Abstract
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.
Highlights
With the rapid development of modern big data and communication technologies, environmental quality monitoring has entered the era of crowdsourcing big data [1,2,3]
The purpose of this study was to develop a method to simulate the nonlinear relationship between the digital pixel number (DN) and incident light radiance by multiple reflectance reference cards, thereby deriving water leaving reflectance from the nonlinear corrected DN in digital images
The dot–dash curve is the power function simulated by the four reflectance reference cards, which is the basis of the approach in this study
Summary
With the rapid development of modern big data and communication technologies, environmental quality monitoring has entered the era of crowdsourcing big data [1,2,3]. Citizen science becomes an important source in collecting crowdsourcing big data to provide more valuable scientific data [4]. Citizen science refers to the involvement of the community and collecting data by these non-professionals in organized research endeavors [5]. The participation of citizen scientists in environmental data collection can complement traditional monitoring methods and. Because it has many potential advantages, such as reduced monitoring costs, increased data coverage, enhanced support for decision-making, and enhanced potential for knowledge co-creation [6,7,8]. In developing countries where the availability of data and the financial resources are limited, such an approach helps to expand the monitoring network in a cost-effective way [11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
