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Mechanisms of Remote-Sensing Reflectance Variability and Its Relation to Bio-Optical Processes in a Highly Turbid Eutrophic Lake: Lake Taihu (China)

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5 Author(s)
Qiao Wang ; Key Lab. of Virtual Geographic Environ., Nanjing Normal Univ., Nanjing, China ; Deyong Sun ; Yunmei Li ; Chengfeng Le
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During November 2006 and November 2007, two field bio-optical campaigns were conducted in Lake Taihu to assess the mechanisms regulating the water-color variability in a typically inland eutrophic lake. Various bio-optical parameters were acquired simultaneously with measurements of remote-sensing reflectance (R rs), which generates a collection of 106 samples. The bio-optical parameters span a wide range of variability indicating the presence of complex water-column conditions, which also correspondingly brings about a great variability of the R rs( ??) spectra. The R rs(??) variability characteristics are extracted by an empirical orthogonal-function (EOF) analysis. The results show that 84% of the total variance of R rs(??) is mainly attributed to particulate absorption (particularly chlorophyll a (Chl a) absorption) and pure-water absorption, which has been summarized as the first EOF mode, M 1, and M 2 can mainly reflect the other variance of R rs(??), which can be attributed entirely to phytoplankton: absorption by pigments and scattering by particles. Further, the in situ R rs(??) spectra are categorized into two types by a hierarchical cluster approach, that is, a universal type and a phytoplankton-dominant type. The phytoplankton-dominant stations are characterized by high Chl a concentration, organic suspended-matter concentrations, and phytoplankton absorption (a ph) with a good regression relationship between R rs (700) and Chl a concentration (R 2 = 0.922), while the universal stations present the wider variation ranges of particulate scattering (b p) and backscattering (b bp) due to the great inorganic suspended-matter concentration variation.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:48 ,  Issue: 1 )