Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (L W ) and incident radiance reflected off the sea surface into the detector’s field of view (L SR ). L W is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while L SR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for L SR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove L SR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove L SR . Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of L SR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.