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A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters From Simulated MERIS and OLCI Data.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Mar2018, Vol. 56 Issue 3, p1374-1385, 12p
- Publication Year :
- 2018
-
Abstract
- Existing remote-sensing algorithms to estimate the phycocyanin (PC) concentration in turbid inland waters have high associated uncertainties, especially at low PC concentrations in diverse phytoplankton communities. This paper provides the theoretical framework for a four-band semi-analytical algorithm (FBA_PC) which isolates PC absorption from second-order variability caused by yellow matter and other phytoplankton pigment absorption. The algorithm suits the band configuration of both the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3 Ocean and Land Color Instrument (OLCI). Calibration of the algorithm was based on absorption data from 12 inland water bodies in the USA, The Netherlands, and China, combined with measurements from laboratory-grown cultures, which demonstrated that the assumptions underlying FBA-PC are an improvement over existing three-band approaches. Validation of FBA_PC in seven inland water bodies in the USA, The Netherlands, and China showed good agreement of FBA_PC adjusted to the MERIS/OLCI band configuration with measured PC, with root-mean-square error = 27.691 mg · m<superscript>-3</superscript>, mean absolute percentage error = 172.863%, and coefficient of determination (R<superscript>2</superscript>) = 0.730. FBA_PC outperformed previously proposed PC algorithms that can be applied to MERIS or OLCI data, and is expected to be more robust when applied to a wider range of water bodies. [ABSTRACT FROM AUTHOR]
- Subjects :
- CYANOBACTERIA
SPECTROMETERS
PHYCOCYANIN
PHYTOPLANKTON
REMOTE sensing
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 56
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 128707572
- Full Text :
- https://doi.org/10.1109/TGRS.2017.2761996