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

Abstract

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 (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW 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 LSR 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 LSR 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 LSR 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 LSR. 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 LSR 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.

Applying multispectral UAV imagery to delineate in and near stream cover along a small urban stream

“…As discussed in previous sections, our findings point to the need for mission planning that may achieve the greatest transferability in high quality imagery through time. In addition, though our analysis focuses on purely RGB data combined with one analysis technique (LDA), the use of multispectral bands or indices (e.g., normalized difference vegetation index, normalized difference water index) may also be applied (e.g., McFeeters, 1996; Windle & Silsbe, 2021; O'Sullivan et al, 2020) to resolve heterogeneity within the water column.…”

Drones address an observational blind spot for biological oceanography

“…2021). This promising approach, alongside other recent work evaluating common techniques for removing reflected skylight (Windle and Silsbe 2021), can help ensure high‐quality data for a variety of drone platform and sensor combinations.…”

“…Recently, an imageprocessing approach using image texture as a glint indicator was developed for removing glint from imagery (Cavanaugh et al 2021). This promising approach, alongside other recent work evaluating common techniques for removing reflected skylight (Windle and Silsbe 2021), can help ensure highquality data for a variety of drone platform and sensor combinations.…”

Robust ocean color from drones: Viewing geometry, sky reflection removal, uncertainty analysis, and a survey of the Gulf Stream front

“…A large and growing repertoire of techniques are available to address challenges of glare and reflection in aerial imagery of ocean color (Mobley 1999; Lee et al 2010; Zhang et al 2017; O'Shea et al 2020; Windle and Silsbe 2021). One approach that is widely used and easily implemented uses three measurements to obtain R rs : total sea radiance ( L t ), sky radiance ( L sky ), and downwelling irradiance ( E d ) (Fig.…”