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Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

1
Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg. 3F, 3-17-1 Toranomon, Minato-ku, Tokyo 105-0001, Japan
2
Bestmateria, 2-43-15 Misawa, Hino-shi, Tokyo 191-0032, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1155; https://doi.org/10.3390/rs11101155
Received: 28 March 2019 / Revised: 29 April 2019 / Accepted: 12 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Satellite Derived Bathymetry)
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Abstract

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions. View Full-Text
Keywords: satellite derived bathymetry; shallow water; machine learning; random forest; Google Earth Engine; multi-temporal; Landsat-8 satellite derived bathymetry; shallow water; machine learning; random forest; Google Earth Engine; multi-temporal; Landsat-8
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sagawa, T.; Yamashita, Y.; Okumura, T.; Yamanokuchi, T. Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images. Remote Sens. 2019, 11, 1155.

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