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Optimized extraction of daily bio-optical time series derived from MODIS/Aqua imagery for Lake Tanganyika, Africa
Authors:S. Horion  N. Bergamino  J.-P. Descy  S.A. Loiselle
Affiliation:a Unit of Geomatics, University of Liège, Allée du 6 Août 17, B-4000 Liège, Belgium
b Environmental Spectroscopy Group, Department of Applied and Medicinal Chemistry, CSGI, University of Siena, Via A. Moro 2, 53100 Siena, Italy
c Laboratory of Freshwater Ecology, Research Unit in Biology of Organisms, Department of Biology, University of Namur, Rue de Bruxelles 61, B-5000 Namur, Belgium
d Royal Museum for Central Africa, Leuvensesteenweg, 13, B-3080 Tervuren, Belgium
Abstract:Lake Tanganyika is one of the world's great freshwater ecosystems. In recent decades its hydrodynamic characteristics have undergone important changes that have had consequences on the lake's primary productivity. The establishment of a long-term Ocean Color dataset for Lake Tanganyika is a fundamental tool for understanding and monitoring these changes. We developed an approach to create a regionally calibrated dataset of chlorophyll-a concentrations (CHL) and attenuation coefficients at 490 nm (K490) for the period from July 2002 to December 2006 using daily calibrated radiances retrieved from the MODIS-Aqua sensor. Standard MODIS Aqua Ocean Color products were found to not provide a suitable calibration for high altitude lakes such as the Lake Tanganyika. An optimization of the extraction process and the validation of the dataset were performed with independent sets of in situ measurements. Our results show that for the geographical, atmospheric and optical conditions of Lake Tanganyika: (i) a coastal aerosol model set with high relative humidity (90%) provides a suitable atmospheric correction; (ii) a significant correlation between in situ data and CHL estimates using the MODIS specific OC3 algorithm is possible; and (iii) K490 estimates provide a good level of significance. The resulting validated time series of bio-optical properties provides a fundamental information base for the study of phytoplankton and primary production dynamics and interannual trends. A comparison between surface chlorophyll-a concentrations estimated from field monitoring and from the MODIS based dataset shows that remote sensing allows improved detection of surface blooms in Lake Tanganyika.
Keywords:Phytoplankton dynamics   Bio-optical algorithms   SeaDAS process optimization   MODIS Aqua   Lake Tanganyika
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