Remotely estimating total suspended solids concentration in clear to extremely turbid waters using a novel semi-analytical method

• Published in: Remote sensing of environment Vol. 258; p. 112386
• Main Authors: Jiang, Dalin, Matsushita, Bunkei, Pahlevan, Nima, Gurlin, Daniela, Lehmann, Moritz K., Fichot, Cédric G., Schalles, John, Loisel, Hubert, Binding, Caren, Zhang, Yunlin, Alikas, Krista, Kangro, Kersti, Uusõue, Mirjam, Ondrusek, Michael, Greb, Steven, Moses, Wesley J., Lohrenz, Steven, O'Donnell, David
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center Elsevier Inc 01.06.2021; Elsevier; Elsevier BV
• Subjects: Algorithms; Analytical methods; Backscattering; color; data collection; Earth Resources And Remote Sensing; environment; Environmental Sciences; Estimation; Image acquisition; Imaging spectrometers; Japan; Lakes; Mathematical models; MERIS and OLCI; Multi-wavelength; Performance assessment; Quality management; Reflectance; Remote sensing; Sediment transport; Semi-analytical models; Solid suspensions; spectrometers; time series analysis; Total suspended solids; Water quality; Water quality management; Water type classification; Wavelength; wavelengths; Semi-analytical models; Water type classification; Multi-wavelength; Total suspended solids; MERIS and OLCI; Meris And Olci; Water Type Classification; Mulit-Wavelength; Total Suspended Solids; Semi-Analytical Models
• ISSN: 0034-4257; 1879-0704; 1879-0704
• DOI: 10.1016/j.rse.2021.112386

Total suspended solids (TSS) concentration is an important biogeochemical parameter for water quality management and sediment-transport studies. In this study, we propose a novel semi-analytical method for estimating TSS in clear to extremely turbid waters from remote-sensing reflectance (Rrs). The proposed method includes three sub-algorithms used sequentially. First, the remotely sensed waters are classified into clear (Type I), moderately turbid (Type II), highly turbid (Type III), and extremely turbid (Type IV) water types by comparing the values of Rrs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (bbp) at a corresponding single wavelength (i.e., 560 nm for Type I, 665 nm for Type II, 754 nm for Type III, and 865 nm for Type IV). Third, a specific relationship between TSS and bbp at the corresponding wavelength is used in each water type. Unlike other existing approaches, this method is strictly semi-analytical and its sub-algorithms were developed using synthetic datasets only. The performance of the proposed method was compared to that of three other state-of-the-art methods using simulated (N = 1000, TSS ranging from 0.01 to 1100 g/m3) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m3) pairs of Rrs and TSS. Results showed a significant improvement with a Median Absolute Percentage Error (MAPE) of 16.0% versus 30.2–90.3% for simulated data and 39.7% versus 45.9–58.1% for in situ data, respectively. The new method was subsequently applied to 175 MEdium Resolution Imaging Spectrometer (MERIS) and 498 Ocean and Land Colour Instrument (OLCI) images acquired in the 2003–2020 timeframe to produce long-term TSS time-series for Lake Suwa and Lake Kasumigaura, Japan. Performance assessments using MERIS and OLCI matchups showed good agreements with in situ TSS measurements. •We developed a novel semi-analytical method for estimating TSS.•The new method outperformed three existing state-of-the-art methods.•The new method is widely applicable from clear to extremely turbid waters.•The new method can produce reasonable TSS time-series from MERIS & OLCI data.