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Open AccessEditor’s ChoiceArticle

Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach

1
Department of Geography, University of California at Los Angeles, Los Angeles, CA 90095, USA
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
3
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
4
Department of Geological Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
5
School of the Coast and Environment, Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1629; https://doi.org/10.3390/rs11131629
Received: 11 May 2019 / Revised: 19 June 2019 / Accepted: 27 June 2019 / Published: 9 July 2019
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids. View Full-Text
Keywords: Remote sensing; imaging spectroscopy; hyperspectral; multispectral; AVIRIS-NG; sediment; total suspended solids (TSS) Remote sensing; imaging spectroscopy; hyperspectral; multispectral; AVIRIS-NG; sediment; total suspended solids (TSS)
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Jensen, D.; Simard, M.; Cavanaugh, K.; Sheng, Y.; Fichot, C.G.; Pavelsky, T.; Twilley, R. Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach. Remote Sens. 2019, 11, 1629.

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