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Remote Sens. 2017, 9(6), 538; doi:10.3390/rs9060538

Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations

1
Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA
2
U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH 45202, USA
3
U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA
4
U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA
5
Kentucky Department of Environmental Protection, Division of Water, Frankfort, KY 40601, USA
6
National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD 20910, USA
7
Department of Physics and Geosciences, Texas A & M Kingsville, Kingsville, TX 78363-8202, USA
8
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
9
School of Geographic Sciences, Key Laboratory of Geographic Information Science, East China Normal University, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
Academic Editors: Raphael M. Kudela, Deepak R. Mishra and Prasad S. Thenkabail
Received: 1 March 2017 / Revised: 14 April 2017 / Accepted: 3 May 2017 / Published: 29 May 2017

Abstract

We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km2 reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r2 = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation. View Full-Text
Keywords: cyanobacteria; total biovolume; blue-green algae; BGA; phycocyanin; algal bloom; harmful algal bloom; algorithm; aircraft; satellite; hyperspectral; multispectral; coincident surface observations cyanobacteria; total biovolume; blue-green algae; BGA; phycocyanin; algal bloom; harmful algal bloom; algorithm; aircraft; satellite; hyperspectral; multispectral; coincident surface observations
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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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Beck, R.; Xu, M.; Zhan, S.; Liu, H.; Johansen, R.A.; Tong, S.; Yang, B.; Shu, S.; Wu, Q.; Wang, S.; Berling, K.; Murray, A.; Emery, E.; Reif, M.; Harwood, J.; Young, J.; Martin, M.; Stillings, G.; Stumpf, R.; Su, H.; Ye, Z.; Huang, Y. Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations. Remote Sens. 2017, 9, 538.

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