE SENSING OF ENVIRONMENT
卷 160, 期 -, 页码 235-251出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2015.01.023
关键词
Optical properties; Coastal regions; Classification; SeaWiFS; Ocean color
资金
- GlobCoast project - French Agence Nationale de la Recherche [ANR 2011 BS56 018 01]
Coastal regions are a resource for societies while being under severe pressure from a variety of factors. They also show a large diversity of optical characteristics, and the potential to optically classify these waters and distinguish similarities between regions is a fruitful application for satellite ocean color. Recognizing the specificities and complexity of coastal waters in terms of optical properties, a training data set is assembled for coastal regions and marginal seas using full resolution SeaWiFS global remote sensing reflectance RRs data that maximize the geographic coverage and seasonal sampling of the domain. An unsupervised clustering technique is operated on the training data set to derive a set of 16 classes that cover conditions from very turbid to oligotrophic. When applied to a global seven-year SeaWiFS data set, this set of optical water types allows an efficient classification of coastal regions, marginal seas and large inland water bodies. Classes associated with more turbid conditions show relative dominance close to shore and in the mid-latitudes. A geographic partition of the global coastal ocean serves to distinguish general optical similarities between regions. The local optical variability is quantified by the number of classes selected as dominant across the period, averaging 5.2 classes if the cases accounting for 90% of the data days are considered. Optical diversity is more specifically analyzed with a Shannon index computed with the class memberships. Regions with low optical diversity are the most turbid waters as well as closed seas and inland water bodies. Oligotrophic waters also show a relatively low diversity, while intermediate regions between coastal domain and open ocean are associated with the highest diversity, which has interesting connections with ecological features. (C) 2015 The Authors. Published by Elsevier Inc.
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