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Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.<\/jats:p>","DOI":"10.3390\/rs15143570","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:56:47Z","timestamp":1689555407000},"page":"3570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0566-2831","authenticated-orcid":false,"given":"Xue","family":"Ji","sequence":"first","affiliation":[{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China"},{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9120-7354","authenticated-orcid":false,"given":"Jingyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9015-5131","authenticated-orcid":false,"given":"Wenxue","family":"Xu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Yanhong","family":"Wang","sequence":"additional","affiliation":[{"name":"First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Caballero, I., and Stumpf, R.P. (2020). Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A\/B Satellites Mission. Remote Sens., 12.","DOI":"10.3390\/rs12030451"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1364\/AO.17.000379","article-title":"Passive remote sensing techniques for mapping water depth and bottom features","volume":"17","author":"Lyzenga","year":"1978","journal-title":"Appl. Opt."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Caballero, I., Stumpf, R.P., and Meredith, A. (2019). Preliminary Assessment of Turbidity and Chlorophyll Impact on Bathymetry Derived from Sentinel-2A and Sentinel-3A Satellites in South Florida. Remote Sens., 11.","DOI":"10.3390\/rs11060645"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1364\/AO.420673","article-title":"Island features classification for single-wavelength airborne LiDAR bathymetry based on full-waveform parameters","volume":"60","author":"Ji","year":"2021","journal-title":"Appl. Opt."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1080\/01431160110075785","article-title":"Bathymetric mapping in Kakinada Bay, India, using IRS-1D LISS-III data","volume":"23","author":"Tripathi","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.icesjms.2005.03.007","article-title":"Acoustic detection of a scallop bed from a single-beam echosounder in the St. Lawrence","volume":"62","author":"Hutin","year":"2005","journal-title":"Ices J. Mar. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107387","DOI":"10.1016\/j.apacoust.2020.107387","article-title":"Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model","volume":"167","author":"Ji","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"501","DOI":"10.2112\/05-756A.1","article-title":"Interpretation of Seabed Geomorphology Based on Spatial Analysis of High-Density Airborne Laser Bathymetry","volume":"21","author":"Finkl","year":"2005","journal-title":"J. Coast. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8129","DOI":"10.1109\/TGRS.2021.3050789","article-title":"A Coarse-to-Fine Strip Mosaicing Model for Airborne Bathymetric LiDAR Data","volume":"59","author":"Ji","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01431160010008573","article-title":"Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data","volume":"23","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.4236\/ars.2015.42013","article-title":"A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons","volume":"4","author":"Jawak","year":"2015","journal-title":"Int. J. Adv. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/01431161.2015.1129563","article-title":"Sentinel-1 bathymetry for North Sea palaeolandscape analysis","volume":"37","author":"Stewart","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","unstructured":"Muzirafuti, A., Crupi, A., Lanza, S., Barreca, G., and Randazzo, G. (2019, January 3\u20135). Shallow water bathymetry by satellite image: A case study on the coast of San Vito Lo Capo Peninsula, Northwestern Sicily, Italy. Proceedings of the IMEKO TC-19 International Workshop on Metrology for the Sea, Genoa, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1109\/JSTARS.2022.3153681","article-title":"Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison between Sentinel-2, Landsat-8, and Testing Gaofen-2","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","first-page":"102993","article-title":"Satellite derived bathymetry based on ICESat-2 diffuse attenuation signal without prior information","volume":"113","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","first-page":"6185017","article-title":"Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery","volume":"59","author":"Bierwirth","year":"1992","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/JSTARS.2020.3040186","article-title":"Bathymetric Retrieval Selectively Using Multi-Angular High-Spatial-Resolution Satellite Imagery","volume":"14","author":"Cao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","unstructured":"Jupp, D. (1988, January 7\u20139). Background and extension to depth of penetration (DOP) mapping in shallow coastal waters. Proceedings of the Symposium on Remote Sensing of the Coastal Zone, Gold Coast, QLD, Australia."},{"key":"ref_19","unstructured":"Polcyn, F.C., and Lyzenga, D.R. (1973, January 5\u20139). Calculations of water depth from ERTS-MSS data. Proceedings of the Symposium on Significant Results Obtained from ERTS-1, New Carrollton, MD, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.joes.2021.02.006","article-title":"Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research","volume":"6","author":"Ashphaq","year":"2021","journal-title":"J. Ocean Eng. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2018.03.024","article-title":"An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data","volume":"210","author":"Kerr","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"102693","article-title":"Automated high-resolution satellite-derived coastal bathymetry mapping","volume":"107","author":"McCarthy","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","unstructured":"Spitzer, D., and Dirks, R.W.J. (1986, January 25\u201329). Classification of bottom composition and bathymetry of shallow waters by passive remote sensing. Proceedings of the Seventh International Symposium, Enschede, The Netherlands."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/LGRS.2015.2496401","article-title":"A Modified Lyzenga\u2019s Model for Multispectral Bathymetry Using Tikhonov Regularization","volume":"13","author":"Figueiredo","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","unstructured":"Tanis, F.J., and Byrnes, H.J. (1985, January 21\u201325). Optimization of multispectral sensors for bathymetry applications. Proceedings of the 19th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/01431168708948642","article-title":"Bottom influence on the reflectance of the sea","volume":"8","author":"Spitzer","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1080\/01431161.2012.734934","article-title":"Multispectral derivation of bathymetry in Singapore\u2019s shallow, turbid waters","volume":"34","author":"Bramante","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"444","DOI":"10.4319\/lo.2003.48.1_part_2.0444","article-title":"Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery","volume":"48","author":"Dierssen","year":"2003","journal-title":"Limnol. Oceanogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111302","DOI":"10.1016\/j.rse.2019.111302","article-title":"Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites","volume":"232","author":"Li","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"547","DOI":"10.4319\/lo.2003.48.1_part_2.0547","article-title":"Determination of Water Depth with High-Resolution Satellite Imagery over Variable Bottom Types","volume":"48","author":"Stumpf","year":"2003","journal-title":"Limnol. Oceanogr."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sagawa, T., Yamashita, Y., Okumura, T., and Yamanokuchi, T. (2019). Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images. Remote Sens., 11.","DOI":"10.3390\/rs11101155"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/JSTARS.2021.3134013","article-title":"An APMLP deep learning model for bathymetry retrieval using adjacent pixels","volume":"15","author":"Zhu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117","DOI":"10.14710\/geoplanning.3.2.117-126","article-title":"Satellite-derived bathymetry using random forest algorithm and worldview-2 Imagery","volume":"3","author":"Manessa","year":"2016","journal-title":"Geoplan. J. Geomat. Plan."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s12517-016-2803-1","article-title":"Assessment of proposed approaches for bathymetry calculations using multispectral satellite images in shallow coastal\/lake areas: A comparison of five models","volume":"10","author":"Mohamed","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"565","DOI":"10.5194\/isprs-annals-V-3-2020-565-2020","article-title":"A machine learning approach to multispectral satellite derived bathymetry","volume":"V-3-2020","author":"Tonion","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1109\/JSTARS.2016.2598152","article-title":"Nonparametric empirical depth regression for bathymetric mapping in coastal waters","volume":"9","author":"Kibele","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0034-4257(98)00043-1","article-title":"Coastal bathymetry from hyperspectral observations of water radiance","volume":"65","author":"Sandidge","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1175\/1520-0442(2004)017<0163:WAOTIV>2.0.CO;2","article-title":"Wavelet analysis of the interannual variability in southern Qu\u00e9bec streamflow","volume":"17","author":"Anctil","year":"2004","journal-title":"J. Clim."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Leng, Z., Zhang, J., Ma, Y., and Zhang, J. (2020). Underwater topography inversion in Liaodong Shoal based on GRU deep learning model. Remote Sens., 12.","DOI":"10.3390\/rs12244068"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Najar, M.A., Benshila, R., Bennioui, Y.E., Thoumyre, G., Almar, R., Bergsma, E.W.J., Delvit, J., and Wilson, D.G. (2022). Coastal bathymetry estimation from Sentinel-2 satellite imagery: Comparing deep learning and physics-based approaches. Remote Sens., 14.","DOI":"10.3390\/rs14051196"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s10994-021-05977-w","article-title":"Satellite derived bathymetry using deep learning","volume":"112","author":"Thoumyre","year":"2023","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Leng, Z., Zhang, J., Ma, Y., and Zhang, J. (2023). ICESat-2 bathymetric signal reconstruction method based on a deep learning model with active\u2013passive data fusion. Remote Sens., 15.","DOI":"10.3390\/rs15020460"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/01431168508948428","article-title":"Shallow-water bathymetry using combined lidar and passive multispectral scanner data","volume":"6","author":"Lyzenga","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2006.872909","article-title":"Multispectral bathymetry using a simple physically based algorithm","volume":"44","author":"Lyzenga","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1080\/01431160500034086","article-title":"Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos","volume":"26","author":"Hedley","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/JSTARS.2015.2398898","article-title":"An improved empirical model for retrieving bottom reflectance in optically shallow water","volume":"8","author":"Yang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhang, H., Li, X., Wang, J., and Fan, K. (2021). An exponential algorithm for bottom reflectance retrieval in clear optically shallow waters from multispectral imagery without ground data. Remote Sens., 13.","DOI":"10.3390\/rs13061169"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.1109\/TGRS.2017.2702061","article-title":"A novel adaptive fuzzy local information C -Means clustering algorithm for remotelysensed imagery classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","first-page":"1","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat."},{"key":"ref_50","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_51","unstructured":"Zhu, H., Ye, W., and Bei, G. (2009, January 26\u201329). A particle swarm optimization for integrated process planning and scheduling. Proceedings of the IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design, Wenzhou, China."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3570\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:13:01Z","timestamp":1760127181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,16]]},"references-count":51,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143570"],"URL":"https:\/\/doi.org\/10.3390\/rs15143570","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,16]]}}}