Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data
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Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data

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  • Journal Title:
    Journal of Great Lakes Research
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  • Description:
    Timely identification of color-producing agents (CPAs) in Lake Erie is a challenging, but vital aspect of monitoring harmful algal blooms (HABs). In particular, HABs that include large amounts of cyanobacteria (CyanoHABs) can be toxic to humans, posing a threat to drinking water, in addition to recreational and economic use of Lake Erie. The optical signal of Lake Erie is complex (Becker et al., 2009; Moore et al., 2017), typically comprised of phytoplankton, cyanobacteria, colored dissolved organic matter (CDOM), detritus, and terrigenous inorganic particles, varying in composition both spatially and temporally. The Kent State University (KSU) spectral decomposition method effectively partitions CPAs using a varimax-rotated, principal component analysis (VPCA) of visible reflectance spectra measured using lab, field or satellite instruments (Ali et al., 2013; Ortiz et al., 2017, 2013). We analyze 2015 imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and field samples collected during the early 2015 cyanoHAB season. We identified four primary CPA spectral signatures, and the spatial distribution of each identified CPA, in the reflectance spectra datasets of both the MODIS and lab-measured water samples. The KSU spectral decomposition method results in mixtures of specific pigments, pigment degradation products, and minerals that describe the optically complex water. We found very good agreement between the KSU VPCA spectral decomposition results and in situ measurements, indicating that this method may be a powerful tool for rapid CyanoHAB monitoring and assessment in large lakes using instruments that provide moderate resolution imagery (0.3 to 1 km2).
  • Source:
    Journal of Great Lakes Research 45(3): 466-479
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