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Remote Sens. 2016, 8(5), 377; doi:10.3390/rs8050377

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

1
The City College of New York, Optical Remote Sensing Laboratory, Department of Electrical Engineering, 160 Convent Ave, New York, NY 10031, USA
2
NATO Science & Technology Organization, Center for Maritime Research and Experimentation, Viale San, Bartolomeo 400, La Spezia 19126, Italy
3
NOAA National Centers for Coastal Ocean Science, 1305 East-West Highway Code N/SCI1 Silver Spring, MD 20910, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Richard W. Gould, Xiaofeng Li and Prasad S. Thenkabail
Received: 30 December 2015 / Revised: 16 April 2016 / Accepted: 20 April 2016 / Published: 4 May 2016
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)

Abstract

We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (aph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The aph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved aph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum aph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs. View Full-Text
Keywords: neural networks; harmful algal blooms; ocean color remote sensing reflectance; Karenia brevis; retrieved chlorophyll-a; normalized fluorescence height; West Florida Shelf neural networks; harmful algal blooms; ocean color remote sensing reflectance; Karenia brevis; retrieved chlorophyll-a; normalized fluorescence height; West Florida Shelf
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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

El-habashi, A.; Ioannou, I.; Tomlinson, M.C.; Stumpf, R.P.; Ahmed, S. Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques. Remote Sens. 2016, 8, 377.

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