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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 10; p. 1578
• Main Authors: Malthus, Tim J., Ohmsen, Renee, Woerd, Hendrik J. van der
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2020
• Subjects: Algae; Algorithms; citizen science; Coastal waters; Color; Composition; Environmental monitoring; Evaluation; EyeOnWater; HydroColor; Inland waters; Laboratories; Lagoons; Lakes; Monitoring; Outliers (statistics); Parameters; Particulate matter; Quality assessment; Quality control; Reflectance; Remote sensing; Remote sensors; Response functions; Rivers; Sensors; smartphone; Smartphones; Spectrometers; Surface water; Systematic errors; Water analysis; Water color; Water quality; Water quality assessments; Water sampling; Queensland Australia; Australia; New South Wales Australia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12101578

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.