Laboratory measurements of remote sensing reflectance of selected phytoplankton species from the Baltic Sea
Monika Soja-Woźniak , Mirosław Darecki , Bożena Wojtasiewicz , Katarzyna Bradtke
AbstractResults of unique laboratory measurements of remote sensing reflectance (Rrs) of several phytoplankton species typically occurring in high abundances in the Baltic Sea waters are presented. Reflectance spectra for diatoms: Cyclotella meneghiniana and Skeletonema marinoi and cyanobacteria: Dolichospermum sp., Nodularia spumigena and Synechococcus sp. were analysed in terms of assessment of their characteristic features and the differences between them. These species contain similar pigments, which results in general similarities of reflectance spectra, i.e. decrease of reflectance magnitude in the blue and red spectrum regions. However, hyper-spectral resolution of optical measurements let us find differences between optical signatures of diatoms and cyanobacteria groups and between species belonging to one group as well. These differences are reflected in location of local maxima and minima in the reflectance spectrum and changes in relative height of characteristic peaks with changes of phytoplankton concentration. Wide ranges of phytoplankton concentrations were analysed in order to show the persistence of Rrs characteristic features. The picoplankton species, Synechococcus sp. show the most distinct optical signature, which let to distinguish separate cluster in hierarchical cluster analysis (HCA). The results can be used to calibrate input data into radiative transfer model, e.g. phase function or to validate modelled Rrs spectra.
|Journal series||Oceanologia, ISSN 0078-3234, (A 20 pkt)|
|Publication size in sheets||0.55|
|Keywords in English||Phytoplankton monoculture, laboratory measurements, remote sensing reflectance|
|License||Journal (articles only); published final; ; with publication|
|Score|| = 20.0, 20-12-2017, ArticleFromJournal|
= 20.0, 20-12-2017, ArticleFromJournal
|Publication indicators||: 2016 = 1.5 (2) - 2016=1.341 (5)|
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