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Open AccessArticle

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Department of Marine Sciences, University of Connecticut, 1080 Shennecossett Rd, Groton, CT 06269, USA
Vlaams Instituut voor de Zee (VLIZ), 8400 Ostend, Belgium
Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Department of Environmental, Earth and Ocean Sciences, University of Massachusetts Boston, Morrissey Blvd., Boston, MA 02125, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1664;
Received: 29 April 2019 / Revised: 24 June 2019 / Accepted: 29 June 2019 / Published: 12 July 2019
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
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Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality. View Full-Text
Keywords: hyperspectral airborne imagery; eelgrass; optically shallow water; ocean color; prism; water quality; bathymetry hyperspectral airborne imagery; eelgrass; optically shallow water; ocean color; prism; water quality; bathymetry

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Dierssen, H.M.; Bostrom, K.J.; Chlus, A.; Hammerstrom, K.; Thompson, D.R.; Lee, Z. Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California. Remote Sens. 2019, 11, 1664.

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