4.7 Article

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

期刊

REMOTE SENSING OF ENVIRONMENT
卷 258, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112386

关键词

Semi-analytical models; Water type classification; Multi-wavelength; MERIS and OLCI; Total suspended solids

资金

  1. MEXT from Japan [17H01850, 17H04475A]
  2. Joint Polar Satellite System, National Aeronautics and Space Administration [NNX14AO73G]
  3. National Science Foundation [OCE0752254]
  4. EU [730066]
  5. Estonian Research Council [PSG10]
  6. NASA ROSES [80HQTR19C0015]
  7. Grants-in-Aid for Scientific Research [17H01850] Funding Source: KAKEN

向作者/读者索取更多资源

A semi-analytical method was proposed in this study for estimating total suspended solids (TSS) concentration in water bodies from remote-sensing data, with successful performance evaluations conducted in Lake Suwa and Lake Kasumigaura in Japan using MERIS and OLCI images.
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 (R-rs). 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 R-rs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (b(bp)) 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 b(bp) 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/m(3)) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m(3)) pairs of R-rs 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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