{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:34:51Z","timestamp":1773930891435,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Science and Technology Innovation Action Planning","award":["20dz1203800"],"award-info":[{"award-number":["20dz1203800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow water bathymetry is of great significance in understanding, managing, and protecting coastal ecological environments. Many studies have shown that both empirical models and deep learning models can achieve promising results from satellite imagery bathymetry inversion. However, the spectral information available today in multispectral or\/and hyperspectral satellite images has not been explored thoroughly in many models. The Band-optimized Bidirectional Long Short-Term Memory (BoBiLSTM) model proposed in this paper feeds only the optimized bands and band ratios to the deep learning model, and a series of experiments were conducted in the shallow waters of Molokai Island, Hawaii, using hyperspectral satellite imagery (PRISMA) and multispectral satellite imagery (Sentinel-2) with ICESat-2 data and multibeam scan data as training data, respectively. The experimental results of the BoBiLSTM model demonstrate its robustness over other compared models. For example, using PRISMA data as the source image, the BoBiLSTM model achieves RMSE values of 0.82 m (using ICESat-2 as the training data) and 1.43 m (using multibeam as the training data), respectively, and because of using the bidirectional strategy, the inverted bathymetry reaches as far as a depth of 25 m. More importantly, the BoBiLSTM model does not overfit the data in general, which is one of its advantages over many other deep learning models. Unlike other deep learning models, which require a large amount of training data and all available bands as the inputs, the BoBiLSTM model can perform very well using equivalently less training data and a handful of bands and band ratios. With ICESat-2 data becoming commonly available and covering many shallow water regions around the world, the proposed BoBiLSTM model holds potential for bathymetry inversion for any region around the world where satellite images and ICESat-2 data are available.<\/jats:p>","DOI":"10.3390\/rs15143472","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:42:58Z","timestamp":1689039778000},"page":"3472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaotao","family":"Xi","sequence":"first","affiliation":[{"name":"College of Information, Shanghai Ocean University, No. 999 Hucheng Ring Road, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4393-6250","authenticated-orcid":false,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Ocean University, No. 999 Hucheng Ring Road, Shanghai 201306, China"}]},{"given":"Yingxi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Ocean University, No. 999 Hucheng Ring Road, Shanghai 201306, China"}]},{"given":"Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Ocean University, No. 999 Hucheng Ring Road, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3390\/rs3010042","article-title":"Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007","volume":"3","author":"Lyons","year":"2011","journal-title":"Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1130\/B25200.1","article-title":"Quantitative Morphology of A Fringing Reef Tract from High-resolution Laser Bathymetry Southern Molokai, Hawaii","volume":"115","author":"Storlazzi","year":"2003","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3389\/fmars.2019.00283","article-title":"Seafloor Mapping\u2014The Challenge of a Truly Global Ocean Bathymetry","volume":"6","author":"Snaith","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109046","DOI":"10.1016\/j.oceaneng.2021.109046","article-title":"Prediction and Reconstruction of Ocean Wave Heights Based on Bathymetric Data Using LSTM Neural Networks","volume":"232","author":"Berkenbrink","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shang, X., Zhao, J., and Zhang, H. (2019). Obtaining High-Resolution Seabed Topography and Surface Details by Co-Registration of Side-Scan Sonar and Multibeam Echo Sounder Images. Remote Sens., 11.","DOI":"10.3390\/rs11121496"},{"key":"ref_7","unstructured":"(2022, September 15). National Oceanic and Atmospheric Administration (NOAA): Field Procedures Manual, Available online: https:\/\/nauticalcharts.noaa.gov\/publications\/docs\/standards-and-requirements\/fpm\/2014-fpm-final.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.isprsjprs.2017.04.008","article-title":"Analysis and Correction of Ocean Wave Pattern Induced Systematic Coordinate Errors in Airborne LiDAR Bathymetry","volume":"128","author":"Westfeld","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5133","DOI":"10.3390\/rs70505133","article-title":"Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry","volume":"7","author":"Pan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, X., Zhang, H., and Zhou, F. (2017). Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems. Remote Sens., 9.","DOI":"10.3390\/rs9070710"},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","unstructured":"Almar, R., Bergsma, E.W.J., Thoumyre, G., Baba, M.W., Cesbron, G., Daly, C., Garlan, T., and Lifermann, A. (2021). Global Satellite-Based Coastal Bathymetry from Waves. Remote Sens., 13.","DOI":"10.3390\/rs13224628"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Danilo, C., and Melgani, F. (2019). High-Coverage Satellite-Based Coastal Bathymetrythrough a Fusion of Physical and Learning Methods. Remote Sens., 11.","DOI":"10.3390\/rs11040376"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/22797254.2019.1658542","article-title":"Shallow Water Bathymetry from WorldView-2 Stereo Imagery Using Two-media Photogrammetry","volume":"52","author":"Cao","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/LGRS.2013.2274475","article-title":"Estimation of Coastal Bathymetry Using RISAT-1 C-Band Microwave SAR Data","volume":"11","author":"Mishra","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Specht, M., Stateczny, A., Specht, C., Wid\u017agowski, S., Lewicka, O., and Wi\u015bniewska, M. (2021). Concept of an Innovative Autonomous Unmanned System for Bathymetric Monitoring of Shallow Waterbodies (INNOBAT System). Energies, 14.","DOI":"10.3390\/en14175370"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Specht, M., Wisniewska, M., Stateczny, A., Specht, C., Szostak, B., Lewicka, O., Stateczny, M., Widzgowski, S., and Halicki, A. (2022). Analysis of Methods for Determining Shallow Waterbody Depths Based on Images Taken by Unmanned Aerial Vehicles. Sensors, 22.","DOI":"10.3390\/s22051844"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lewicka, O., Specht, M., Stateczny, A., Specht, C., Dardanelli, G., Br\u010di\u0107, D., Szostak, B., Halicki, A., Stateczny, M., and Wid\u017agowski, S. (2022). Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms. Remote Sens., 14.","DOI":"10.3390\/rs14164075"},{"key":"ref_20","unstructured":"Najar, M.A., Bennioui, Y.E., Thoumyre, G., Almar, R., Bergsma, E.W.J., Benshila, R., Delvit, J.-M., and Wilson, D.G. (2022, January 6\u201311). A Combined Color and Wave-Based Approach to Satellite Derived Bathymetry Using Deep Learning. Proceedings of the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Nice, France."},{"key":"ref_21","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":"Adv. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431161.2018.1533660","article-title":"Assessment of Empirical Algorithms for Bathymetry Extraction Using Sentinel-2 Data","volume":"40","author":"Casal","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yunus, A.P., Dou, J., Song, X., and Avtar, R. (2019). Improved Bathymetric Mapping of Coastal and Lake Environments Using Sentinel-2 and Landsat-8 Images. Sensors, 19.","DOI":"10.3390\/s19122788"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Amrari, S., Bourassin, E., Andr\u00e9fou\u00ebt, S., Soulard, B., Lemonnier, H., and Le Gendre, R. (2021). Shallow Water Bathymetry Retrieval Using a Band-Optimization Iterative Approach: Application to New Caledonia Coral Reef Lagoons Using Sentinel-2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13204108"},{"key":"ref_25","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":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1068\/a301905","article-title":"Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis","volume":"30","author":"Fotheringham","year":"1998","journal-title":"Environ. Plan. A"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1080\/15481603.2019.1685198","article-title":"Understanding Satellite-derived Bathymetry Using Sentinel 2 Imagery and Spatial Prediction Models","volume":"57","author":"Casal","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ecss.2010.05.015","article-title":"Remote Sensing of Water Depths in Shallow Waters via Artificial Neural Networks","volume":"89","author":"Ceyhun","year":"2010","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s12524-011-0142-y","article-title":"Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters-Initial Model Results","volume":"40","author":"Nagamani","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/TGRS.2018.2814012","article-title":"Deriving Bathymetry from Optical Images With a Localized Neural Network Algorithm","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5250","DOI":"10.1109\/JSTARS.2022.3187179","article-title":"River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2","volume":"15","author":"Legleiter","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Najar, M.A., Benshila, R., Bennioui, Y.E., Thoumyre, G., Almar, R., Bergsma, E.W.J., Delvit, J.-M., 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_35","doi-asserted-by":"crossref","unstructured":"Alevizos, E., Nicodemou, V.C., Makris, A., Oikonomidis, I., Roussos, A., and Alexakis, D.D. (2022). Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach. Remote Sens., 14.","DOI":"10.3390\/rs14174160"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/JSTARS.2020.2993731","article-title":"Convolutional Neural Network to Retrieve Water Depth in Marine Shallow Water Area from Remote Sensing Images","volume":"13","author":"Ai","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wilson, B., Kurian, N.C., Singh, A., and Sethi, A. (October, January 26). Satellite-Derived Bathymetry Using Deep Convolutional Neural Network. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324053"},{"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","unstructured":"Malambo, L., and Popescu, S. (2020). PhotonLabeler: An Inter-Disciplinary Platform for Visual Interpretation and Labeling of ICESat-2 Geolocated Photon Data. Remote Sens., 12.","DOI":"10.20944\/preprints202008.0293.v1"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Zhong, J., Sun, J., Lai, Z., and Song, Y. (2022). Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Deep Learning Approach. Remote Sens., 14.","DOI":"10.3390\/rs14174229"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, M., Wu, G., Wang, J., Wang, Y., and Hong, Z. (2023). Double Deep Q-Network for Hyperspectral Image Band Selection in Land Cover Classification Applications. Remote Sens., 15.","DOI":"10.3390\/rs15030682"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, M., Zhu, W., Hu, L., and Wang, Y. (2022). A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14030760"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Alevizos, E., Oikonomou, D., Argyriou, A.V., and Alexakis, D.D. (2022). Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion. Remote Sens., 14.","DOI":"10.3390\/rs14051127"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rossi, L., Mammi, I., and Pelliccia, F. (2020). UAV-Derived Multispectral Bathymetry. Remote Sens., 12.","DOI":"10.3390\/rs12233897"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Evagorou, E., Argyriou, A., Papadopoulos, N., Mettas, C., Alexandrakis, G., and Hadjimitsis, D. (2022). Evaluation of Satellite-Derived Bathymetry from High and Medium-Resolution Sensors Using Empirical Methods. Remote Sens., 14.","DOI":"10.3390\/rs14030772"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6871","DOI":"10.1109\/JSTARS.2021.3093624","article-title":"A Multiband Model With Successive Projections Algorithm for Bathymetry Estimation Based on Remotely Sensed Hyperspectral Data in Qinghai Lake","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Alevizos, E. (2020). A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data. Remote Sens., 12.","DOI":"10.3390\/rs12213489"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112091","DOI":"10.1016\/j.rse.2020.112091","article-title":"SMART-SDB: Sample-specific Multiple Band Ratio Technique for Satellite-Derived Bathymetry","volume":"251","author":"Bovolo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TGRS.2014.2372787","article-title":"Integration of Hyperspectral Imagery and Sparse Sonar Data for Shallow Water Bathymetry Mapping","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cui, J., Kong, W., Zhang, X., Chen, D., and Zeng, Q. (2021). DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes. Entropy, 23.","DOI":"10.3390\/e23070863"},{"key":"ref_52","unstructured":"Liu, S., Zhang, C., and Ma, J. (2017). Neural Information Processing, Springer. Lecture Notes in Computer Science."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"106568","DOI":"10.1016\/j.compag.2021.106568","article-title":"Developing a Novel Framework for Forecasting Groundwater Level Fluctuations Using Bi-directional Long Short-Term Memory (BiLSTM) Deep Neural Network","volume":"191","author":"Ghasemlounia","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"17497","DOI":"10.1038\/s41598-021-96751-4","article-title":"Streamflow Prediction Using An Integrated Methodology Based on Convolutional Neural Network and Long Short-term Memory Networks","volume":"11","author":"Ghimire","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.imavis.2019.03.003","article-title":"Image Caption Model of Double LSTM with Scene Factors","volume":"86","author":"Peng","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"78452","DOI":"10.1109\/ACCESS.2021.3083794","article-title":"Reasoning for Local Graph Over Knowledge Graph With a Multi-Policy Agent","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_58","unstructured":"(2022, September 15). PRISMA Data. Available online: https:\/\/prisma.asi.it\/."},{"key":"ref_59","unstructured":"(2022, September 15). PRISMA User Manual. Available online: http:\/\/prisma.asi.it\/missionselect\/docs\/PRISMA%20User%20Manual_Is1_3.pdf."},{"key":"ref_60","unstructured":"(2022, September 15). ESA Copernicus Data Center. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_61","unstructured":"(2022, September 15). SNAP Download. Available online: http:\/\/step.esa.int\/main\/download\/snap-download\/."},{"key":"ref_62","unstructured":"(2022, September 15). Sentinel-2 User Handbook. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_63","unstructured":"Neumann, T., Brenner, A., Hancock, D., Robbins, J., Saba, J., and Harbeck, K. (2022, September 15). Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) Project: Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons (ATL03), Available online: https:\/\/icesat-2.gsfc.nasa.gov\/sites\/default\/files\/files\/ATL03_05June2018.pdf."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","unstructured":"Le, Y., Hu, M., Chen, Y., Yan, Q., Zhang, D., Li, S., Zhang, X., and Wang, L. (2022). Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea. Remote Sens., 14.","DOI":"10.3390\/rs14143406"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1364\/OE.409941","article-title":"Refraction Correction and Coordinate Displacement Compensation in Nearshore Bathymetry Using ICESat-2 Lidar Data and Remote-sensing Images","volume":"29","author":"Chen","year":"2021","journal-title":"Opt. Express"},{"key":"ref_67","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_68","unstructured":"(2022, September 15). NOAA Tides and Currents, Available online: https:\/\/tidesandcurrents.noaa.gov."},{"key":"ref_69","unstructured":"Richards, B.L., Smith, J.R., Smith, S.G., Ault, J.S., Kelley, C.D., and Moriwake, V.N. (2022, September 15). A Five-meter Resolution Multi-Beam Bathymetric and Backskatter Synthesis for the Main Hawaiian Islands. Available online: http:\/\/www.soest.hawaii.edu\/HMRG\/multibeam\/index.php."},{"key":"ref_70","unstructured":"Smith, J.R. (2016). Multibeam Backscatter and Bathymetry Synthesis for the Main Hawaiian Islands, Final Technical Report, NOAA."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","unstructured":"Asner, G.P., Vaughn, N.R., Balzotti, C., Brodrick, P.G., and Heckler, J. (2020). High-Resolution Reef Bathymetry and Coral Habitat Complexity from Airborne Imaging Spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12020310"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"631842","DOI":"10.3389\/fmars.2021.631842","article-title":"Abiotic and Human Drivers of Reef Habitat Complexity Throughout the Main Hawaiian Islands","volume":"8","author":"Asner","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional Recurrent Neural Networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Yuan, X., Wang, X., Han, J., Liu, J., Chen, H., Zhang, K., and Ye, Q. (2019, January 11\u201313). A High Accuracy Integrated Bagging-Fuzzy-GBDT Prediction Algorithm for Heart Disease Diagnosis. Proceedings of the 2019 IEEE\/CIC International Conference on Communications in China (ICCC), Changchun, China.","DOI":"10.1109\/ICCChina.2019.8855897"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"7466","DOI":"10.1109\/ACCESS.2018.2886549","article-title":"Research on Travel Time Prediction Model of Freeway Based on Gradient Boosting Decision Tree","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Song, Y., Niu, R., Xu, S., Ye, R., Peng, L., Guo, T., Li, S., and Chen, T. (2018). Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China). ISPRS Int. J. Geo. Inf., 8.","DOI":"10.3390\/ijgi8010004"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TNNLS.2020.3009776","article-title":"GBDT-MO Gradient-Boosted Decision Trees for Multiple Outputs","volume":"32","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_81","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_82","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the the 3rd International Conference for Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/JSTARS.2020.3034375","article-title":"An Adaptive Blended Algorithm Approach for Deriving Bathymetry from Multispectral Imagery","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"025012","DOI":"10.1117\/1.JRS.10.025012","article-title":"Airborne Mapping of Shallow Water Bathymetry in the Optically Complex Waters of the Baltic Sea","volume":"10","author":"Kutser","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Ch\u00e9nier, R., Faucher, M.-A., and Ahola, R. (2018). Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080306"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:09:49Z","timestamp":1760126989000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":85,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143472"],"URL":"https:\/\/doi.org\/10.3390\/rs15143472","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]}}}