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Remote Sens. 2017, 9(4), 393; doi:10.3390/rs9040393

Hyperspectral and Multispectral Retrieval of Suspended Sediment in Shallow Coastal Waters Using Semi-Analytical and Empirical Methods

1
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
2
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
3
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
4
Department of Civil, Architectural and Environmental Engineering, University of Padova, Padova 35131, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 27 January 2017 / Revised: 8 April 2017 / Accepted: 16 April 2017 / Published: 21 April 2017
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Abstract

Natural lagoons and estuaries worldwide are experiencing accelerated ecosystem degradation due to increased anthropogenic pressure. As a key driver of coastal zone dynamics, suspended sediment concentration (SSC) is difficult to monitor with adequate spatial and temporal resolutions both in the field and using remote sensing. In particular, the spatial resolutions of currently available remote sensing data generated by satellite sensors designed for ocean color retrieval, such as MODIS (Moderate Resolution Imaging Spectroradiometer) or SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), are too coarse to capture the dimension and geomorphological heterogeneity of most estuaries and lagoons. In the present study, we explore the use of hyperspectral (Hyperion) and multispectral data, i.e., the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and ALOS (Advanced Land Observing Satellite), to estimate SSC through semi-analytical and empirical approaches in the Venice lagoon (Italy). Key parameters of the retrieval models are calibrated and cross-validated by matching the remote sensing estimates of SSC with in situ data from a network of water quality sensors. Our analysis shows that, despite the higher spectral resolution, hyperspectral data provide limited advantages over the use of multispectral data, mainly due to information redundancy and cross-band correlation. Meanwhile, the limited historical archive of hyperspectral data (usually acquired on demand) severely reduces the chance of observing high turbidity events, which are relatively rare but critical in controlling the coastal sediment and geomorphological dynamics. On the contrary, retrievals using available multispectral data can encompass a much wider range of SSC values due to their frequent acquisitions and longer historical archive. For the retrieval methods considered in this study, we find that the semi-analytical method outperforms empirical approaches, when applied to both the hyperspectral and multispectral dataset. Interestingly, the improved performance emerges more clearly when the data used for testing are kept separated from those used in the calibration, suggesting a greater ability of semi-analytical models to “generalize” beyond the specific data set used for model calibration. View Full-Text
Keywords: suspended sediment concentration; hyperspectral and multispectral data; radiative-transfer model; empirical model suspended sediment concentration; hyperspectral and multispectral data; radiative-transfer model; empirical model
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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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Zhou, X.; Marani, M.; Albertson, J.D.; Silvestri, S. Hyperspectral and Multispectral Retrieval of Suspended Sediment in Shallow Coastal Waters Using Semi-Analytical and Empirical Methods. Remote Sens. 2017, 9, 393.

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