Abstract
Ultraviolet (UV)-visible imaging spectroscopy is an emerging and highly anticipated technology, expected to improve the remote sensing of coastal waters and expand its range of applications. Upcoming NASA satellite missions including PACE and GLIMR will feature imaging spectrometers capable of measuring hyperspectral remote-sensing reflectance (Rrs) across the visible range and well into the near-infrared and ultraviolet domains. The availability of UV reflectance is expected to facilitate the remote sensing of chromophoric dissolved organic matter (CDOM) in optically complex waters, thereby improving coastal water-quality monitoring. Although this argument is well supported by the dominance of CDOM absorption in the UV domain, few studies have directly evaluated the potential advantages conferred by UV reflectance for monitoring CDOM-related coastal water quality. Here, we took advantage of a 6-week wastewater diversion event in Santa Monica Bay, California in 2015 and the availability of Portable Remote Imaging SpectroMeter (PRISM) imagery acquired during the diversion to assess if UV-visible imaging spectroscopy could facilitate the detection of CDOM and help differentiate wastewater effluent-derived CDOM from other sources. A comparison of local empirical algorithms with varying amounts of spectral information implemented on PRISM data showed that incorporating UV Rrs as a predictor significantly improved retrieval of CDOM absorption coefficients (ag). Optimal performance was reached when combining Rrs(365), Rrs(400), and Rrs(700) as predictors of ag in a multiple linear regression. The use of the entire UV-visible spectrum (365–700 nm) in a partial-least-squares regression (PLSR) did not improve retrievals, indicating that a few carefully chosen predictors in the UV-visible domain were sufficient to empirically differentiate CDOM from phytoplankton in coastal waters minimally influenced by sediments or bottom reflectance. Finally, the development of a new fluorescence-based indicator of effluent-derived CDOM (effluent fluorescence ratio, EFR) helped demonstrate the feasibility of remotely detecting CDOM from wastewater. A PLSR-based algorithm using Rrs(365–700) provided reasonable EFR retrievals and successfully identified effluent-derived CDOM at the wastewater outfall when implemented on PRISM imagery. Although further work should investigate the influence of effluent-CDOM fluorescence on Rrs more mechanistically, these results confirmed that UV-visible imaging spectrometers can facilitate coastal CDOM-related water quality monitoring and expand its range of applications.
Highlights
Urban coastal waters are productive environments that provide important ecosystem services to humans, including the dilution of terrestrial inputs (IOCCG, 2008; Rabalais et al, 2009; McLaughlin et al, 2017), fisheries and aquaculture, and various recreational and transportation services (Halpern et al, 2012; Caron et al, 2017; Gierach et al, 2017)
From in-situ stations where both Rrs(λ) and excitationemission matrix (EEM) fluorescence were measured (n 40), we developed empirical algorithms for inferring the ratio between the mean intensity of an EEM fluorescence peak associated with effluent, FE, (340–360 nm excitation and 426–454 nm emission), and one that was indicative of runoff-influenced Chromophoric dissolved organic matter (CDOM), FR, (255–265 nm excitation and 382–398 nm emission) from remote sensing reflectance
The spatial distribution of CDOM absorption in Santa Monica Bay was very variable over the course of the fall sampling, as local inputs from riverine and wastewater effluent sources were added to the bay (Figure 2)
Summary
Urban coastal waters are productive environments that provide important ecosystem services to humans, including the dilution of terrestrial inputs (IOCCG, 2008; Rabalais et al, 2009; McLaughlin et al, 2017), fisheries and aquaculture, and various recreational and transportation services (Halpern et al, 2012; Caron et al, 2017; Gierach et al, 2017). Empirical algorithms for deriving bio-optical properties from Rrs(λ) were calibrated using in-situ data including stations where Rrs(λ) was measured along with CDOM absorption (n 41). From in-situ stations where both Rrs(λ) and EEM fluorescence were measured (n 40), we developed empirical algorithms for inferring the ratio between the mean intensity of an EEM fluorescence peak associated with effluent, FE, (340–360 nm excitation and 426–454 nm emission), and one that was indicative of runoff-influenced CDOM, FR, (255–265 nm excitation and 382–398 nm emission) from remote sensing reflectance (see Results). Multiple linear regression and partial least squares regression were implemented using MATLAB version R2018b
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