Smartphone-Camera-Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion

Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low-cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN-to-reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi-disk depth (Z(sd)()) and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy.

Automated summary

This study proposes a method to extract more accurate water reflectance data from smartphone photographs and tests the usability of these data in deriving water quality parameters. The results demonstrate that the proposed method based on the smartphone camera can effectively derive remote sensing reflectance and water quality parameters with acceptable accuracy.