Remote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin

TitleRemote sensing of freshwater cyanobacteria: An extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin
Publication TypeJournal Article
Year of Publication2015
AuthorsLi L.H, Li L., Song K.S
JournalRemote Sensing of Environment
Volume157
Pagination9-23
Date Published2015/02
Type of ArticleArticle
ISBN Number0034-4257
Accession NumberWOS:000348257100002
Keywordsabsorption; chlorophyll-a; cyanobacteria; diffuse-reflectance; dissolved organic-matter; eutrophic lakes; IIMIW; inherent optical-properties; Light absorption coefficient partitioning; oceanic waters; Phycocyanin; phytoplankton; radiance model; Remote sensing of inland waters; satellite retrieval; spectral
Abstract

Phycocyanin primarily exists in freshwater cyanobacteria. Accurate estimation of low phycocyanin concentration (PC) is critical for issuing an early warning of potential risks of cyanobacterial population growth to the public. To monitor cyanobacterial biomass in eutrophic inland waters, an approach is proposed to partition non-water absorption coefficient (a(t-w)(lambda)) into the contribution of colored dissolved matter (CDM), non-phycocyanin pigments, and phycocyanin with the aim of improving the accuracy in remotely estimated PC, in particular for low PC. The proposed algorithm extends the IOP Inversion Model of Inland Waters (IIMIW) that derives a(t-w)(lambda) and chlorophyll-a concentration from remote sensing reflectance. The extended IIMNV retrieves absorption spectra of both CDM (a(cdm)(lambda)) and phytoplankton (a(ph)(lambda)) with R-2 >= 0.80 and a relative root mean square error (rRMSE) <= 31.79% for a(cdm)(412), a(ph)(443), a(ph)(620), and a(ph)(665) when validated with data collected in 2010 from three Indiana reservoirs. In fact, comparison of our algorithm with other partitioning models demonstrates the new algorithm to be more suitable for inland waters. The algorithm also achieved more accurate PC estimation with R-2 = 0.81, rRMSE = 33.60%, and mean relative error (RE) = 49.11% than the widely used semi-empirical algorithm with R-2 = 0.73, rRMSE = 45.09%, and mean RE = 182.29% for the same dataset The validation of our algorithm against the data collected in other years shows that the proposed algorithm worked for a wide range of limnological conditions. In particular, low PC (PC <= 50 mg m(-3)) values of for all datasets used in this study were well predicted by the proposed algorithm. (C) 2014 Elsevier Inc. All rights reserved.

DOI10.1016/j.rse.2014.06.009
Short TitleRemote Sens. Environ.
Student Publication: 
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