Evaluation of Nearshore and Offshore Water Quality Assessment Using UAV Multispectral Imagery - Ryan McEliece; Shawn Hinz; Jean-Marc Guarini; Jennifer Coston-Guarini (Remote Sensing, 2020)
A cost-effective technology has emerged which combines multispectral sensors mounted on Unmanned Aerial Vehicles (UAVs). This technology has a promising potential for monitoring water quality in coastal environments. Our study aimed at evaluating this technology to infer the spatial distribution of chlorophyll a concentration [Chl-a] (in µg·L−1) and turbidity (FNU) in surface waters. The multispectral sensor measured reflectance at 4 distinct wavelength bands centered on 448 nm, 494 nm, 550 nm and 675 nm, hence providing 4 datasets {R(448), R(494), R(550), R(675)}. We investigated the potential of estimating [Chl-a] and turbidity based on reflectance ratios and indexes calculated from two different wavelength bands. The calibration functions were formulated based on the property that any of the reflectance measurements was linearly correlated to any other one. The calibration was performed from 35 measurements of reflectance, [Chl-a] and turbidity collected in seven sites in the U.K. between May and August 2017. Two calibration functions derived from the index δ=(R(550) − R(448))/(R(550) + R(448)) presented the best fit and explained 78% of the total variance for [Chl-a] and 74% for turbidity measurements, respectively. Calibration functions were then inversed to estimate [Chl-a] and turbidity from reflectance measurements. Finally, we performed a validation test using independent measurements from three sites in France, in July 2017. The resulting maps show a pattern with higher [Chl-a] in lower turbidity areas. However, discrepancies between the observed and re-calculated values and difficulties in validating low turbidity values suggest that site-specific calibrations should be performed at each investigated location.