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

Malthus, Tim J.; Ohmsen, Renee; Woerd, H.J. van der

doi:10.3390/rs12101578

Cited by 37 publications

(49 citation statements)

References 52 publications

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“…HydroColor can be downloaded from App stores and has been applied and validated by several studies. Malthus et al [ 21 ] used HydroColor to collect water images from 32 sampling stations in eastern Australia to calculate water leaving reflectance, which was compared with in situ measurements. The accuracy of HydroColor was lower when the surrounding water environment was complex.…”

Section: Introductionmentioning

confidence: 99%

“…This assumes that there is a linear relationship between camera-measured digital pixel number (DN) values and the incident light radiance, which is the basis of the water leaving reflectance derivation and water quality parameter inversion. However, studies have shown that the linear relationship hypothesis between the DN and incident light radiance in HydroColor is not accurate [ 21 , 23 , 24 ]. Therefore, the purpose of this study was to develop a method to simulate the nonlinear relationship between the DN and incident light radiance by multiple reflectance reference cards, thereby deriving water leaving reflectance from the nonlinear corrected DN in digital images.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

Gao

Zhang

et al. 2020

Sensors

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With the development of citizen science, digital cameras and smartphones are increasingly utilized in water quality monitoring. The smartphone application HydroColor quantitatively retrieves water quality parameters from digital images. HydroColor assumes a linear relationship between the digital pixel number (DN) and incident radiance and applies a grey reference card to derive water leaving reflectance. However, image DNs change with incident light brightness non-linearly, according to a power function. We developed an improved method for observing and calculating water leaving reflectance from digital images based on multiple reflectance reference cards. The method was applied to acquire water, sky, and reflectance reference card images using a Cannon 50D digital camera at 31 sampling stations; the results were validated using synchronously measured water leaving reflectance using a field spectrometer. The R2 for the red, green, and blue color bands were 0.94, 0.95, 0.94, and the mean relative errors were 27.6%, 29.8%, 31.8%, respectively. The validation results confirm that this method can derive accurate water leaving reflectance, especially when compared with the results derived by HydroColor, which systematically overestimates water leaving reflectance. Our results provide a more accurate theoretical foundation for quantitative water quality monitoring using digital and smartphone cameras.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

Gao

Zhang

et al. 2020

Sensors

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“…Water color, as perceived by the human eye, is an integrative measure of water quality. It can be scaled to rivers globally and measured across multiple sensor platforms from human eyes (Garaba et al., 2015) to cell phone cameras (Leeuw & Boss, 2018; Malthus et al., 2020) to satellites. The intuitive and easily observable nature of water color could enable collection of massive volumes of data across spatial and temporal scales for water quality monitoring, identifying global hotspots of change, and advancing macrosystems ecology in rivers.…”

Section: Resultsmentioning

confidence: 99%

The Color of Rivers

Gardner

Yang

Topp

et al. 2021

Geophysical Research Letters

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Rivers are among the most degraded ecosystems on earth (Best, 2019). Water quality is impaired due to human activities such as agriculture and urbanization (Foley et al., 2005; Meybeck et al., 1990), and currently only 23% of earth's largest rivers flow uninterrupted to the ocean (Grill et al., 2019; Nilsson et al., 2005). Because large rivers integrate millions of kilometers of land area, understanding rivers and their impairments is inherently macroscale: both distant and local impacts generate the patterns we observe (Heffernan et al., 2014; McCluney et al., 2014). There is a profound need for integrative water quality measurements that are spatially explicit and globally scalable, as local and global changes impairing Earth's rivers cannot be fully understood using sparse, ground-based measurements (Stanley et al., 2019; Stets et al., 2020). Remote sensing enables spatially explicit, global observations of large rivers (Palmer et al., 2015). Satellite missions, such as the joint NASA/USGS Landsat mission, have been used for decades to measure river and lake water quality (Brezonik et al., 2005; Carpenter & Carpenter, 1983). However, measuring water quality at continental to global scales remains challenging over inland waters due to optical complexity, or the presence of multiple water quality constituents (Ross et al., 2019; Topp et al., 2020). The three main constituents are chlorophyll-a (chl-a), suspended sediment, and colored dissolved organic matter (CDOM) (Davies-Colley et al., 2003; Ritchie et al., 2003). These water quality constituents are ecologically important, control light availability for photosynthesis and photodegradation, and together determine the color of water which is an integrative measure of water quality (

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“…Significant filtration was needed to obtain data points which really fulfilled the scientific criteria for a research study (Figure 9B). In a similar citizen science project using FU index to measure water colour of Australian inland waters, Malthus et al (2020) have reported difference of more than 2 FUI units between the observer data and the photo-based colour code in only 3% of the cases. Therefore, the reason for the larger bias in our case needs to be investigated.…”

Section: Discussionmentioning

confidence: 99%

Citizen Scientists Contribute to Real-Time Monitoring of Lake Water Quality Using 3D Printed Mini Secchi Disks

et al. 2021

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Citizen science aims to mobilise the general public, motivated by curiosity, to collect scientific data and contribute to the advancement of scientific knowledge. In this article, we describe a citizen science network that has been developed to assess the water quality in a 100 km long tropical lake-estuarine system (Vembanad Lake), which directly or indirectly influences the livelihood of around 1.6 million people. Deterioration of water quality in the lake has resulted in frequent outbreaks of water-associated diseases, leading to morbidity and occasionally, to mortality. Water colour and clarity are easily measurable and can be used to study water quality. Continuous observations on relevant spatial and temporal scales can be used to generate maps of water colour and clarity for identifying areas that are turbid or eutrophic. A network of citizen scientists was established with the support of students from 16 colleges affiliated with three universities of Kerala (India) and research institutions, and stakeholders such as houseboat owners, non-government organisations (NGOs), regular commuters, inland fishermen, and others residing in the vicinity of Vembanad Lake and keen to contribute. Mini Secchi disks, with Forel-Ule colour scale stickers, were used to measure the colour and clarity of the water. A mobile application, named “TurbAqua,” was developed for easy transmission of data in near-real time. In-situ data from scientists were used to check the quality of a subset of the citizen observations. We highlight the major economic benefits from the citizen network, with stakeholders voluntarily monitoring water quality in the lake at low cost, and the increased potential for sustainable monitoring in the long term. The data can be used to validate satellite products of water quality and can provide scientific information on natural or anthropogenic events impacting the lake. Citizens provided with scientific tools can make their own judgement on the quality of water that they use, helping toward Sustainable Development Goal 6 of clean water. The study highlights potential for world-wide application of similar citizen-science initiatives, using simple tools for generating long-term time series data sets, which may also help monitor climate change.

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

Cited by 37 publications

References 52 publications

Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

Measurement of Water Leaving Reflectance Using a Digital Camera Based on Multiple Reflectance Reference Cards

The Color of Rivers

Citizen Scientists Contribute to Real-Time Monitoring of Lake Water Quality Using 3D Printed Mini Secchi Disks

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