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Article

An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment

1
Coastal Sensing and Modelling Group, Coasts Program, CSIRO Oceans and Atmosphere, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia
2
School of Earth and Environmental Sciences, University of Queensland, St Lucia, QLD 4072, Australia
3
Institute for Environmental Studies (IVM), VU University Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1578; https://doi.org/10.3390/rs12101578
Received: 28 March 2020 / Revised: 11 May 2020 / Accepted: 13 May 2020 / Published: 15 May 2020
(This article belongs to the Section Environmental Remote Sensing)
Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually. View Full-Text
Keywords: citizen science; smartphone; water quality; lakes; EyeOnWater; HydroColor; Australia citizen science; smartphone; water quality; lakes; EyeOnWater; HydroColor; Australia
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3820099
    Link: https://zenodo.org/record/3731789#.Xn7JtC17HUI
    Description: Supplementary Notes. Includes Table 1. Raw data upon which analysis is based and two figures highlighting the robustness of our spectral reflectance measurements.
MDPI and ACS Style

Malthus, T.J.; Ohmsen, R.; Woerd, H.J.v.d. An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment. Remote Sens. 2020, 12, 1578. https://doi.org/10.3390/rs12101578

AMA Style

Malthus TJ, Ohmsen R, Woerd HJvd. An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment. Remote Sensing. 2020; 12(10):1578. https://doi.org/10.3390/rs12101578

Chicago/Turabian Style

Malthus, Tim J., Renee Ohmsen, and Hendrik J.v.d. Woerd. 2020. "An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment" Remote Sensing 12, no. 10: 1578. https://doi.org/10.3390/rs12101578

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