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Remote Sens. 2014, 6(12), 11689-11707; doi:10.3390/rs61211689

Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir

1
Remote Sensing Division, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil
2
Image Processing Division, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil
3
Associate Laboratory of Sensors and Materials, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil
*
Author to whom correspondence should be addressed.
Received: 3 June 2014 / Revised: 13 November 2014 / Accepted: 19 November 2014 / Published: 25 November 2014
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Abstract

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms. View Full-Text
Keywords: chlorophyll-a; remote sensing reflectance; bio-optical models; MERIS; OLCI chlorophyll-a; remote sensing reflectance; bio-optical models; MERIS; OLCI
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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

Augusto-Silva, P.B.; Ogashawara, I.; Barbosa, C.C.F.; de Carvalho, L.A.S.; Jorge, D.S.F.; Fornari, C.I.; Stech, J.L. Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir. Remote Sens. 2014, 6, 11689-11707.

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