Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP and AOP observations have been temporally limited and have not assessed statistical differences between IOPs over spatial and temporal gradients. The objective of this study is to assess trends in IOPs over variable spatial and temporal scales. Large spatio-temporal variability in IOPs was observed between 2015 and 2016 likely due to the difference in the extent and duration of mid-summer cyanobacteria blooms. Differences in the seasonal trends of the specific phytoplankton absorption coefficient between 2015 and 2016 suggest differing algal assemblages between the years. Other IOP variables, including chromophoric, dissolved organic matter (CDOM) and beam attenuation spectral slopes, suggest variability is influenced by river discharge and sediment re-suspension. The datasets presented in this study show how these IOPs and AOPs change over a season and between years, and are useful in advancing the applicability and robustness of remote sensing methods to retrieve water quality information in western Lake Erie.     

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

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).