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Remote Sens. 2017, 9(6), 556; doi:10.3390/rs9060556

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

1
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
2
Faculty of Engineering, Alexandria University, Lotfy El-Sied St. Off Gamal Abd El-Naser-Alexandria, Alexandria 11432, Egypt
3
Faculty of Urban Innovation, Yokohama National University, Tokiwadai 79-5, Hodogaya, Yokohama, Kanagawa 240-8501, Japan
4
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
5
Graduate School of Interdisciplinary Research, University of Yamanashi, 4-4-37 Takeda, Kofu, Yamanashi 400-8510, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Xiaofeng Li
Received: 14 April 2017 / Revised: 22 May 2017 / Accepted: 31 May 2017 / Published: 3 June 2017
(This article belongs to the Section Ocean Remote Sensing)
View Full-Text   |   Download PDF [3640 KB, uploaded 3 June 2017]   |  

Abstract

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 0.57, RMSE = 36.5 mg m−3). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura. View Full-Text
Keywords: bio-optical model; Red-NIR algorithm; Case 2 waters; inland lakes; MERIS; look-up table; spectral matching bio-optical model; Red-NIR algorithm; Case 2 waters; inland lakes; MERIS; look-up table; spectral matching
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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

Salem, S.I.; Higa, H.; Kim, H.; Kazuhiro, K.; Kobayashi, H.; Oki, K.; Oki, T. Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data. Remote Sens. 2017, 9, 556.

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