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Measured and numerically partitioned phytoplankton spectral absorption coefficients in inland waters

Zhang, Y. and Liu, M. and Van Dijk, M.A. and Zhu, G. and Gong, Z. and Li, Y.M. and Qin, B. (2009) Measured and numerically partitioned phytoplankton spectral absorption coefficients in inland waters. Journal of Plankton Research, 31, 311-323. ISSN 0142-7873.

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Total particulate, tripton and phytoplankton absorption coefficients were measured for eutrophic (Lake Taihu), meso-eutrophic (Lake Tianmuhu) and mesotrophic waters (the Three Gorges Reservoir) in China using the quantitative filter technique. Meanwhile, tripton and phytoplankton absorption coefficients were numerically partitioned from total particulate matter absorption, using eight different models. The root mean square error (RMSE), relative RMSE (RRMSE), mean relative error (MRE) and similarity coefficient (SC) were used to assess the performance of the models. Phytoplankton absorption aph(440) and aph(675) ranged from 0.03 to 8.39 m–1 and from 0.02 to 5.21 m–1, respectively, including a large variability for particles and phytoplankton concentrations (total suspended matter: 1.6–191 mg L–1; chlorophyll a: 0.4–477.1 µg L–1). For the best model, model 6, tripton absorption was modeled using an exponential model with a background constant. The ratios of aph(380)/aph(505), aph(580)/aph(692.5) and aph(510)/aph(412) were used to model the spectral absorption of phytoplankton. The best performance of the partition models tended to a slight overestimation with the RRMSE equal to 44.4%, 27.0%, 33.5% and 40.1% for aph(400), aph(440), aph(675) and aph(PAR), respectively. The precision of the numerical method was largely determined by the total particle composition and the relative contribution of phytoplankton to the total particulate matter. Overall, the numerical partition model provides a reasonable estimation of the phytoplankton absorption coefficient, which enables us to retrieve the concentration of optically active substances from in situ measurements and remote sensing ocean color data.

Item Type:Article
Institutes:Nederlands Instituut voor Ecologie (NIOO)
ID Code:6269
Deposited On:23 Mar 2010 01:00
Last Modified:04 Sep 2014 09:36

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