{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:33:58Z","timestamp":1772260438774,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130805"],"award-info":[{"award-number":["42130805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074154"],"award-info":[{"award-number":["42074154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from \u221210 m to 0 m and the RF-CID model excelling in the range from \u221215 m to \u221210 m.<\/jats:p>","DOI":"10.3390\/rs16234603","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T10:11:47Z","timestamp":1733739107000},"page":"4603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Mengying","family":"Ye","sequence":"first","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Changbao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Xuqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Sixu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130021, China"}]},{"given":"Xiaoran","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Yuyang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5813-6417","authenticated-orcid":false,"given":"Tianyi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112955","DOI":"10.1016\/j.rse.2022.112955","article-title":"Satellite retrieval of benthic reflectance by combining lidar and passive high-resolution imagery: Case-I water","volume":"272","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"1247","article-title":"Multiresolution Satellite-Derived Bathymetry in Shallow Coral Reefs: Improving Linear Algorithms with Geographical Analysis","volume":"36","author":"Strenzel","year":"2020","journal-title":"J. Coast. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111619","DOI":"10.1016\/j.rse.2019.111619","article-title":"Remote sensing of shallow waters\u2014A 50 year retrospective and future directions","volume":"240","author":"Kutser","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2013.2248372","article-title":"Bathymetry Retrieval from Hyperspectral Remote Sensing Data in Optical-Shallow Water","volume":"52","author":"Ma","year":"2014","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/LGRS.2019.2915122","article-title":"Bathymetry Model Based on Spectral and Spatial Multifeatures of Remote Sensing Image","volume":"17","author":"Wang","year":"2020","journal-title":"Ieee Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kulbacki, A., Lubczonek, J., and Zaniewicz, G. (2024). Acquisition of Bathymetry for Inland Shallow and Ultra-Shallow Water Bodies Using PlanetScope Satellite Imagery. Remote Sens., 16.","DOI":"10.3390\/rs16173165"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bannari, A., and Kadhem, G. (2017). MBES-CARIS Data Validation for Bathymetric Mapping of ShallowWater in the Kingdom of Bahrain on the Arabian Gulf. Remote Sens., 9.","DOI":"10.3390\/rs9040385"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1016\/j.rse.2009.01.015","article-title":"Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems","volume":"113","author":"Costa","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ji, X., Ma, Y., Zhang, J., Xu, W., and Wang, Y. (2023). A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15143570"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Xie, C., Chen, P., Zhang, S., and Huang, H. (2024). Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN. Remote Sens., 16.","DOI":"10.3390\/rs16030511"},{"key":"ref_12","first-page":"101033","article-title":"Incorporation of neighborhood information improves performance of SDB models","volume":"32","author":"Knudby","year":"2023","journal-title":"Remote Sens. Appl. -Soc. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, C.L., Jiang, Q.G., and Wang, P. (2024). An Improved Physics-Based Dual-Band Model for Satellite-Derived Bathymetry Using SuperDove Imagery. Remote Sens., 16.","DOI":"10.3390\/rs16203801"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114411","DOI":"10.1016\/j.rse.2024.114411","article-title":"Nearshore satellite-derived bathymetry from a single-pass satellite video: Improvements from adaptive correlation window size and modulation transfer function","volume":"315","author":"Klotz","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114302","DOI":"10.1016\/j.rse.2024.114302","article-title":"Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery","volume":"311","author":"Richardson","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, Z.Q., Zhao, Y.C., Wu, S.L., Chen, H.D., Song, C.H., Mao, Z.H., and Shen, W. (2024). Satellite-Derived Bathymetry Using a Fast Feature Cascade Learning Model in Turbid Coastal Waters. J. Remote Sens., 4.","DOI":"10.34133\/remotesensing.0272"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1002\/esp.4060","article-title":"Bathymetric Structure-from-Motion: Extracting shallow stream bathymetry from multi-view stereo photogrammetry","volume":"42","author":"Dietrich","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lubczonek, J., Kazimierski, W., Zaniewicz, G., and Lacka, M. (2022). Methodology for Combining Data Acquired by Unmanned Surface and Aerial Vehicles to Create Digital Bathymetric Models in Shallow and Ultra-Shallow Waters. Remote Sens., 14.","DOI":"10.3390\/rs14010105"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/01490419.2019.1685030","article-title":"Photogrammetric Bathymetry for the Canadian Arctic","volume":"43","author":"Faucher","year":"2020","journal-title":"Mar. Geod."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Del Savio, A.A., Torres, A.L., Olivera, M.A.V., Rojas, S.R.L., Ibarra, G.T.U., and Neckel, A. (2023). Using UAVs and Photogrammetry in Bathymetric Surveys in Shallow Waters. Appl. Sci., 13.","DOI":"10.3390\/app13063420"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111487","DOI":"10.1016\/j.rse.2019.111487","article-title":"Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: Comparison of radar altimetry, LIDAR and photogrammetry techniques","volume":"237","author":"Bandini","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112047","DOI":"10.1016\/j.rse.2020.112047","article-title":"Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets","volume":"250","author":"Ma","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Polcyn, F.C., and Rollin, R.A. (2024, June 06). Remote sensing techniques for the location and measurement of shallow-water features. Available online: https:\/\/deepblue.lib.umich.edu\/handle\/2027.42\/7114."},{"key":"ref_24","unstructured":"Polcyn, F.C., Brown, W.L., and Sattinger, I.J. (2024, June 06). The Measurement of Water Depth by Remote Sensing Techniques. Available online: https:\/\/agris.fao.org\/search\/en\/providers\/122415\/records\/647368ca53aa8c89630d65ca."},{"key":"ref_25","unstructured":"Polcyn, F.C. (2024, June 06). Calculations of water depth from ERTS-MSS data, Available online: https:\/\/ntrs.nasa.gov\/citations\/19730019626."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/01431168108948342","article-title":"Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data","volume":"2","author":"Lyzenga","year":"1981","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","unstructured":"Stumpf, R.P., Holderied, K., and Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr., 48.","DOI":"10.4319\/lo.2003.48.1_part_2.0547"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.12.004","article-title":"Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters","volume":"159","author":"Pacheco","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.rse.2018.07.014","article-title":"Coral reef applications of Sentinel-2: Coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8","volume":"216","author":"Hedley","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","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":"Geoplanning J. Geomat. Plan."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/15481603.2018.1538620","article-title":"Bathymetry retrieval from optical images with spatially distributed support vector machines","volume":"56","author":"Wang","year":"2019","journal-title":"Giscience Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"111325","DOI":"10.1016\/j.rse.2019.111325","article-title":"The Ice, Cloud, and Land Elevation Satellite-2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System","volume":"233","author":"Neumann","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2016.12.029","article-title":"The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation","volume":"190","author":"Markus","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/JPROC.2009.2034765","article-title":"The ICESat-2 Laser Altimetry Mission","volume":"98","author":"Abdalati","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111352","DOI":"10.1016\/j.rse.2019.111352","article-title":"Land ice height-retrieval algorithm for NASA\u2019s ICESat-2 photon-counting laser altimeter","volume":"233","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"38168","DOI":"10.1364\/OE.27.038168","article-title":"Ground elevation accuracy verification of ICESat-2 data: A case study in Alaska, USA","volume":"27","author":"Wang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Parrish, C.E., Magruder, L.A., Neuenschwander, A.L., Forfinski-Sarkozi, N., Alonzo, M., and Jasinski, M. (2019). Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS\u2019s Bathymetric Mapping Performance. Remote Sens., 11.","DOI":"10.3390\/rs11141634"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2021.05.012","article-title":"A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: A case study in the South China Sea","volume":"178","author":"Hsu","year":"2021","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112326","DOI":"10.1016\/j.rse.2021.112326","article-title":"A photon-counting LiDAR bathymetric method based on adaptive variable ellipse filtering","volume":"256","author":"Chen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xie, C., Chen, P., Pan, D., Zhong, C., and Zhang, Z. (2021). Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13214303"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3213248","DOI":"10.1109\/TGRS.2022.3213248","article-title":"A Physics-Assisted Convolutional Neural Network for Bathymetric Mapping Using ICESat-2 and Sentinel-2 Data","volume":"60","author":"Peng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Guo, X., Jin, X., and Jin, S. (2022). Shallow Water Bathymetry Mapping from ICESat-2 and Sentinel-2 Based on BP Neural Network Model. Water, 14.","DOI":"10.3390\/w14233862"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"van der Woerd, H.J., and Wernand, M.R. (2018). Hue-Angle Product for Low to Medium Spatial Resolution Optical Satellite Sensors. Remote Sens., 10.","DOI":"10.3390\/rs10020180"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fronkova, L., Greenwood, N., Martinez, R., Graham, J.A., Harrod, R., Graves, C.A., Devlin, M.J., and Petus, C. (2022). Can Forel-Ule Index Act as a Proxy of Water Quality in Temperate Waters? Application of Plume Mapping in Liverpool Bay, UK. Remote Sens., 14.","DOI":"10.3390\/rs14102375"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"107032","DOI":"10.1016\/j.ecss.2020.107032","article-title":"An evaluation of apparent color of seawater based on the in-situ and satellite-derived Forel-Ule color scale","volume":"246","author":"Nie","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_51","first-page":"102488","article-title":"Leaf area index retrieval with ICESat-2 photon counting LiDAR","volume":"103","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xing, Y., Huang, J., Gruen, A., and Qin, L. (2020). Assessing the Performance of ICESat-2\/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain. Remote Sens., 12.","DOI":"10.3390\/rs12132084"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"126312","DOI":"10.1016\/j.jhydrol.2021.126312","article-title":"Inland water level measurement from spaceborne laser altimetry: Validation and comparison of three missions over the Great Lakes and lower Mississippi River","volume":"597","author":"Xiang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e2020EA001555","DOI":"10.1029\/2020EA001555","article-title":"ICESat-2 Early Mission Synopsis and Observatory Performance","volume":"8","author":"Magruder","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112844","DOI":"10.1016\/j.rse.2021.112844","article-title":"Neural network guided interpolation for mapping canopy height of China\u2019s forests by integrating GEDI and ICESat-2 data","volume":"269","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111831","DOI":"10.1016\/j.rse.2020.111831","article-title":"A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry","volume":"244","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7883","DOI":"10.1109\/TGRS.2019.2917012","article-title":"Deriving High-Resolution Reservoir Bathymetry From ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery","volume":"57","author":"Li","year":"2019","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2016.04.009","article-title":"Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data","volume":"118","author":"Gwenzi","year":"2016","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_60","unstructured":"Markel, J. (2024, February 24). Shallow Water Bathymetry with ICESat-2 (Tutorial Led by Jonathan Markel at the 2023 ICESat-2 Hackweek). Available online: https:\/\/icesat-2-2023.hackweek.io\/tutorials\/bathymetry\/bathymetry_tutorial.html."},{"key":"ref_61","unstructured":"Sutterley, T. (2024, February 24). Python Interpretation of the NASA Goddard Space Flight Center YAPC (\u201cYet Another Photon Classifier\u201d) Algorithm. Available online: https:\/\/yapc.readthedocs.io\/en\/latest\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.patrec.2011.01.021","article-title":"Characteristic analysis of Otsu threshold and its applications","volume":"32","author":"Xu","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bernardis, M., Nardini, R., Apicella, L., Demarte, M., Guideri, M., Federici, B., Quarati, A., and De Martino, M. (2023). Use of ICEsat-2 and Sentinel-2 Open Data for the Derivation of Bathymetry in Shallow Waters: Case Studies in Sardinia and in the Venice Lagoon. Remote Sens., 15.","DOI":"10.3390\/rs15112944"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.rse.2017.10.022","article-title":"Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands","volume":"204","author":"Harmel","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"He, C.L., Jiang, Q.G., Tao, G.F., and Zhang, Z.C. (2023). A Convolutional Neural Network with Spatial Location Integration for Nearshore Water Depth Inversion. Sensors, 23.","DOI":"10.3390\/s23208493"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chen, T.Q., Guestrin, C., and Assoc Comp, M. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/JSTARS.2023.3326238","article-title":"Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data","volume":"17","author":"Xu","year":"2024","journal-title":"Ieee J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4603\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:49:37Z","timestamp":1760114977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":69,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234603"],"URL":"https:\/\/doi.org\/10.3390\/rs16234603","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,7]]}}}