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