Publicação: Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters
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Elsevier B.V.
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Acesso aberto
Resumo
Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (R-rs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (a(CDM)) and 7566.33% for phytoplankton absorption coefficient (a(phy)). We re-parameterized a QAA for CDOM dominated (hereafter QAA(CDOM)) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400-750nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for a(CDM) and 25.90 and 29.76 for a(phy) in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAA(CDOM) showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(lambda) and a(phy)(lambda). Successful retrieval of the absorption coefficients by QAA(CDOM) would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters. (C) 2016 Elsevier B.V. All rights reserved.
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Bio-optical model, Colored dissolved organic matter, Inland waters, Quasi-analytical algorithm, Inherent optical properties, Absorption coefficient of phytoplankton, Remote sensing reflectance
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Inglês
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International Journal Of Applied Earth Observation And Geoinformation. Amsterdam: Elsevier Science Bv, v. 53, p. 128-145, 2016.
