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Satellite monitoring and interpretation of dynamically changing concentrations of Karenia Brevis harmful algal blooms in the West Florida Shelf (Conference Presentation)
Author(s): Samir Ahmed; Ahmed El-Habashi; Vincent Lovko
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Paper Abstract

Karenia brevis Harmful Algal blooms (KB HABS) plague the coasts of the West Florida Shelf (WFS) and effective monitoring is needed to provide information to local authorities for health warnings etc. We compare results of satellite retrievals of KB HABs, using previously existing algorithms, for both MODIS and VIIRS, as well as with our more recently developed neural network (NN) algorithm. Retrievals are compared against in-situ HABs measurements. To obtain sufficient numbers of in-situ measurements nearly concurrent with satellite overpasses, comparisons were extended over 2012-17. Algorithms compared included nFLH, RBD OCI/OC3, GIOP and QAA. Retrieval statistics showed that the NN technique achieved the best accuracies, possibly due to the fact that it uses the 486, 551 and 671 nm channels for the retrievals, which are less impacted by atmospheric correction inadequacies in coastal waters, than the deeper blue channels used with other retrieval algorithms. Results highlight impacts of temporal variabilities on retrieval accuracies. Thus a shorter overlap time window between in-situ measurement and satellite observation of 15 minutes, showed significantly better accuracies than a 100 minutes, reflecting short-term changes in the KB HABs scene being observed. These relatively rapid temporal changes are further confirmed by retrievals from consecutive satellite overpasses: VIIRS-MODIS-A –VIIRS (second overpass) within a 100 minute period, and by in-situ measurements in the WFS. Temporal changes are seen to clearly affect the timeliness and relevance of satellite retrievals of HABs and Ocean Color parameters, particularly in coastal zones with dynamically changing conditions, and need to be taken into account, including possible development of alternate observation means in real time, such as UAVs.

Paper Details

Date Published: 15 May 2018
Proc. SPIE 10631, Ocean Sensing and Monitoring X, 106310D (15 May 2018); doi: 10.1117/12.2304357
Show Author Affiliations
Samir Ahmed, NOAA-CREST, The City College of New York (United States)
Ahmed El-Habashi, NOAA-CREST, The City College of New York (United States)
Vincent Lovko, Mote Marine Lab. (United States)

Published in SPIE Proceedings Vol. 10631:
Ocean Sensing and Monitoring X
Weilin "Will" Hou; Robert A. Arnone, Editor(s)

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