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5 October 2018 Coastal water bathymetry retrieval using high-resolution remote sensing data
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Abstract
In this study an optical empirical algorithm was used to evaluate the robustness of some experimental processes to derive Satellite-Derived Bathymetric (SDB) models for shallow waters in the Minho river mouth, Caminha, Portugal. Multiple procedures to calibrate and modelling SDB calculation were tested. Regarding to these procedures, the following approaches were studied: i) Based on TOA and WL reflectance, a linear regression, as proposed by Stumpf et al.[3], and a quadratic regression to modelling the SDB algorithm were tested; (ii) several in situ data sets acquired at different epochs were used in order to investigate the dependency of the temporal and the spatial density of the calibration sample; (iii) The tidal level was considered in order to provide a tide height at the time of image acquisition to depth models calibration. Numerous SDB models from Sentinel-2 imagery were derived and compared with a reference hydrographic survey data. The results show that SDB models present a good level of reliability regardless of the acquisition date or source of the in situ datasets. Coefficients of determination (R2) higher than 50% were obtained for the majority of the tested procedures. The quadratic modelling approach also appear to retrieve SDB models in agreement with hydrographic surveys data. In addition, the results demonstrate that the SDB information is influenced by the spatial density variation of the calibration datasets. Furthermore, the operational capabilities of the synergy of optical and synthetic aperture radar (SAR) data to derive SDB information in shallow waters will be briefly discussed.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pedro Vilar, Ana Moura, Luísa Lamas, Rui Guerreiro, and José Paulo Pinto "Coastal water bathymetry retrieval using high-resolution remote sensing data ", Proc. SPIE 10784, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, 1078408 (5 October 2018); https://doi.org/10.1117/12.2324466
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