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J. Mar. Sci. Eng. 2016, 4(1), 8;

Deriving Bathymetry from Multispectral Remote Sensing Data

NOAA-CREST, University of Puerto Rico Mayagüez, Mayagüez, Puerto Rico 00681, USA
Author to whom correspondence should be addressed.
Academic Editor: Magnus Wahlberg
Received: 2 November 2015 / Revised: 11 January 2016 / Accepted: 21 January 2016 / Published: 2 February 2016
(This article belongs to the Section Physical Oceanography)
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The use of passive satellite sensor data in shallow waters is complicated by the combined atmospheric, water, and bottom signals. Accurate determination of water depth is important for monitoring underwater topography and detection of moved sediments and in support of navigation. A Worldview 2 (WV2) image was used to develop high-resolution bathymetric maps (four meters) that were validated using bathymetry from an active sensor Light Detection and Ranging (LiDAR). The influence of atmospheric corrections in depth retrievals was evaluated using the Dark Substract, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and the Cloud Shadow Approach (CSA) atmospheric corrections. The CSA combined with a simple band ratio (Band2/Band3) provided the best performance, where it explained 82% of model values. The WV2 depth model was validated at another site within the image, where it successfully retrieved depth values with a coefficient of determination (r2) of 0.90 for all the depth values sampled, and an r2 of 0.70, for a depth range to 20 m. The WV2 bands in the visible region were useful for testing different band combinations to derive bathymetry that, when combined with a robust atmospheric correction, provided depth retrievals even in areas with variable bottom composition and near the limits of detection. View Full-Text
Keywords: bathymetry; passive sensors; WV2; Puerto Rico; atmospheric correction; cloud shadow approach (CSA) bathymetry; passive sensors; WV2; Puerto Rico; atmospheric correction; cloud shadow approach (CSA)

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Hernandez, W.J.; Armstrong, R.A. Deriving Bathymetry from Multispectral Remote Sensing Data. J. Mar. Sci. Eng. 2016, 4, 8.

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