POSTER Session 1

Seeing the phytoplankton pigments (and not the noise or the wiggles) in spaceborne hyperspectral reflectance data

The main advantage of hyperspectral over multispectral remote sensing for coastal waters is the potential to detect different phytoplankton groups. This potential is limited in turbid waters where phytoplankton pigments may represent only a small part of the total backscatter and absorption. Algorithms that exploit spectral curvature/derivatives/anomalies are promising there. Many hyperspectral algorithms have been proposed for phytoplankton groups and tested using modeled or in situ measured reflectance, but successful validation for hyperspectral satellite data is rare, especially in turbid waters. There are 3 possible reasons for this: 1. Hyperspectral algorithms exploiting spectral curvature/derivatives/anomalies are very sensitive to random noise, interband calibration and systematic wiggles near atmospheric absorption wavelengths. 2. Most hyperspectral satellites were not designed for this application, which has stringent signal:noise and interband calibration requirements. 3. Atmospheric correction algorithms have been designed and validated to provide accurate water reflectance but may give very inaccurate second derivative of water reflectance for wavelengths near atmospheric absorption features. This presentation will provide a mathematical framework for the impact of random noise, interband calibration and systematic wiggles on the second derivative of reflectance. This will then be used to limit the impact of such measurement errors on phytoplankton group algorithms, using examples from simulated, in situ and satellite measurements.

Kevin Ruddick, Royal Belgian Institute of Natural Sciences (RBINS), Belgium

Quinten Vanhellemont, Royal Belgian Institute of Natural Sciences (RBINS), Belgium

Code of Conduct
Privacy Policy