Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote se...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-09, Vol.13 (18), p.3785
Main Authors: Hossain, A. K. M. Azad, Mathias, Caleb, Blanton, Richard
Format: Article
Language:English
Subjects:
Algorithms
Chlorophyll
COVID-19
GIS
Image acquisition
In situ measurement
Landsat
Landsat 8 Operational Land Imager (OLI)
Landsat satellites
Mathematical models
Numerical models
Optical properties
Radiation
Regression models
Remote sensing
Remote sensing systems
Rivers
Root-mean-square errors
Satellite imagery
Satellite technology
Satellites
Sensors
Storm runoff
Stormwater
Stormwater management
Surface water
Tennessee River
Tributaries
Turbidity
Water pollution
Water quality
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Summary:The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13183785