Abstract
Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.
Highlights
Eutrophication threatens the sustainability of lake ecosystems, well-being (Cox et al, 2006; Wheeler et al, 2012), and economies (Dodds et al, 2009) of communities around the world (UNEP, 2007; Wilson & Fischetti, 2010)
It may be possible for chl-a concentrations to range up to 5,000 μg L−1 in cyanobacteria scum conditions or approach 50,000 μg L−1 with wind-induced concentrations of scums at the surface (Chorus & Bartram, 1999)
Chl-a serves as a proxy for phytoplankton biomass and is an ecologically important indicator of aquatic ecosystem health and condition
Summary
Eutrophication threatens the sustainability of lake ecosystems, well-being (Cox et al, 2006; Wheeler et al, 2012), and economies (Dodds et al, 2009) of communities around the world (UNEP, 2007; Wilson & Fischetti, 2010). Algae and cyanobacteria can proliferate, outcompeting native aquatic flora and fauna and threatening aquatic ecosystems. Some cyanobacteria create compounds toxic to humans and livestock, leading to public health and socio-economic risks (Stroming et al, 2020). For these reasons, understanding eutrophication is crucial. Chlorophyll-a (chl-a) concentration is often the targeted water quality indicator for nutrient eutrophication (Schaeffer et al, 2012), as its presence in water tends to originate from algae and cyanobacteria growth responses to nutrient availability
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