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

Rhavel Salviano Dias Paulista, Frederico Terra de Almeida (), Adilson Pacheco de Souza, Aaron Kinyu Hoshide, Daniel Carneiro de Abreu, Jaime Wendeley da Silva Araujo and Charles Campoe Martim
Additional contact information
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
Daniel Carneiro de Abreu: Institute of Agrarian and Environmental Sciences, Federal University of Mato Grosso, Sinop 78557-287, 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

Sustainability, 2023, vol. 15, issue 9, 1-22

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.

Keywords: Amazonia; Google Earth Engine; hydro-sedimentology; reflectance; satellite imagery (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/9/7049/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/9/7049/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:9:p:7049-:d:1130514

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().