Satellite Derived Bathymetry
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 50940
Special Issue Editor
Interests: ocean optics; shallow-water earth observation; atmospheric correction; bathymetry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Estimation of water depth from satellite imagery, commonly referred to as satellite-derived bathymetry (SDB), is desirable for scientific, military, resource management, and safe transportation purposes. SDB dates back to the 1970s, when empirical methods that rely on georeferenced observations of known depth to calibrate a statistical relationship between water depth and pixel colour were developed. These methods are simple to implement and are still in use today, but in addition to calibration data, they rely on simplifying assumptions concerning seafloor spectral reflectance and also assume spatially homogeneous water quality. Alternative methods based on inversion of radiative transfer models do not rely on these assumptions, nor on calibration data, but they instead require precise radiometry (sensor calibration and atmospheric and sea surface correction) that is not always achievable. Hybrid methods that seek to combine the strengths of each method have also been developed, as have geostatistical and photogrammetric approaches. As economic activity in shallow waters intensifies and melting sea ice opens new routes for marine transportation, while sensors with improved spatial, spectral, and radiometric quality become available, SDB is increasingly used to quickly and cheaply map water depth over large or inaccessible areas. However, the large range of existing methods, most of which are not readily available in software, as well as their varying strengths and weaknesses, makes the optimal selection of data and the method for a specific SDB task difficult. This Special Issue, “Satellite-Derived Bathymetry”, calls for all papers that move forward our understanding of SDB, with specific interest in contributions that (1) illuminate strengths and weaknesses of different methods in different environmental contexts, (2) demonstrate and test the use of the uncertainty estimation, and (3) develop new, modified, or hybrid approaches, or (4) develop frameworks for the upscaling of SDB to achieve regional/global coverage.
Assoc. Prof. Dr. Anders Jensen Knudby
Guest Editor
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Keywords
- empirical, physics-based, and hybrid methods
- photogrammetry
- atmospheric correction
- deglinting
- inversion algorithms
- uncertainty estimation
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