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United States Department of Agriculture

Agricultural Research Service


item Zimba, Paul
item Gitelson, A - UNIV. OF NEBRASKA

Submitted to: Aquaculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 1, 2006
Publication Date: April 1, 2006
Citation: Zimba, P.V., Gitelson, A.A. 2006. Remote estimation of chlorophyll concentration in hypereutrophic aquatic systems: model tuning and accuracy optimization. Aquaculture pp.272-286

Interpretive Summary: Detection of algae by remote sensing is difficult in systems having high concentrations of suspended material. Field and remote sensing reflectance data were collected from catfish production ponds. A modified mathematical formula was developed that was able to better predict algal biomass was devised. This model improved accuracy of algal assessment by 7% over previous models. These developments will be useful in estimating algal concentrations accurately.

Technical Abstract: Technical Abstract: Accurate assessment of phytoplankton chlorophyll a concentration by remote sensing is challenging in turbid hyper-eutrophic waters. This paper assessed methods to improve this problem. A hand-held spectroradiometer was used to measure subsurface spectral reflectance (R) in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained variable chlorophyll a (chl a from 107 to more than 3000 mg/m3) and turbidity (from 11 to 357 NTU) levels. The conceptual three-band model [R-1(lambda1)-R-1(lambda2)]lambdaR(lambda3) and its special case, the two-band model R(lambda3)/R(lambda1), were spectrally tuned in accord with optical properties of the media to optimize spectral bands (lambda1,lambda2, and lambda3) for accurate chl a estimation. Strong linear relationships were established between analytically measured chl a and both the three-band [R-1(650)-R-1(710)]xR(740) and the reflectance ratio model R(714)/(R650). The three-band model accounted for 7% more variation of chl a concentration than the ratio model (78 versus 71%). The findings underlined the rationale behind the conceptual model and demonstrated the robustness of the algorithm for chl a retrieval in very turbid, hyper-eutrophic waters.

Last Modified: 9/3/2015
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