TY - JOUR
T1 - Remotely estimating total suspended solids concentration in clear to extremely turbid waters using a novel semi-analytical method
AU - Jiang, Dalin
AU - Matsushita, Bunkei
AU - Pahlevan, Nima
AU - Gurlin, Daniela
AU - Lehmann, Moritz K.
AU - Fichot, Cédric G.
AU - Schalles, John
AU - Loisel, Hubert
AU - Binding, Caren
AU - Zhang, Yunlin
AU - Alikas, Krista
AU - Kangro, Kersti
AU - Uusõue, Mirjam
AU - Ondrusek, Michael
AU - Greb, Steven
AU - Moses, Wesley J.
AU - Lohrenz, Steven
AU - O'Donnell, David
N1 - Funding Information:
This research was supported in part by the Grants-in-Aid for Scientific Research of MEXT from Japan (No. 17H01850 and No. 17H04475A ), and funding from the Joint Polar Satellite System, National Aeronautics and Space Administration ( NNX14AO73G ), National Science Foundation ( OCE-0752254 ), EU's Horizon 2020 research and innovation programme (grant agreement no. 730066 , EOMORES), Estonian Research Council grant PSG10 , and NASA ROSES contract # 80HQTR19C0015 , Remote Sensing of Water Quality element. We thank the European Space Agency for providing the MERIS and OLCI satellite images. Monitoring data on TSS in Lake Kasumigaura and Lake Suwa were provided by the National Institute for Environmental Studies (NIES) and Prof. Yuichi Miyabara of Shinshu University, Japan. The views, opinions, and findings contained in this paper are those of the authors and should not be construed as an official NOAA or U.S. government position, policy, or decision. We also appreciate the time, effort, and insight offered by four anonymous reviewers and members of the editorial team.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.rse.2021.112386
DO - 10.1016/j.rse.2021.112386
M3 - Article
AN - SCOPUS:85102380637
SN - 0034-4257
VL - 258
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112386
ER -