Abstract
Monitoring the dynamics of the productivity of ocean water and how it affects fisheries is essential for management. It requires data on proper spatial and temporal scales, which can be provided by operational ocean colour satellites. However, accurate productivity data from ocean colour imagery is only possible with proper validation of, for instance, the atmospheric correction applied to the images. In situ water reflectance data are of great value due to the requirements for validation and reflectance is traditionally measured with the Surface Acquisition System (SAS) solar tracker system. Recently, an application for mobile devices, “HydroColor”, was developed to acquire water reflectance data. We examined the accuracy of the water reflectance measures acquired by HydroColor with the help of both trained and untrained citizens, under different environmental conditions. We used water reflectance data acquired by SAS solar tracker and by HydroColor onboard the BC ferry Queen of Oak Bay from July to September 2016. Monte Carlo permutation F tests were used to assess whether the differences between measurements collected by SAS solar tracker and HydroColor with citizens were significant. Results showed that citizen HydroColor measurements were accurate in red, green, and blue bands, as well as red/green and red/blue ratios under different environmental conditions. In addition, we found that a trained citizen obtained higher quality HydroColor data especially under clear skies at noon.
Highlights
Improved understanding of the long-term spatio-temporal productivity of coastal oceans is of fundamental importance for the management of natural resources, especially fisheries [1]
We have conducted the first evaluation of the accuracy of Hydrocolor samples collected by citizens aboard a ferry based on a thorough statistical analysis
We have shown that water reflectance measurements acquired by HydroColor can be used for data acquisition with trained citizen scientists; we must be careful when using this method with untrained citizen science
Summary
Improved understanding of the long-term spatio-temporal productivity of coastal oceans is of fundamental importance for the management of natural resources, especially fisheries [1]. Operational ocean colour satellites such as MODIS-AQUA, VIIRS, and Sentinel-3 can provide data at the required scale; data validation is required [2,3]. These satellites measure water reflectance at different wavelengths, which is the basis for the models used to derive concentration of chlorophyll, a proxy for the ocean’s productivity [4]. Limitations on the use of satellite-derived reflectance measurements to retrieve accurate chlorophyll concentrations exist [5] These limitations are generally a function of inaccurate atmospheric correction of the satellite measured signal and inability to relate this signal to chlorophyll concentrations [6,7,8,9]
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