The HR (High-Resolution) EO (Earth Observation) satellite systems Landsat 8 OLI and Sentinel 2 were tested for mapping the frequent phytoplankton blooms and Chl a distributions in the sea basin of the Mar Piccolo of Taranto (Ionian Sea, southern Italy), using the sea truth calibration data acquired in 2013. The data were atmospherically corrected for accounting of the aerosol load on optically complexes waters (case II). Various blue-green and additional spectral indices ratios, were then satisfyingly tested for mapping the distribution of Chl a and differently sized phytoplankton populations through PLS (Partial Least Square regression) models, regressive statistical models and bio-optical algorithms. The PLS models demonstrated higher robustness for assessing the distribution of all the phytoplankton and Chl a except for those related to sub-surface micro-phytoplankton. The distributions obtained via a bio-optical approach (OC3 algorithm and full physically based inversion) showed a general agreement with the previous ones produced by statistical methods. The reflectance signals, captured by OLI and Sentinel 2 sensors in the visible and shorter wavelengths once atmospherically corrected, were found to be useful to map the coastal variability at detailed scale of Chl a and different phytoplankton populations, in the optically complexes waters of the Mar Piccolo.

Multispectral data by the new generation of high-resolution satellite sensors for mapping phytoplankton blooms in the Mar Piccolo of Taranto (Ionian Sea, southern Italy)

Borfecchia F.;Micheli C;Pignatelli V.;
2019

Abstract

The HR (High-Resolution) EO (Earth Observation) satellite systems Landsat 8 OLI and Sentinel 2 were tested for mapping the frequent phytoplankton blooms and Chl a distributions in the sea basin of the Mar Piccolo of Taranto (Ionian Sea, southern Italy), using the sea truth calibration data acquired in 2013. The data were atmospherically corrected for accounting of the aerosol load on optically complexes waters (case II). Various blue-green and additional spectral indices ratios, were then satisfyingly tested for mapping the distribution of Chl a and differently sized phytoplankton populations through PLS (Partial Least Square regression) models, regressive statistical models and bio-optical algorithms. The PLS models demonstrated higher robustness for assessing the distribution of all the phytoplankton and Chl a except for those related to sub-surface micro-phytoplankton. The distributions obtained via a bio-optical approach (OC3 algorithm and full physically based inversion) showed a general agreement with the previous ones produced by statistical methods. The reflectance signals, captured by OLI and Sentinel 2 sensors in the visible and shorter wavelengths once atmospherically corrected, were found to be useful to map the coastal variability at detailed scale of Chl a and different phytoplankton populations, in the optically complexes waters of the Mar Piccolo.
Phytoplankton, Coastal Shallow Waters, Landsat 8 Oli, Regressive Partial Least Square And Bio-optical Models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/52909
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