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Abstract
Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.
Publication Date
8-15-2024
Journal Title
Journal of Marine Science and Engineering
Rights
© 2024 by the authors
Publisher
MDPI
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Lowell, K.; Hermann, J. Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning. J. Mar. Sci. Eng. 2024, 12, 1401. https://doi.org/10.3390/jmse12081401
Comments
This is an open access article published by MDPI in Journal of Marine Science and Engineering in 2024, available online: https://dx.doi.org/10.3390/jmse12081401