{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:33:57Z","timestamp":1772260437112,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51839002"],"award-info":[{"award-number":["51839002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41906158"],"award-info":[{"award-number":["41906158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ts20190963"],"award-info":[{"award-number":["ts20190963"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41-Y30F07-9001-20\/22"],"award-info":[{"award-number":["41-Y30F07-9001-20\/22"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholar Project of Shandong Province","award":["51839002"],"award-info":[{"award-number":["51839002"]}]},{"name":"Taishan Scholar Project of Shandong Province","award":["41906158"],"award-info":[{"award-number":["41906158"]}]},{"name":"Taishan Scholar Project of Shandong Province","award":["ts20190963"],"award-info":[{"award-number":["ts20190963"]}]},{"name":"Taishan Scholar Project of Shandong Province","award":["41-Y30F07-9001-20\/22"],"award-info":[{"award-number":["41-Y30F07-9001-20\/22"]}]},{"name":"China High-Resolution Earth Observation System Program","award":["51839002"],"award-info":[{"award-number":["51839002"]}]},{"name":"China High-Resolution Earth Observation System Program","award":["41906158"],"award-info":[{"award-number":["41906158"]}]},{"name":"China High-Resolution Earth Observation System Program","award":["ts20190963"],"award-info":[{"award-number":["ts20190963"]}]},{"name":"China High-Resolution Earth Observation System Program","award":["41-Y30F07-9001-20\/22"],"award-info":[{"award-number":["41-Y30F07-9001-20\/22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When carrying out SDB (satellite-derived bathymetry) in island area based on ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) data, it is often found that the ICESat-2 bathymetric signals are partially missing due to the influence of thick aerosols such as clouds and fog. This not only hinders the accurate extraction of the along-track underwater topography, but also restricts the active\u2013passive fusion bathymetry based on ICESat-2 data and multi\/hyperspectral remote sensing images. In this paper, aiming at the partially missing ICESat-2 bathymetric signals, combined with passive optical remote sensing images, and based on an LSTM (long short-term memory) deep recurrent neural network model, an ICESat-2 bathymetric signal reconstruction method based on active\u2013passive data fusion is proposed. It is found that this method can effectively reconstruct the local missing bathymetric signals. When the reconstructed ICESat-2 bathymetric data are applied to carry out active\u2013passive fusion and bathymetric inversion, the accuracy indices are better than those of the inversion results of the data with partial missing signals, and the performance is comparable to that of the original data without missing data, which is of great value for the bathymetric application of ICESat-2 data in island and reef areas.<\/jats:p>","DOI":"10.3390\/rs15020460","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:29:57Z","timestamp":1673576997000},"page":"460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["ICESat-2 Bathymetric Signal Reconstruction Method Based on a Deep Learning Model with Active\u2013Passive Data Fusion"],"prefix":"10.3390","volume":"15","author":[{"given":"Zihao","family":"Leng","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, 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":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","first-page":"331","article-title":"Progress in shallow water depth mapping from optical remote sensing","volume":"36","author":"Ma","year":"2018","journal-title":"Adv. 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