Optical types of inland and coastal waters
By E. Spyrakos, Ruth O’Donnell, P. Hunter, Claire Miller, M. Scott, Stefan G. H. Simis, C. Neil, C. Barbosa, C. Binding, S. Bradt and 15 more. 2017
279 citations recorded by the source corpus.
Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.
Subfields: Ecology, Oceanography
View Paper: https://www.semanticscholar.org/paper/af2e1084dbd87a9e70cdb1a98a13d759d8824506
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In Oceanography
Citations in the corpus
- Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach (2020, background): “Given various degrees in the sensitivity of Chl a algorithms to different water types, water-type retrieval algorithms have drawn major attention (Moore et al., 2001; Neil et al., 2019; Spyrakos et al., 2018; Vertucci and Likens, 1989).”