{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:48:49Z","timestamp":1774320529737,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T00:00:00Z","timestamp":1663286400000},"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":["42174022"],"award-info":[{"award-number":["42174022"]}],"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":["2020WLKXJ049"],"award-info":[{"award-number":["2020WLKXJ049"]}],"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":["KYCX20_2003"],"award-info":[{"award-number":["KYCX20_2003"]}],"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":["B20046"],"award-info":[{"award-number":["B20046"]}],"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":["202106420009"],"award-info":[{"award-number":["202106420009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Future Scientists Program of China University of Mining and Technology","award":["42174022"],"award-info":[{"award-number":["42174022"]}]},{"name":"Future Scientists Program of China University of Mining and Technology","award":["2020WLKXJ049"],"award-info":[{"award-number":["2020WLKXJ049"]}]},{"name":"Future Scientists Program of China University of Mining and Technology","award":["KYCX20_2003"],"award-info":[{"award-number":["KYCX20_2003"]}]},{"name":"Future Scientists Program of China University of Mining and Technology","award":["B20046"],"award-info":[{"award-number":["B20046"]}]},{"name":"Future Scientists Program of China University of Mining and Technology","award":["202106420009"],"award-info":[{"award-number":["202106420009"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["42174022"],"award-info":[{"award-number":["42174022"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["2020WLKXJ049"],"award-info":[{"award-number":["2020WLKXJ049"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX20_2003"],"award-info":[{"award-number":["KYCX20_2003"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["B20046"],"award-info":[{"award-number":["B20046"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["202106420009"],"award-info":[{"award-number":["202106420009"]}]},{"name":"Programme of Introducing Talents of Discipline to Universities","award":["42174022"],"award-info":[{"award-number":["42174022"]}]},{"name":"Programme of Introducing Talents of Discipline to Universities","award":["2020WLKXJ049"],"award-info":[{"award-number":["2020WLKXJ049"]}]},{"name":"Programme of Introducing Talents of Discipline to Universities","award":["KYCX20_2003"],"award-info":[{"award-number":["KYCX20_2003"]}]},{"name":"Programme of Introducing Talents of Discipline to Universities","award":["B20046"],"award-info":[{"award-number":["B20046"]}]},{"name":"Programme of Introducing Talents of Discipline to Universities","award":["202106420009"],"award-info":[{"award-number":["202106420009"]}]},{"name":"China Scholarship Council","award":["42174022"],"award-info":[{"award-number":["42174022"]}]},{"name":"China Scholarship Council","award":["2020WLKXJ049"],"award-info":[{"award-number":["2020WLKXJ049"]}]},{"name":"China Scholarship Council","award":["KYCX20_2003"],"award-info":[{"award-number":["KYCX20_2003"]}]},{"name":"China Scholarship Council","award":["B20046"],"award-info":[{"award-number":["B20046"]}]},{"name":"China Scholarship Council","award":["202106420009"],"award-info":[{"award-number":["202106420009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.<\/jats:p>","DOI":"10.3390\/rs14184634","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:49:22Z","timestamp":1663562962000},"page":"4634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9412-3121","authenticated-orcid":false,"given":"Jinwei","family":"Bu","sequence":"first","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Department of Physics, Universitat Polit\u00e8cnica de Catalunya, 08034 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7710-3073","authenticated-orcid":false,"given":"Kegen","family":"Yu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0031-0802","authenticated-orcid":false,"given":"Hyuk","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Physics, Universitat Polit\u00e8cnica de Catalunya, 08034 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9622-5041","authenticated-orcid":false,"given":"Weimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}]},{"given":"Shuai","family":"Han","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6693-957X","authenticated-orcid":false,"given":"Qingyun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8551-2720","authenticated-orcid":false,"given":"Nijia","family":"Qian","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Yiruo","family":"Lin","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s10236-016-0941-3","article-title":"Inversion and assessment of swell waveheights from HF radar spectra in the Iroise Sea","volume":"66","author":"Wang","year":"2016","journal-title":"Ocean Dyn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ocemod.2018.09.001","article-title":"Directional correction of modeled sea and swell wave heights using satellite altimeter data","volume":"131","author":"Albuquerque","year":"2018","journal-title":"Ocean Model."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/15210608409379502","article-title":"Swell in the Pacific Ocean observed by SEASAT radar altimeter","volume":"8","author":"Mognard","year":"1984","journal-title":"Mar. Geodesy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15210608409379501","article-title":"World Ocean mean monthly waves, swell, and surface winds for July through October 1978 from SEASAT radar altimeter data","volume":"8","author":"Mognard","year":"1984","journal-title":"Mar. Geodesy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5202","DOI":"10.1002\/2016GL068702","article-title":"A new insight from space into swell propagation and crossing in the global oceans","volume":"43","author":"Li","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2021GL096224","DOI":"10.1029\/2021GL096224","article-title":"SAR Altimetry Data as a New Source for Swell Monitoring","volume":"49","author":"Altiparmaki","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, H., Mouche, A., Husson, R., and Chapron, B. (2021). Indian Ocean Crossing Swells: New Insights from \u201cFireworks\u201d Perspective Using Envisat Advanced Synthetic Aperture Radar. Remote Sens., 13.","DOI":"10.3390\/rs13040670"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, H., Mouche, A., Husson, R., Grouazel, A., Chapron, B., and Yang, J. (2022). Assessment of Ocean Swell Height Observations from Sentinel-1A\/B Wave Mode against Buoy in Situ and Modeling Hindcasts. Remote Sens., 14.","DOI":"10.3390\/rs14040862"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, H., Mouche, A., Husson, R., and Chapron, B. (2018, January 22\u201327). Dynamic validation of ocean swell derived from Sentinel-1 wave mode against buoys. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517708"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1175\/2010JPO4324.1","article-title":"Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation","volume":"40","author":"Ardhuin","year":"2010","journal-title":"J. Phys. Oceanogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1029\/RS015i004p00843","article-title":"Methods for the extraction of long-period ocean wave parameters from narrow beam HF radar sea echo","volume":"15","author":"Lipa","year":"1980","journal-title":"Radio Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4089","DOI":"10.1029\/JC086iC05p04089","article-title":"HF radar measurements of long ocean waves","volume":"86","author":"Lipa","year":"1981","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/JOE.2006.886237","article-title":"A Method of Swell-Wave Parameter Extraction from HF Ocean Surface Radar Spectra","volume":"31","author":"Bathgate","year":"2006","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, C., Gill, E., and Huang, W. (May, January 29). Extraction of swell parameters from simulated noisy HF radar signals. Proceedings of the 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, Canada.","DOI":"10.1109\/RADAR.2013.6585983"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1175\/JTECH-D-18-0166.1","article-title":"Swell and Wind Wave Inversion Using a Single Very High Frequency (VHF) Radar","volume":"36","author":"Alattabi","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_16","first-page":"1747","article-title":"Evaluation and Validation of HF Radar Swell and Wind wave Inversion Method","volume":"38","author":"Voulgaris","year":"2021","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1109\/LGRS.2017.2733538","article-title":"Estimation of Significant Wave Height From X-Band Marine Radar Images Based on Ensemble Empirical Mode Decomposition","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1078053","DOI":"10.1155\/2016\/1078053","article-title":"Wave Height Estimation from Shipborne X-Band Nautical Radar Images","volume":"2016","author":"Liu","year":"2016","journal-title":"J. Sens."},{"key":"ref_19","first-page":"4202111","article-title":"Influences of Nononshore Winds on Significant Wave Height Estimations Using Coastal X-Band Radar Images","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, W., Liu, X., and Gill, E.W. (2017). Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review. Remote Sens., 9.","DOI":"10.3390\/rs9121261"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111744","DOI":"10.1016\/j.rse.2020.111744","article-title":"Temporal variability of GNSS-Reflectometry Ocean wind speed retrieval performance during the UK TechDemoSat-1 mission","volume":"242","author":"Hammond","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1175\/JTECH-D-16-0196.1","article-title":"Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations","volume":"34","author":"Clarizia","year":"2017","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1109\/TGRS.2017.2785343","article-title":"Revisiting the GNSS-R Waveform Statistics and Its Impact on Altimetric Retrievals","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1109\/JSTARS.2016.2582690","article-title":"Spaceborne GNSS-R Sea Ice Detection Using Delay-Doppler Maps: First Results from the U.K. TechDemoSat-1 Mission","volume":"9","author":"Yan","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TGRS.2015.2478776","article-title":"Weak Tsunami Detection Using GNSS-R-Based Sea Surface Height Measurement","volume":"54","author":"Yu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"12585","DOI":"10.1029\/2018GL079708","article-title":"Can GNSS Reflectometry Detect Precipitation Over Oceans?","volume":"45","author":"Asgarimehr","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_27","first-page":"5802015","article-title":"Sea Surface Rainfall Detection and Intensity Retrieval Based on GNSS-Reflectometry Data From the CYGNSS Mission","volume":"60","author":"Bu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"5803116","article-title":"Retrieval of Sea Surface Rainfall Intensity Using Spaceborne GNSS-R Data","volume":"60","author":"Bu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s10291-022-01320-5","article-title":"Machine learning-based methods for sea surface rainfall detection from CYGNSS delay-doppler maps","volume":"26","author":"Bu","year":"2022","journal-title":"GPS Solut."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1002\/2015GL064204","article-title":"Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission","volume":"42","author":"Foti","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1109\/TGRS.2016.2541343","article-title":"Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission","volume":"54","author":"Clarizia","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jing, C., Niu, X., Duan, C., Lu, F., Di, G., and Yang, X. (2019). Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results. Remote Sens., 11.","DOI":"10.3390\/rs11243013"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Munoz-Martin, J.F., Fernandez, L., Perez, A., Ruiz-De-Azua, J.A., Park, H., Camps, A., Dom\u00ednguez, B.C., and Pastena, M. (2021). In-Orbit Validation of the FMPL-2 Instrument\u2014The GNSS-R and L-Band Microwave Radiometer Payload of the FSSCat Mission. Remote Sens., 13.","DOI":"10.3390\/rs13010121"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, G., Bai, W., Wang, J., Hu, X., Zhang, P., Sun, Y., Xu, N., Zhai, X., Xiao, X., and Xia, J. (2022). FY3E GNOS II GNSS Reflectometry: Mission Review and First Results. Remote Sens., 14.","DOI":"10.3390\/rs14040988"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Peng, Q., and Jin, S. (2019). Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry. Remote Sens., 11.","DOI":"10.3390\/rs11050584"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, S., Jin, S., Jia, Y., and Ye, M. (2021). Significant Wave Height Estimation from Joint CYGNSS DDMA and LES Observations. Sensors, 21.","DOI":"10.3390\/s21186123"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1503705","DOI":"10.1109\/LGRS.2022.3155563","article-title":"Significant Wave Height Retrieval Method Based on Spaceborne GNSS Reflectometry","volume":"19","author":"Bu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1505605","DOI":"10.1109\/LGRS.2022.3198131","article-title":"A New Integrated Method of CYGNSS DDMA and LES Measurements for Significant Wave Height estimation","volume":"19","author":"Bu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, F., Yang, D., and Yang, L. (2022). Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14153666"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, K., Han, S., Bu, J., An, Y., Zhou, Z., Wang, C., Tabibi, S., and Cheong, J.W. (2022). Spaceborne GNSS Reflectometry. Remote Sens., 14.","DOI":"10.3390\/rs14071605"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1175\/JTECH-D-11-00075.1","article-title":"Wind Sea and Swell Separation of 1D Wave Spectrum by a Spectrum Integration Method","volume":"29","author":"Hwang","year":"2012","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_42","first-page":"4202414","article-title":"GNSS-R Wind Speed Retrieval of Sea Surface Based on Particle Swarm Optimization Algorithm","volume":"60","author":"Guo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/JSTARS.2018.2833075","article-title":"Development of the CYGNSS Geophysical Model Function for Wind Speed","volume":"12","author":"Ruf","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112801","DOI":"10.1016\/j.rse.2021.112801","article-title":"GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet","volume":"269","author":"Asgarimehr","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/36.841977","article-title":"Scattering of GPS signals from the ocean with wind remote sensing application","volume":"38","author":"Zavorotny","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6829","DOI":"10.1109\/TGRS.2014.2303831","article-title":"Spaceborne GNSS-R Minimum Variance Wind Speed Estimator","volume":"52","author":"Clarizia","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111202","DOI":"10.1016\/j.rse.2019.05.021","article-title":"An Arctic Sea ice multi-step classification based on GNSS-R data from the TDS-1 mission","volume":"230","author":"Holt","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4730","DOI":"10.1109\/JSTARS.2016.2588467","article-title":"Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation","volume":"9","author":"Camps","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bu, J., Yu, K., Zhu, Y., Qian, N., and Chang, J. (2020). Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms. Remote Sens., 12.","DOI":"10.3390\/rs12223760"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1175\/2010JCLI3718.1","article-title":"A Global View on the Wind Sea and Swell Climate and Variability from ERA-40","volume":"24","author":"Semedo","year":"2011","journal-title":"J. Clim."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Reinking, J., Roggenbuck, O., and Even-Tzur, G. (2019). Estimating Wave Direction Using Terrestrial GNSS Reflectometry. Remote Sens., 11.","DOI":"10.20944\/preprints201904.0081.v1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"113135","DOI":"10.1016\/j.rse.2022.113135","article-title":"Estimating Sea level, wind direction, significant wave height, and wave peak period using a geodetic GNSS receiver","volume":"279","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:33:00Z","timestamp":1760142780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":52,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184634"],"URL":"https:\/\/doi.org\/10.3390\/rs14184634","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,16]]}}}