To monitor blooms, empirical algorithms based on band ratios or statistical approaches like genetic algorithms can be applied, since blooms are usually dominated by a single species. However, such algorithms are inherently not universally applicable and need to be tuned to the current situation. A challenge lies in the identification of different groups of phytoplankton under non-bloom conditions, as needed to establish an early warning system. If more than a single species affects the water colour, data analysis must be based on bio-optical models which are able to simulate the measured sensor signal of all bands for all possible concentrations of the optically active components in the water. These models have to be fed with inherent optical properties (IOPs) that characterise the actual water constituents. This is particularly difficult for cyanobacteria, because the IOPs of cyanobacteria are highly variable due to their capability to adapt the concentration and composition of light-harvesting pigments to the light conditions during growth. Therefore, one focus of this project lies on investigating the variability of the absorption and fluorescence properties of cyanobacteria, their parameterisation and implementation into bio-optical models.
External link to IGSSE project:
Further information about this project can be found here: IMOTOX
- Envilab: Measuring phytoplankton in-vivo absorption and scattering properties under tunable environmental conditions. Opt. Express 25 (21), 2017, 25267--25277 more… BibTeX
- IMOTOX - Identification and Monitoring of Toxic Cyanobacteria. IGSSE Water Focus Area, 2016 more… BibTeX
- Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake. Remote Sensing 8 (11), 2016 more… BibTeX
- Absorption and Fluorescence Characteristics of Colored Dissolved Matter for Remote Sensing: A Case Study on CDOM in Pre-alpine Lakes. International Workshop on Organic Matter Spectroscopy, 2015 more… BibTeX