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Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil

Author

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  • Rhavel Salviano Dias Paulista

    (Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, MT, Brazil)

  • Frederico Terra de Almeida

    (Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, MT, Brazil)

  • Adilson Pacheco de Souza

    (Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, MT, Brazil)

  • Aaron Kinyu Hoshide

    (AgriSciences, Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Avenida Alexandre Ferronato, 1200, Sinop 78555-267, MT, Brazil
    College of Natural Sciences, Forestry and Agriculture, University of Maine, Orono, ME 04469, USA)

  • Daniel Carneiro de Abreu

    (Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, MT, Brazil
    AgriSciences, Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Avenida Alexandre Ferronato, 1200, Sinop 78555-267, MT, Brazil)

  • Jaime Wendeley da Silva Araujo

    (Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, MT, Brazil)

  • Charles Campoe Martim

    (Postgraduate Program in Environmental Physics, Federal University of Mato Grosso, Cuiabá 78060-900, MT, Brazil)

Abstract

Improving environmental sustainability involves measuring indices that show responses to different production processes and management types. Suspended sediment concentration (SSC) in water bodies is a parameter of great importance, as it is related to watercourse morphology, land use and occupation in river basins, and sediment transport and accumulation. Although already established, the methods used for acquiring such data in the field are costly. This hinders extrapolations along water bodies and reservoirs. Remote sensing is a feasible alternative to remedy these obstacles, as changes in suspended sediment concentrations are detectable by satellite images. Therefore, satellite image reflectance can be used to estimate SSC spatially and temporally. We used Sentinel-2 A and B imagery to estimate SSC for the Teles Pires River in Brazil’s Amazon. Sensor images used were matched to the same days as field sampling. Google Earth Engine (GEE), a tool that allows agility and flexibility, was used for data processing. Access to several data sources and processing robustness show that GEE can accurately estimate water quality parameters via remote sensing. The best SSC estimator was the reflectance of the B4 band corresponding to the red range of the visible spectrum, with the exponential model showing the best fit and accuracy.

Suggested Citation

  • Rhavel Salviano Dias Paulista & Frederico Terra de Almeida & Adilson Pacheco de Souza & Aaron Kinyu Hoshide & Daniel Carneiro de Abreu & Jaime Wendeley da Silva Araujo & Charles Campoe Martim, 2023. "Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7049-:d:1130514
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    References listed on IDEAS

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    1. Daniela Roberta Borella & Adilson Pacheco de Souza & Frederico Terra de Almeida & Daniel Carneiro de Abreu & Aaron Kinyu Hoshide & Glauber Altrão Carvalho & Rafaela Rocha Pereira & Apoliano Francisco , 2022. "Dynamics of Sediment Transport in the Teles Pires River Basin in the Cerrado-Amazon, Brazil," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
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    Cited by:

    1. Aaron Kinyu Hoshide, 2023. "Sustainable Development Agricultural Economics and Policy: Intensification versus Diversification," Sustainability, MDPI, vol. 15(12), pages 1-4, June.

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