Estimation of Reservoir Bio-Optical Water Quality Parameters Using Smartphone Sensor Apps and Landsat ETM+: Review and Comparative Experimental Results

This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll-a (Chl-a), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible...

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Bibliographic Details
Published in:Journal of sensors 2018, Vol.2018 (2018), p.1-32
Main Authors: Lavisa, O., Okech, M., Waga, J., Ouma, Yashon O., Mbuthia, D.
Format: Article
Language:English
Subjects:
Chlorophyll
Correlation coefficients
Environmental monitoring
Imaging
In situ measurement
Laboratories
Lakes
Landsat satellites
Optical activity
Parameter estimation
Pollutants
Prediction models
Regression models
Remote sensing
Science
Sediments
Smartphones
Surface water
Turbidity
Water quality
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Summary:This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll-a (Chl-a), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the remote reflectances of ETM+ green, blue, and NIR independently yielded the best fits for the respective predictions of Chl-a, turbidity, and SPM. The concentration of Chl-a was derived from the Landsat ETM+ and HydroColor with respective Pearson correlation coefficients r of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and HydroColor with respective r values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with the laboratory measurements with r value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater, and HydroColor. The empirical study results showed that the imaging models can be effectively applied in the estimation of the physical WQP.
ISSN:1687-725X
1687-7268
DOI:10.1155/2018/3490757