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• W2904615853 abstract "Total suspended matter (TSM) is related to water quality. High TSM concentrations limit underwater light availability, thus affecting the primary productivity of aquatic ecosystems. Accurate estimation of TSM concentrations in various waters with remote sensing technology is particularly challenging, as the concentrations and optical properties vary greatly among different waters. In this research, a semi-analytical model was established for Hangzhou Bay and Lake Taihu for estimating TSM concentration. The model construction proceeded in two steps. 1) Two indices of the model were calculated by deriving absorption and backscattering coefficients of suspended matter (ap(λ) and bbp(λ)) from the reflectance signal using a semi-analytical method. 2) The two indices were then weighted to derive TSM. The performance of the proposed model was tested using in situ reflectance and Geostationary Ocean Color Imager (GOCI) data. The derived TSM based on in situ reflectance and GOCI images both corresponded well with the in situ TSM with low mean relative error (32%, 41%), root mean square error (20.1 mg/L, 43.1 mg/L), and normalized root mean square error (33%, 55%). The model was further used for the slightly turbid Xin'anjiang Reservoir to demonstrate its applicability to derive ap(λ) and bbp(λ) in other water types. The results indicated that the form Rrs-1(λ1) - Rrs-1(λ2) could minimize the effect of CDOM absorption in deriving ap(λ) from the total absorption. The model exploited the different relationships between TSM concentration and multiband reflectance, thus improving the performance and application range in deriving TSM." @default.
• W2904615853 created "2018-12-22" @default.
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• W2904615853 date "2018-12-14" @default.
• W2904615853 modified "2023-10-16" @default.
• W2904615853 title "A semi-analytical model for estimating total suspended matter in highly turbid waters" @default.
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• W2904615853 doi "https://doi.org/10.1364/oe.26.034094" @default.
• W2904615853 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30650838" @default.
• W2904615853 hasPublicationYear "2018" @default.
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