Field-validated detection of Aureoumbra lagunensis brown tide blooms in
the Indian River Lagoon, Florida using Sentinel-3A OLCI and ground-based
hyperspectral spectroradiometers
Abstract
Frequent Aureoumbra lagunensis blooms 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 OLCI and a field-based
spectroradiometer are consistent despite vast differences in spatial
resolution (~50 cm vs. 300 m). Furthermore, the VPCA
components associated with A. lagunensis in 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
semi-analytical algorithms. The results from the VPCA spectral
decomposition method are more efficient than traditional EOF 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 indicate A. lagunensis was 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.