Evaluation and optimization of bio‐optical inversion algorithms for remote sensing of Lake Superior's optical properties

Mouw, Colleen B.; Chen, Haidi; McKinley, Galen A.; Effler, Steven W.; O’Donnell, David M.; Perkins, MaryGail; Strait, Chris

doi:10.1002/jgrc.20139

Cited by 32 publications

(26 citation statements)

References 56 publications

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“…At high levels of biomass in the MB, absorption is almost entirely due to phytoplankton. Our determination of the relative absorption budget for Lake Erie differs significantly compared to similarly depicted data from Lake Superior (Mouw et al, 2013a) and the global oceans (Figure 4 inset). Lake Superior is a system dominated by CDOM absorption with minimal phytoplankton and non-algal particle absorption, and occupies a region in the ternary plot that has no overlap with the Erie data.…”

Section: Absorptioncontrasting

confidence: 56%

“…Knowing the relative contribution to absorption provides insight into the retrieval accuracy of individual components from bio-optical algorithms. Mouw et al (2013a) showed that the accuracy of total absorption was good from algorithm inversions for Lake Superior, while the CDOM absorption component fared better than the phytoplankton absorption component. Given Lake Superior is a CDOM-dominated system with a very small contribution from phytoplankton to the overall absorption budget, the retrieval of chlorophyll concentration via inversion algorithms was not possible due to errors in derived CDOM absorption being greater than phytoplankton absorption values.…”

Section: Absorptionmentioning

confidence: 99%

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Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

et al. 2017

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There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Toward bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135 µg/L across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443 nm > 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best Moore et al. Lake Erie IOPs algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies.

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Section: Absorptioncontrasting

confidence: 56%

Section: Absorptionmentioning

confidence: 99%

Bio-optical Properties of Cyanobacteria Blooms in Western Lake Erie

et al. 2017

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“…In this study, images were processed with default settings in SeaDAS. Mouw et al (2013) found that the standard atmospheric correction worked best over Lake Superior. However, this evaluation was based on the blue-green region.…”

Section: Discussionmentioning

confidence: 99%

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters

Moore

Dowell

Bradt

et al. 2014

Remote Sensing of Environment

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Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

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“…Ice cover is prevalent in winter and thus wintertime data are essentially non-existent. Satellite algorithms for biogeochemistry have only recently been developed (Mouw et al, 2013). The overall lack of understanding of the carbon cycle and related biogeochemical processes is of particular concern in light of the desire to better manage the lakes so that they can continue to sustainably support ecosystems and economies in the face of the many anthropogenic stressors (Great Lakes Restoration Initiative 2010, http://greatlakesrestoration.us/pdfs/glri_actionplan.pdf), including ocean acidification (NOAA, 2010, Phillips et al, 2015.…”

Section: Regional Settingmentioning

confidence: 99%

“…In addition, algorithms may not perform well in optically complex coastal waters and where the bottom contributes to the measured above-water reflectance. To some extent, these limitations can be addressed through the use of regionally tuned algorithms and use of multiple sensors with differing spatial resolution and temporal coverage (e.g., Werdell et al, 2009;Le et al, 2013;Aurin et al, 2013;Mouw et al 2013). Continuity of ocean color observations is another concern for the continued future applications of satellite algorithms (NRC, 2011), and such continuity is critical for the ability to detect trends in NPP in relation to other factors such as climate and anthropogenic impacts (e.g., Beaulieu et al, 2012).…”

Section: Biological Transformationsmentioning

confidence: 99%