{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:32:28Z","timestamp":1777653148072,"version":"3.51.4"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Helmholtz Association's Initiative and Networking Fund"},{"DOI":"10.13039\/501100023371","name":"Helmholtz Artificial Intelligence Cooperation Unit","doi-asserted-by":"publisher","award":["ZT-I-PF-5-01"],"award-info":[{"award-number":["ZT-I-PF-5-01"]}],"id":[{"id":"10.13039\/501100023371","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Helmholtz AI project AI4GNSSR","award":["ZT-I-PF-5-091"],"award-info":[{"award-number":["ZT-I-PF-5-091"]}]},{"name":"Helmholtz Association's Initiative and Networking","award":["HAICORE@FZJ"],"award-info":[{"award-number":["HAICORE@FZJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/jstars.2024.3453999","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T17:27:49Z","timestamp":1725384469000},"page":"17860-17875","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning in Spaceborne GNSS Reflectometry: Correcting Precipitation Effects on Wind Speed Products"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-143X","authenticated-orcid":false,"given":"Tianqi","family":"Xiao","sequence":"first","affiliation":[{"name":"German Research Centre for Geosciences, Potsdam, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9458-1517","authenticated-orcid":false,"given":"Caroline","family":"Arnold","sequence":"additional","affiliation":[{"name":"German Climate Computing Center, Hamburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-1338","authenticated-orcid":false,"given":"Daixin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Data Science in Earth Observation, Technical University of Munich, Munich, Germany"}]},{"given":"Lichao","family":"Mou","sequence":"additional","affiliation":[{"name":"Data Science in Earth Observation, Technical University of Munich, Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7379-5276","authenticated-orcid":false,"given":"Jens","family":"Wickert","sequence":"additional","affiliation":[{"name":"German Research Centre for Geosciences, Potsdam, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9378-1886","authenticated-orcid":false,"given":"Milad","family":"Asgarimehr","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, Berlin, Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3389\/fmars.2019.00425"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2303831"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1023\/B:JOCE.0000009570.41358.c5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/36.992782"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.5589\/m02-035"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.814658"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10712-023-09771-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2014.2374220"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2018.2873241"},{"issue":"9","key":"ref10","article-title":"Evaluating impact of rain attenuation on space-borne GNSS reflectometry wind speeds","volume-title":"Remote Sens.","volume":"11","author":"Asgarimehr","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/2015gl064204"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-27127-4"},{"issue":"24","key":"ref13","article-title":"Sea surface wind speed retrieval from the first Chinese GNSS-R mission: Technique and preliminary results","volume-title":"Remote Sens.","volume":"11","author":"Jing","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/36.981349"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/36.841977"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SECON.1997.598694"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1029\/2008gl036292"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"ref19","first-page":"166","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume-title":"ISPRS J. Photogrammetry Remote Sens.","volume":"152","author":"Ma","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2948566"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3010879"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.2968156"},{"issue":"23","key":"ref23","article-title":"FA-RDN: A. hybrid neural network on GNSS-R sea surface wind speed retrieval","volume-title":"Remote Sens.","volume":"13","author":"Liu","year":"2021"},{"key":"ref24","article-title":"GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet","volume-title":"Remote Sens. Environ.","volume":"269","author":"Asgarimehr","year":"2022"},{"issue":"17","key":"ref25","article-title":"Hybrid CNN-LSTM deep learning for track-wise GNSS-R ocean wind speed retrieval","volume-title":"Remote Sens.","volume":"15","author":"Arabi","year":"2023"},{"key":"ref26","article-title":"DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation","volume-title":"Remote Sens. Environ.","volume":"294","author":"Zhao","year":"2023"},{"issue":"16","key":"ref27","article-title":"High wind speed inversion model of CYGNSS sea surface data based on machine learning","volume-title":"Remote Sens.","volume":"13","author":"Zhang","year":"2021"},{"issue":"14","key":"ref28","article-title":"Spaceborne GNSS-R wind speed retrieval using machine learning methods","volume-title":"Remote Sens.","volume":"14","author":"Wang","year":"2022"},{"issue":"3","key":"ref29","first-page":"356","article-title":"Challenges to satellite sensors of ocean winds: Addressing precipitation effects","volume-title":"J. Atmos. Ocean. Technol.","volume":"29","author":"Weissman","year":"2012"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2538181"},{"key":"ref31","article-title":"Characterization of rain impact on L-band GNSS-R ocean surface measurements","volume-title":"Remote Sens. Environ.","volume":"239","author":"Balasubramaniam","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1029\/2018gl079708"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2602703"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JOE.1985.1145133"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1029\/2020gl087287"},{"issue":"3","key":"ref36","article-title":"GloWS-Net: A. deep learning framework for retrieving global sea surface wind speed using spaceborne GNSS-R data","volume-title":"Remote Sens.","volume":"15","author":"Bu","year":"2023"},{"issue":"11","key":"ref37","article-title":"Joint retrieval of sea surface rainfall intensity, wind speed, and wave height based on spaceborne GNSS-R: A case study of the oceans near China","volume-title":"Remote Sens.","volume":"15","author":"Bu","year":"2023"},{"key":"ref38","article-title":"CYGNSS level 1 science data record version 3.0 netcdf data dictionary","year":"2021"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2017.2752219"},{"key":"ref40","article-title":"CYGNSS level 1 science data record version 3.0","year":"2021"},{"key":"ref41","volume-title":"CYGNSS Handbook","author":"Ruf","year":"2016"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2541343"},{"key":"ref43","article-title":"GPM IMERG final precipitation L3 half hourly 0.1 degree 0.1 degree V06 Greenbelt, MD, Goddard earth sciences data and information services center (GES DISC)","author":"Huffman","year":"2021"},{"key":"ref44","article-title":"ERA5 hourly data on single levels from 1940 to present","author":"Hersbach","year":"2023"},{"issue":"56","key":"ref45","first-page":"1929","article-title":"Dropout: A. simple way to prevent neural networks from overfitting","volume-title":"J. Mach. Learn. Res.","volume":"15","author":"Srivastava","year":"2014"},{"key":"ref46","first-page":"124","article-title":"Activation Functions: Comparison of trends in practice and research for deep learning","volume-title":"Proc. 2nd Int. Conf. Comput. Sci. Technol.","author":"Nwankpa","year":"2021"},{"key":"ref47","article-title":"PyTorch lightning","author":"Falcon","year":"2019"},{"key":"ref48","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref49","article-title":"CYGNSS level 2 science data record version 3.0","year":"2021"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2597438"},{"key":"ref51","article-title":"National hurricane center tropical cyclone report: Hurricane Laura (AL132020)","volume-title":"Nat. Ocean. Atmos. Admin.","author":"Pasch","year":"2021"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054102"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/4609443\/10330207\/10663853.pdf?arnumber=10663853","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T17:31:02Z","timestamp":1729272662000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10663853\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/jstars.2024.3453999","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}