{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:47:45Z","timestamp":1784821665512,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Science and Technology Development Funds","award":["21ZR1469700"],"award-info":[{"award-number":["21ZR1469700"]}]},{"name":"Shanghai Science and Technology Development Funds","award":["41776186"],"award-info":[{"award-number":["41776186"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21ZR1469700"],"award-info":[{"award-number":["21ZR1469700"]}],"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":["41776186"],"award-info":[{"award-number":["41776186"]}],"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>Bed topography and roughness play important roles in numerous ice-sheet analyses. Although the coverage of ice-penetrating radar measurements has vastly increased over recent decades, significant data gaps remain in certain areas of subglacial topography and need interpolation. However, the bed topography generated by interpolation such as kriging and mass conservation is generally smooth at small scales, lacking topographic features important for sub-kilometer roughness. DeepBedMap, a deep learning method combined with multiple surface observation inputs, can generate high-resolution (250 m) bed topography with realistic bed roughness but produces some unrealistic artifacts and higher bed elevation values in certain regions, which could bias ice-sheet models. To address these issues, we present MB_DeepBedMap, a multi-branch deep learning method to generate more realistic bed topography. The model improves upon DeepBedMap by separating inputs into two groups using a multi-branch network structure according to their characteristics, rather than fusing all inputs at an early stage, to reduce artifacts in the generated topography caused by earlier fusion of inputs. A direct upsampling branch preserves large-scale subglacial landforms while generating high-resolution bed topography. We use MB_DeepBedMap to generate a high-resolution (250 m) bed elevation grid product of Antarctica, MB_DeepBedMap_DEM, which can be used in high-resolution ice-sheet modeling studies. Moreover, we test the performance of MB_DeepBedMap model in Thwaites Glacier, Gamburtsev Subglacial Mountains, and several other regions, by comparing the qualitative topographic features and quantitative errors of MB_DeepBedMap, BEDMAP2, BedMachine Antarctica, and DeepBedMap. The results show that MB_DeepBedMap can provide more realistic small-scale topographic features and roughness compared to BEDMAP2, BedMachine Antarctica, and DeepBedMap.<\/jats:p>","DOI":"10.3390\/rs15051359","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T06:09:52Z","timestamp":1677564592000},"page":"1359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5859-5858","authenticated-orcid":false,"given":"Yiheng","family":"Cai","sequence":"first","affiliation":[{"name":"School of Information and Communications Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxing","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3909-7316","authenticated-orcid":false,"given":"Shinan","family":"Lang","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4269-8086","authenticated-orcid":false,"given":"Xiangbin","family":"Cui","sequence":"additional","affiliation":[{"name":"Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijun","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"375","DOI":"10.5194\/tc-7-375-2013","article-title":"Bedmap2: Improved Ice Bed, Surface and Thickness Datasets for Antarctica","volume":"7","author":"Fretwell","year":"2013","journal-title":"Cryosphere"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41561-019-0510-8","article-title":"Deep Glacial Troughs and Stabilizing Ridges Unveiled beneath the Margins of the Antarctic Ice Sheet","volume":"13","author":"Morlighem","year":"2019","journal-title":"Nat. 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