{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:06:29Z","timestamp":1782932789051,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["41825014"],"award-info":[{"award-number":["41825014"]}]},{"name":"the National Natural Science Foundation of China","award":["42176177"],"award-info":[{"award-number":["42176177"]}]},{"name":"the National Natural Science Foundation of China","award":["GML2019ZD0602"],"award-info":[{"award-number":["GML2019ZD0602"]}]},{"name":"the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["41825014"],"award-info":[{"award-number":["41825014"]}]},{"name":"the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["42176177"],"award-info":[{"award-number":["42176177"]}]},{"name":"the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","award":["GML2019ZD0602"],"award-info":[{"award-number":["GML2019ZD0602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (Rrs\u03bb) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which Rrs\u03bb were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from Rrs\u03bb using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R2) was greater than 0.98, and the root mean squared error (RMSE) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.<\/jats:p>","DOI":"10.3390\/rs14184590","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5050-4739","authenticated-orcid":false,"given":"Yuxin","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianqiang","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Donghai Laboratory, Zhoushan 316021, China"},{"name":"School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Bai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7747-3082","authenticated-orcid":false,"given":"Difeng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiankun","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s10533-010-9424-7","article-title":"Metabolism of Different Benthic Habitats and Their Contribution to the Carbon Budget of a Shallow Oligotrophic Sub-Tropical Coastal System (Southern Moreton Bay, Australia)","volume":"102","author":"Eyre","year":"2011","journal-title":"Biogeochemistry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12377","DOI":"10.1073\/pnas.0905620106","article-title":"Accelerating Loss of Seagrasses across the Globe Threatens Coastal Ecosystems","volume":"106","author":"Waycott","year":"2009","journal-title":"Proc. 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