Presenter: Anna Windle (University of Maryland Center for Environmental Science)

Description:
Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can be used to derive water quality properties in coastal waters. While atmospheric effects can usually be ignored in low altitude UAS flights, the effect of sun glint and surface reflected light needs to be accounted for to obtain accurate water quality data. The at-sensor total radiance a UAS measures 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 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 remote sensing reflectance (R_rs), resulting in inaccurate water quality estimates once water quality algorithms are applied. This study evaluates 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 R_rs measurements to determine the best performing method of estimating and removing surface reflected light. UAS-based applications of high resolution multispectral or hyperspectral remote sensing in aquatic environments have the potential to effectively fill current observation gaps and provide critical information needed for ecosystem monitoring, tracking water quality trends, and rapid assessment of events such as harmful algal blooms.