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Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

Department of Civil Engineering, Federal University of Pernambuco, 50670-901 Recife, Brazil
Géosciences Environnement Toulouse (GET), Unité Mixte de Recherche 5563, IRD/CNRS/Université Toulouse III, 31400 Toulouse, France
Hydraulic Research Institute, Federal University of Rio Grande do Sul, CP 15029 Porto Alegre, Brazil
Center for Technology, Federal University of Alagoas, 57072-970 Maceió, Brazil
Author to whom correspondence should be addressed.
Academic Editors: Yunlin Zhang, Claudia Giardino, Linhai Li, Deepak R. Mishra and Prasad S. Thenkabail
Remote Sens. 2017, 9(6), 516;
Received: 27 February 2017 / Revised: 15 May 2017 / Accepted: 19 May 2017 / Published: 24 May 2017
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Remote estimation of chlorophyll-a in turbid and productive estuaries is difficult due to the optical complexity of Case 2 waters. Although recent advances have been obtained with the use of empirical approaches for estimating chlorophyll-a in these environments, the understanding of the relationship between spectral reflectance and chlorophyll-a is based mainly on temperate and subtropical estuarine systems. The potential to apply standard NIR-Red models to productive tropical estuaries remains underexplored. Therefore, the purpose of this study is to evaluate the performance of several approaches based on multispectral data to estimate chlorophyll-a in a productive tropical estuarine-lagoon system, using in situ measurements of remote sensing reflectance, Rrs. The possibility of applying algorithms using simulated satellite bands of modern and recent launched sensors was also evaluated. More accurate retrievals of chlorophyll-a (r2 > 0.80) based on field datasets were found using NIR-Red three-band models. In addition, enhanced chlorophyll-a retrievals were found using the two-band algorithm based on bands of recently launched satellites such as Sentinel-2/MSI and Sentinel-3/OLCI, indicating a promising application of these sensors to remotely estimate chlorophyll-a for coming decades in turbid inland waters. Our findings suggest that empirical models based on optical properties involving water constituents have strong potential to estimate chlorophyll-a using multispectral data from satellite, airborne or handheld sensors in productive tropical estuaries. View Full-Text
Keywords: shallow productive estuary; chlorophyll-a; remote sensing; Sentinel shallow productive estuary; chlorophyll-a; remote sensing; Sentinel

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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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Lins, R.C.; Martinez, J.-M.; Motta Marques, D.D.; Cirilo, J.A.; Fragoso, C.R. Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System. Remote Sens. 2017, 9, 516.

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