Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission
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Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission

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  • Journal Title:
    Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    NOAA General Documents
  • Description:
    Satellite-Derived Bathymetry (SDB) has significant potential to enhance our knowledge of Earth’s coastal regions. However, SDB still has limitations when applied to the turbid, but optically shallow, nearshore regions that encompass large areas of the world’s coastal zone. Turbid water produces false shoaling in the imagery, constraining SDB for its routine application. This paper provides a framework that enables us to derive valid SDB over moderately turbid environments by using the high revisit time (5-day) of the Sentinel-2A/B twin mission from the Copernicus programme. The proposed methodology incorporates a robust atmospheric correction, a multi-scene compositing method to reduce the impact of turbidity, and a switching model to improve mapping in shallow water. Two study sites in United States are explored due to their varying water transparency conditions. Our results show that the approach yields accurate SDB, with median errors of under 0.5 m for depths 0–13 m when validated with lidar surveys, errors that favorably compare to uses of SDB in clear water. The approach allows for the semi-automated creation of bathymetric maps at 10 m spatial resolution, with manual intervention potentially limited only to the calibration to the absolute SDB. It also returns turbidity data to indicate areas that may still have residual shoaling bias. Because minimal in-situ information is required, this computationally-efficient technique has the potential for automated implementation, allowing rapid and repeated application in more environments than most existing methods, thereby helping with a range of issues in coastal research, management, and navigation.
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  • Source:
    Remote Sensing, 12(3), 451
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  • ISSN:
    2072-4292
  • Format:
    PDF
  • Publisher:
    MDPI AG
  • Document Type:
    Journal Article
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    CC BY
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    Library
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