{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T10:10:48Z","timestamp":1773310248748,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This study is supported by the High Resolution Earth Observation Systems of National Science and Technology Major Projects","award":["05-Y30B01-9001-19\/20-2"],"award-info":[{"award-number":["05-Y30B01-9001-19\/20-2"]}]},{"name":"the National Key Research and Development Program of China","award":["2016YFC1400901"],"award-info":[{"award-number":["2016YFC1400901"]}]},{"name":"the National Science Foundation of China","award":["61991454"],"award-info":[{"award-number":["61991454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided and compared against different existing machine learning algorithms with a single training set. For a single training set and a single machine learning method, our analysis shows that the proposed depth estimation method provides a better root-mean-square error (RMSE) and a higher coefficient of determination (R2) under turbid water conditions, with overall RMSE and R2 improvements of 1 cm and 0.7, respectively. The developed method may be employed in monitoring dredging activities, especially in areas with polluted water, mud and\/or a high sediment content.<\/jats:p>","DOI":"10.3390\/rs13214328","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"4328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhongqiang","family":"Wu","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China"},{"name":"States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Natural Resources Ministry, Hangzhou 310012, China"},{"name":"School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0066-1808","authenticated-orcid":false,"given":"Zhihua","family":"Mao","sequence":"additional","affiliation":[{"name":"States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Natural Resources Ministry, Hangzhou 310012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Marine Science, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Marine Surveying and Mapping Engineering and Technology Research Center, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0921-8009(99)00009-9","article-title":"Ecological goods and services of coral reef ecosystems","volume":"29","author":"Moberg","year":"1999","journal-title":"Ecol. 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