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Remote Sens. 2017, 9(5), 420; doi:10.3390/rs9050420

An Optical Classification Tool for Global Lake Waters

1
Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
2
Vrije Universiteit Amsterdam, Institute for Environmental Studies (VU-IVM), De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands
3
Brockmann Consult GmbH, Max-Planck-Str.2, 21502 Geesthacht, Germany
4
Image Processing Laboratory, University of Valencia, P.O. Box 22085, E-46071 Valencia, Spain
5
Water Insight, Marijkeweg 22, 6709 PG Wageningen, The Netherlands
6
University of New Hampshire, 8 College Road, OPAL/Morse Hall, Durham, NH 03824, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Yunlin Zhang, Claudia Giardino, Linhai Li and Prasad S. Thenkabail
Received: 28 February 2017 / Revised: 11 April 2017 / Accepted: 23 April 2017 / Published: 29 April 2017
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
View Full-Text   |   Download PDF [3856 KB, uploaded 11 May 2017]   |  

Abstract

Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes. View Full-Text
Keywords: lakes; reflectance; classification; OWT; atmospheric correction; MERIS; OLCI; water quality lakes; reflectance; classification; OWT; atmospheric correction; MERIS; OLCI; water quality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Eleveld, M.A.; Ruescas, A.B.; Hommersom, A.; Moore, T.S.; Peters, S.W.M.; Brockmann, C. An Optical Classification Tool for Global Lake Waters. Remote Sens. 2017, 9, 420.

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