标题：Field‐Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel‐3A OLCI and Ground‐Based Hyperspectral Spectroradiometers
摘要：AbstractFrequentAureoumbra lagunensisblooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early‐warning system for harmful algal blooms, we apply varimax‐rotated principal component analysis (VPCA) to 12 images spanning ~1.5 years. The method partitions visible‐NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel‐3A Ocean and Land Colour Instrument (OLCI) and a field‐based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated withA. lagunensisin both spectral datasets indicate high correlations to Ochrophyta cell counts (R2 ≥ 0.92,p < 0.001). Recombining components exhibiting a red‐edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel‐3A OLCI‐based VPCA spectral identification and indicateA. lagunensiswas highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.Plain Language SummaryToxic or nuisance blooms of microscopic plankton are causing environmental, economic, and public health problems in Indian River Lagoon, Florida, and other coastal waters. Monitoring from boats can be expensive compared to remote sensing methods, but the remote sensing signal must be validated. Here we present results that document that the brown tide that develops in the Indian River Lagoon can be identified with very little error using different types of supporting data sets. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.Key PointsA novel remote sensing method sees brown tide in Indian River Lagoon, Florida, separate from suspended sediment and cyanobacteriaHandheld optical instrument and satellite measurements, confirmed by cell counts, and pigment concentrations agree with minimal errorThe water quality harmful algal bloom identification approach can be used with a variety of sensors across a wide range of water bodies
