Evaluation and performance of satellite-derived bathymetry algorithms in turbid coastal water: a case study of Vengurla rocks
The coastal region bathymetry is increasingly becoming necessary for emerging needs of navigation and development along coasts. Satellite-Derived Bathymetry (SDB) has been examined as a viable alternative for hydrographic surveys for the past few decades to reduce the data acquisition efforts in coastal regions and augmentation of periodic updating of Electronic Navigational Charts (ENCs). The previous studies have applied SDB algorithms in less complex waters due to the limitations of SDB algorithms in turbid and varying shallow waters. This paper analyses three different medium-resolution satellite imagery data to derive bathymetry in a navigably very complex and highly turbid region, Vengurla rocks, situated on the west coast of India. The objective of the study was to evaluate the best suitable technique for SDB in turbid water. The bathymetry product images have been derived using the two most commonly utilized log ratio, and linear ratio transformation; three semi-automated methods, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and ratio transform; and Machine Learning (ML) regression algorithms. Among the applied transform and algorithms, the ML algorithm using 561 nm band data performed the best, resulting in R2 of 0.77, RMSE of 3.4 m, and MAE of 2.8 m. This work established that open source images of sensor OLI/Landsat-8 satellite provide the best results of SDB estimation in complex turbid water by applying ML algorithms. However, extreme turbid and complex regions resulted in more erroneous SDB estimation specifying the need for refining algorithms using bio-optical parameters.
Bathymetry, Coastal region, Depth estimation, Machine learning, Satellite-derived bathymetry
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