A semi-analytical model for estimating total suspended matter in highly turbid waters

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 (a p (λ) and b bp (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 a p (λ) and b bp (λ) in other water types. The results indicated that the form R rs −1 (λ 1 ) − R rs −1 (λ 2 ) could minimize the effect of CDOM absorption in deriving a p (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.

Remote Estimation of the Particulate Phosphorus Concentrations in Inland Water Bodies: A Case Study in Hongze Lake

“…The positions of Band 11 and 12 are similar to those of the hyperspectral data, thus providing the possibility to estimate TP concentrations using the algorithm established in this paper. Thus, Band 9 was set as λ1, and Bands 11, 12, and 13 were iterated to determine the positions of λ2 and Combining Equations ( 4), ( 5) and ( 12) yields the following expression: y = 0.2x + 0.161 (13) where y is the TP concentration and x is [1/R rs (677) − 1/R rs (720)] × R rs (730). The Sentinel 3 OLCI sensor collects data on 21 bands, and the center wavelength of Band 9 is 674 nm, which is similar to the 677 nm wavelength.…”

“…Increased eutrophication processes eventually lead to increasingly serious water eutrophication conditions [12]. As a highly turbid inland water body, Hongze Lake contains high concentrations of suspended sediments, and these pollutants are dominated by PP [13]. However, there is no study on the spatiotemporal variation of particulate phosphorus concentration in Hongze Lake that has used remote sensing data, only TP in Hongze Lake has been studied.…”

A Prediction Model of Water In Situ Data Change under the Influence of Environmental Variables in Remote Sensing Validation

“…Satellite remote sensing can provide large-scale and long-term observational data, which provide the possibility to dynamically measure and monitor water environment. Chlorophyll-a concentration (Chl-a) and total suspended solids concentration (TSS) generated from remote sensing satellite data are two typical remote sensing products (RSPs), among which Chl-a is an important indicator for red tide monitoring and water eutrophication monitoring [3,4], and TSS is related to water transparency, total primary production, and fluxes of heavy metals and micro-pollutants [1,5]. Furthermore, these two RSPs are the basis of other more advanced products such as net primary productivity (NPP) and water quality [6].…”