Version 1
: Received: 8 September 2017 / Approved: 10 September 2017 / Online: 10 September 2017 (07:36:21 CEST)
How to cite:
Matarrese, R.; Guyennon, N.; Copetti, D. Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data. Preprints2017, 2017090033. https://doi.org/10.20944/preprints201709.0033.v1
Matarrese, R.; Guyennon, N.; Copetti, D. Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data. Preprints 2017, 2017090033. https://doi.org/10.20944/preprints201709.0033.v1
Matarrese, R.; Guyennon, N.; Copetti, D. Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data. Preprints2017, 2017090033. https://doi.org/10.20944/preprints201709.0033.v1
APA Style
Matarrese, R., Guyennon, N., & Copetti, D. (2017). Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data. Preprints. https://doi.org/10.20944/preprints201709.0033.v1
Chicago/Turabian Style
Matarrese, R., Nicolas Guyennon and Diego Copetti. 2017 "Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data" Preprints. https://doi.org/10.20944/preprints201709.0033.v1
Abstract
In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.
Keywords
P. rubescens algal bloom; remote sensing; MERIS; MODIS
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
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.
