{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:06:43Z","timestamp":1770041203689,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:00:00Z","timestamp":1728691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Geological Survey Project","award":["DD20191011"],"award-info":[{"award-number":["DD20191011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow water bathymetry is critical for environmental monitoring and maritime security. Current widely used statistical models based on passive optical satellite remote sensing often rely on prior bathymetric data, limiting their application to regions lacking such information. In contrast, the physics-based dual-band log-linear analytical model (P-DLA) can estimate shallow water bathymetry without in situ measurements, offering significant potential. However, the quasi-analytical algorithm (QAA) used in the P-DLA is sensitive to non-ideal pixels, resulting in unstable bathymetry estimation. To address this issue and evaluate the potential of SuperDove imagery for bathymetry estimation in regions without prior bathymetric data, this study proposes an improved physics-based dual-band model (IPDB). The IPDB replaces the QAA with a spectral optimization algorithm that integrates deep and shallow water sample pixels to estimate diffuse attenuation coefficients for the blue and green bands. This allows for more accurate estimation of shallow water bathymetry. The IPDB was tested on SuperDove images of Dongdao Island, Yongxing Island, and Yongle Atoll. The results showed that SuperDove images are capable of estimating shallow water bathymetry in regions without prior bathymetric data. The IPDB achieved Root Mean Square Error (RMSE) values below 1.7 m and R2 values above 0.89 in all three study areas, indicating strong performance in bathymetric estimation. Notably, the IPDB outperformed the standard P-DLA model in accuracy. Furthermore, this study outlines four sampling principles that, when followed, ensure that variations in the spatial distribution of sampling pixels do not significantly impact model performance. This study also showed that the blue\u2013green band combination is optimal for the analytical expression of the physics-based dual-band model.<\/jats:p>","DOI":"10.3390\/rs16203801","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"3801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Improved Physics-Based Dual-Band Model for Satellite-Derived Bathymetry Using SuperDove Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Chunlong","family":"He","sequence":"first","affiliation":[{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qigang","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,12]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.isprsjprs.2021.07.015","article-title":"A downscaled bathymetric mapping approach combining multitemporal Landsat-8 and high spatial resolution imagery: Demonstrations from clear to turbid waters","volume":"180","author":"Liu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","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_6","first-page":"1","article-title":"An Appraisal of Atmospheric Correction and Inversion Algorithms for Mapping High-Resolution Bathymetry over Coral Reef Waters","volume":"61","author":"Huang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","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_8","doi-asserted-by":"crossref","first-page":"111414","DOI":"10.1016\/j.rse.2019.111414","article-title":"A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry","volume":"233","author":"Cahalane","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1080\/01431161.2020.1809738","article-title":"Partition satellite derived bathymetry for coral reefs based on spatial residual information","volume":"42","author":"Chen","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"8550","DOI":"10.1109\/JSTARS.2023.3310166","article-title":"Shallow-Water Bathymetry Retrieval Based on an Improved Deep Learning Method Using GF-6 Multispectral Imagery in Nanshan Port Waters","volume":"16","author":"Shen","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s13131-022-2065-6","article-title":"Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery","volume":"42","author":"Chu","year":"2023","journal-title":"Acta Oceanol. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1364\/AO.480698","article-title":"Satellite-derived bathymetry integrating spatial and spectral information of multispectral images","volume":"62","author":"Li","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_15","first-page":"1","article-title":"Improving Satellite-Derived Bathymetry Estimation with a Joint Classification\u2013Regression Model","volume":"21","author":"Gupta","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.isprsjprs.2023.07.028","article-title":"Cost-efficient bathymetric mapping method based on massive active\u2013passive remote sensing data","volume":"203","author":"Han","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1109\/JSTARS.2024.3396374","article-title":"ICESat-2 and Multispectral Images Based Coral Reefs Geomorphic Zone Mapping Using a Deep Learning Approach","volume":"17","author":"Zhong","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","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_20","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_21","first-page":"103700","article-title":"Bathymetry derivation and slope-assisted benthic mapping using optical satellite imagery in combination with ICESat-2","volume":"127","author":"Liu","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","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_23","first-page":"1","article-title":"Bathymetry Retrieval Algorithm Based on Hyperspectral Features of Pure Water Absorption From 570 to 600 nm","volume":"61","author":"Wu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Bathymetry and Benthic Habitat Mapping in Shallow Waters from Sentinel-2A Imagery: A Case Study in Xisha Islands, China","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/JSTARS.2018.2874684","article-title":"Multispectral Bathymetry via Linear Unmixing of the Benthic Reflectance","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7538","DOI":"10.1364\/AO.58.007538","article-title":"Rapid estimation of bathymetry from multispectral imagery without in situ bathymetry data","volume":"58","author":"Liu","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TGRS.2019.2953381","article-title":"A Bathymetry Mapping Approach Combining Log-Ratio and Semianalytical Models Using Four-Band Multispectral Imagery without Ground Data","volume":"58","author":"Xia","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1007\/s00343-013-2287-8","article-title":"Bathymetry and bottom albedo retrieval using Hyperion: A case study of Thitu Island and reef","volume":"31","author":"Liu","year":"2013","journal-title":"Chin. J. Oceanol. Limnol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8745","DOI":"10.1109\/TGRS.2019.2922724","article-title":"Technical Framework for Shallow-Water Bathymetry with High Reliability and No Missing Data Based on Time-Series Sentinel-2 Images","volume":"57","author":"Chu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","first-page":"103308","article-title":"Bathymetry over broad geographic areas using optical high-spatial-resolution satellite remote sensing without in-situ data","volume":"119","author":"Xu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, R., Yu, K., Wang, Y., Wang, J., Mu, L., and Wang, W. (2017). Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images. Remote Sens., 9.","DOI":"10.3390\/rs9070750"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.isprsjprs.2024.04.014","article-title":"Making satellite-derived empirical bathymetry independent of high-quality in-situ depth data: An assessment of four possible model calibration data","volume":"211","author":"Cao","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.02.012","article-title":"A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth","volume":"151","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, W., Ye, L., Qiu, Z., Luan, K., He, N., Wei, Z., Yang, F., Yue, Z., Zhao, S., and Yang, F. (2021). Research of the Dual-Band Log-Linear Analysis Model Based on Physics for Bathymetry without In-Situ Depth Data in the South China Sea. Remote Sens., 13.","DOI":"10.3390\/rs13214331"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, Q., Chen, J., Chen, B., and Tao, B. (2022). Evaluation and Improvement of No-Ground-Truth Dual Band Algorithm for Shallow Water Depth Retrieval: A Case Study of a Coastal Island. Remote Sens., 14.","DOI":"10.3390\/rs14246231"},{"key":"ref_38","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":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1364\/AO.28.001569","article-title":"Bathymetric mapping with passive multispectral imagery","volume":"28","author":"Philpot","year":"1988","journal-title":"Appl. Opt."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112035","DOI":"10.1016\/j.rse.2020.112035","article-title":"Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data","volume":"250","author":"Wei","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1364\/AO.37.006329","article-title":"Hyperspectral remote sensing for shallow waters. I. A semianalytical model","volume":"37","author":"Lee","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ma, Y., and Zhang, J. (2020). Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12183027"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4693","DOI":"10.1007\/s11042-021-10748-9","article-title":"Performance analysis of inverting optical properties based on quasi-analytical algorithms","volume":"81","author":"Zhan","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box\u2014A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:12:23Z","timestamp":1760112743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,12]]},"references-count":44,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203801"],"URL":"https:\/\/doi.org\/10.3390\/rs16203801","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,12]]}}}