{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:46:06Z","timestamp":1775486766248,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["NSERC RGPIN-2017-04508 and RGPAS-2017-507962"],"award-info":[{"award-number":["NSERC RGPIN-2017-04508 and RGPAS-2017-507962"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canadian Space Agency CubeSat Grant","award":["17CCPNFL11"],"award-info":[{"award-number":["17CCPNFL11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.<\/jats:p>","DOI":"10.3390\/rs11212565","type":"journal-article","created":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T12:30:50Z","timestamp":1572611450000},"page":"2565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Sea Ice Remote Sensing Using GNSS-R: A Review"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6693-957X","authenticated-orcid":false,"given":"Qingyun","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1029\/1999GL010863","article-title":"Thinning of the Arctic sea-ice cover","volume":"26","author":"Rothrock","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"L01703","DOI":"10.1029\/2007GL031972","article-title":"Accelerated decline in the Arctic sea ice cover","volume":"35","author":"Comiso","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1002\/grl.50193","article-title":"CryoSat-2 estimates of Arctic sea ice thickness and volume","volume":"40","author":"Laxon","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","unstructured":"Hartman, D., Klein Tank, A., Rusicucci, M., Alexander, L., Broenniman, B., Charabi, Y., Dentener, F., Dlugokencky, E., Easterling, E., and Kaplan, A. (2013). Observations: Atmosphere and Surface, Cambridge University Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1029\/93RG01998","article-title":"The Arctic Sea Ice-Climate System: Observations and modeling","volume":"31","author":"Barry","year":"1993","journal-title":"Rev. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"L10602","DOI":"10.1029\/2009GL037525","article-title":"Rapid change in freshwater content of the Arctic Ocean","volume":"36","author":"McPhee","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105","DOI":"10.14430\/arctic4270","article-title":"Summer Sea Ice Concentration, Motion, and Thickness Near Areas of Proposed Offshore Oil and Gas Development in the Canadian Beaufort Sea\u20142009","volume":"66","author":"Galley","year":"2013","journal-title":"ARCTIC"},{"key":"ref_8","unstructured":"Sandven, S., Johannessen, O.M., and Kloster, K. (2006). Sea Ice Monitoring by Remote Sensing, John Wiley & Sons, Ltd."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6865","DOI":"10.1109\/TGRS.2019.2909057","article-title":"Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks","volume":"57","author":"Petrou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1175\/JTECH-D-18-0218.1","article-title":"Evaluation of AMSR-E Thin Ice Thickness Algorithm from a Mooring-Based Observation: How Can the Satellite Observe a Sea Ice Field with Nonuniform Thickness Distribution?","volume":"36","author":"Kashiwase","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5319","DOI":"10.1109\/TGRS.2019.2898872","article-title":"Arctic Sea Ice Classification Using Microwave Scatterometer and Radiometer Data During 2002\u20132017","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1109\/TGRS.2019.2892723","article-title":"Estimating Sea Ice Concentration From SAR: Training Convolutional Neural Networks with Passive Microwave Data","volume":"57","author":"Cooke","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"035005","DOI":"10.1088\/1748-9326\/aaf52c","article-title":"Assessing uncertainties in sea ice extent climate indicators","volume":"14","author":"Meier","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1109\/TGRS.2017.2777670","article-title":"Bayesian Sea Ice Detection With the ERS Scatterometer and Sea Ice Backscatter Model at C-Band","volume":"56","author":"Otosaka","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.5194\/tc-12-2941-2018","article-title":"A scatterometer record of sea ice extents and backscatter: 1992\u20132016","volume":"12","author":"Otosaka","year":"2018","journal-title":"Cryosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/nature02050","article-title":"High interannual variability of sea ice thickness in the Arctic region","volume":"425","author":"Laxon","year":"2003","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rose, S.K., Andersen, O.B., Passaro, M., Ludwigsen, C.A., and Schwatke, C. (2019). Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991\u20132018. Remote Sens., 11.","DOI":"10.3390\/rs11141672"},{"key":"ref_18","first-page":"8038","article-title":"Seasonal ice area and volume production of the Arctic Ocean: November 1996 through April 1997","volume":"107","author":"Kwok","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2016.2543660","article-title":"Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/LGRS.2019.2895656","article-title":"Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4050","DOI":"10.1109\/TGRS.2018.2889519","article-title":"Comparative Evaluation of Sea Ice Lead Detection Based on SAR Imagery and Altimeter Data","volume":"57","author":"Longepe","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hall, C., and Cordey, R. (1988, January 12\u201316). Multistatic Scatterometry. Proceedings of the International Geoscience and Remote Sensing Symposium, \u2018Remote Sensing: Moving Toward the 21st Century\u2019, Edinburgh, UK.","DOI":"10.1109\/IGARSS.1988.570200"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/36.981349","article-title":"Wind speed measurement using forward scattered GPS signals","volume":"40","author":"Garrison","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Katzberg, S.J., Torres, O., and Ganoe, G. (2006). Calibration of reflected GPS for tropical storm wind speed retrievals. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL026825"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1109\/TGRS.2008.922144","article-title":"Correction of the sea state impact in the L-Band brightness temperature by means of delay-Doppler maps of global navigation satellite signals reflected over the sea surface","volume":"46","author":"Camps","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TGRS.2012.2196437","article-title":"Airborne GNSS-R wind retrievals using delay\u2013Doppler maps","volume":"51","author":"Akos","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1109\/LGRS.2014.2320852","article-title":"An algorithm for sea-surface wind field retrieval from GNSS-R delay-doppler map","volume":"11","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/LGRS.2017.2782728","article-title":"Quantification of the relationship between sea surface roughness and the size of the glistening zone for GNSS-R","volume":"15","author":"Yan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"L17502","DOI":"10.1029\/2009GL039430","article-title":"Can we measure snow depth with GPS receivers?","volume":"36","author":"Larson","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.3390\/rs5084006","article-title":"Physical reflectivity and polarization characteristics for snow and ice-covered surfaces interacting with GPS signals","volume":"5","author":"Najibi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6892","DOI":"10.1002\/2014WR015561","article-title":"Snow depth, density, and SWE estimates derived from GPS reflection data: Validation in the western U. S","volume":"50","author":"McCreight","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1016\/j.asr.2014.03.005","article-title":"Sensing snow height and surface temperature variations in Greenland from GPS reflected signals","volume":"53","author":"Jin","year":"2014","journal-title":"Adv. Sp. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2646","DOI":"10.1109\/TAP.2015.2414950","article-title":"Validating the Variability of Snow Accumulation and Melting From GPS-Reflected Signals: Forward Modeling","volume":"63","author":"Najibi","year":"2015","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jin, S., Qian, X., and Kutoglu, H. (2016). Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data. Remote Sens., 8.","DOI":"10.3390\/rs8010063"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.rse.2005.09.015","article-title":"Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02","volume":"100","author":"Katzberg","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/JSTARS.2014.2320792","article-title":"Dual-Polarization GNSS-R Interference Pattern Technique for Soil Moisture Mapping","volume":"7","author":"Camps","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"4752","DOI":"10.1109\/JSTARS.2016.2584092","article-title":"Estimation of Surface Characteristics Using GNSS LH-Reflected Signals: Land Versus Water","volume":"9","author":"Jia","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.asr.2010.01.014","article-title":"GNSS reflectometry and remote sensing: New objectives and results","volume":"46","author":"Jin","year":"2010","journal-title":"Adv. Space Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.asr.2011.01.036","article-title":"Remote sensing using GNSS signals: Current status and future directions","volume":"47","author":"Jin","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2014.2374220","article-title":"Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity","volume":"2","author":"Zavorotny","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MGRS.2013.2260911","article-title":"CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites]","volume":"1","author":"Ruf","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_45","unstructured":"Ruf, C. (2019, October 31). Cyclone Global Navigation Satellite System (CYGNSS) and Soil Moisture Product Prospects; SMAP CalVal Work, Available online: https:\/\/smap.jpl.nasa.gov\/system\/internal_resources\/details\/original\/498_203_-_Ruf.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1109\/TGRS.2012.2230401","article-title":"Space-Based GNSS Scatterometry: Ocean Wind Sensing Using an Empirically Calibrated Model","volume":"51","author":"Gleason","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2750862","DOI":"10.1155\/2016\/2750862","article-title":"GNSS-R Delay-Doppler Map Simulation Based on the 2004 Sumatra-Andaman Tsunami Event","volume":"2016","author":"Yan","year":"2016","journal-title":"J. Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4650","DOI":"10.1109\/JSTARS.2016.2524990","article-title":"Tsunami Detection and Parameter Estimation from GNSS-R Delay-Doppler Map","volume":"9","author":"Yan","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4525","DOI":"10.1109\/JSTARS.2016.2603846","article-title":"Spaceborne GNSS-Reflectometry on TechDemoSat-1: Early Mission Operations and Exploitation","volume":"9","author":"Unwin","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pierdicca, N., Mollfulleda, A., Costantini, F., Guerriero, L., Dente, L., Paloscia, S., Santi, E., and Zribi, M. (2018, January 22\u201327). Spaceborne GNSS Reflectometry Data for Land Applications: An Analysis of Techdemosat Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517987"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cardellach, E., Fabra, F., Nogu\u00e9s-Correig, O., Oliveras, S., Rib\u00f3, S., and Rius, A. (2011). GNSS-R ground-based and airborne campaigns for ocean, land, ice, and snow techniques: Application to the GOLD-RTR data sets. Radio Sci., 46.","DOI":"10.1029\/2011RS004683"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1109\/TGRS.2013.2286257","article-title":"Consolidating the Precision of Interferometric GNSS-R Ocean Altimetry Using Airborne Experimental Data","volume":"52","author":"Cardellach","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/JSTARS.2014.2322854","article-title":"Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation","volume":"7","author":"Egido","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Fabra, F., Cardellach, E., Nogues-Correig, O., Oliveras, S., Ribo, S., Rius, A., Belmonte-Rivas, M., Semmling, M., Macelloni, G., and Pettinato, S. (2010, January 25\u201330). Monitoring sea-ice and dry snow with GNSS reflections. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5649635"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Semmling, A.M., Beyerle, G., Stosius, R., Dick, G., Wickert, J., Fabra, F., Cardellach, E., Rib\u00f3, S., Rius, A., and Helm, A. (2011). Detection of Arctic Ocean tides using interferometric GNSS-R signals. Geophys. Res. Lett., 38.","DOI":"10.1029\/2010GL046005"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2011.2172797","article-title":"Phase Altimetry with Dual Polarization GNSS-R Over Sea Ice","volume":"50","author":"Fabra","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/JSTARS.2014.2357894","article-title":"Detection of bohai bay sea ice using GPS-reflected signals","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","unstructured":"Komjathy, A., Maslanik, J., Zavorotny, V., Axelrad, P., and Katzberg, S. (2000, January 24\u201328). Sea ice remote sensing using surface reflected GPS signals. Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS 2000). Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment, Honolulu, HI, USA. Proceedings (Cat. No.00CH37120)."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1109\/TGRS.2009.2029342","article-title":"Bistatic Scattering of GPS Signals Off Arctic Sea Ice","volume":"48","author":"Rivas","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","first-page":"1096","article-title":"Remote Sensing of Sea Ice Thickness with GNSS Reflected Signal","volume":"39","author":"Gao","year":"2017","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/LGRS.2017.2722041","article-title":"Coastal Sea Ice Detection Using Ground-Based GNSS-R","volume":"14","author":"Strandberg","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/LGRS.2018.2852143","article-title":"Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks","volume":"15","author":"Yan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/JSTARS.2019.2907008","article-title":"Detecting Sea Ice From TechDemoSat-1 Data Using Support Vector Machines with Feature Selection","volume":"12","author":"Yan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4989","DOI":"10.1109\/TGRS.2017.2699122","article-title":"Sea Ice Detection Using U.K. TDS-1 GNSS-R Data","volume":"55","author":"Zavorotny","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/LGRS.2017.2676823","article-title":"Observing Sea\/Ice Transition Using Radar Images Generated from TechDemoSat-1 Delay Doppler Maps","volume":"14","author":"Schiavulli","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3782","DOI":"10.1109\/JSTARS.2017.2690917","article-title":"Single-Pass Sub-Meter Space-Based GNSS-R Ice Altimetry: Results From TDS-1","volume":"10","author":"Hu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"8369","DOI":"10.1002\/2017GL074513","article-title":"First spaceborne phase altimetry over sea ice using TechDemoSat-1 GNSS-R signals","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.1109\/JSTARS.2017.2689009","article-title":"Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps","volume":"10","author":"Yan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Yu, K., Zou, J., and Wickert, J. (2017). Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1. Sensors, 17.","DOI":"10.3390\/s17071614"},{"key":"ref_71","unstructured":"Gleason, S., Adjrad, M., and Unwin, M. (2005, January 13\u201316). Sensing Ocean, Ice and Land Reflected Signals from Space: Results from the UK-DMC GPS Reflectometry Experiment. Proceedings of the ION GNSS 18th International Technical Meeting of theSatellite Division, Long Beach, CA, USA."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gleason, S. (2006). Remote Sensing of Ocean, Ice and Land Surfaces Using Bistatically Scattered GNSS Signals from Low Earth Orbit. [Ph.D. Thesis, University of Surrey].","DOI":"10.1109\/IGARSS.2006.792"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.3390\/rs2082017","article-title":"Towards Sea Ice Remote Sensing with Space Detected GPS Signals: Demonstration of Technical Feasibility and Initial Consistency Check Using Low Resolution Sea Ice Information","volume":"2","author":"Gleason","year":"2010","journal-title":"Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yan, Q., and Huang, W. (2016, January 10\u201313). Sea ice detection from GNSS-R Delay-Doppler Map. Proceedings of the 2016 17th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), Montreal, QC, Canada.","DOI":"10.1109\/ANTEM.2016.7550123"},{"key":"ref_75","first-page":"668","article-title":"Sea Ice Edge Detection Using Spaceborne GNSS-R Signal","volume":"44","author":"Zhang","year":"2019","journal-title":"Geomatics Inf. Sci. Wuhan Univ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.1029\/2019JC015327","article-title":"Sea Ice Detection Using GNSS-R Data From TechDemoSat-1","volume":"124","author":"Cartwright","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yan, Q., and Huang, W. (2018, January 28\u201331). Sea Ice Detection Based on Unambiguous Retrieval of Scattering Coefficient from GNSS-R Delay-Doppler Maps. Proceedings of the 2018 OCEANS\u2014MTS\/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan.","DOI":"10.1109\/OCEANSKOBE.2018.8559148"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/LGRS.2011.2107500","article-title":"Ocean Surface\u2019s Scattering Coefficient Retrieval by Delay\u2013Doppler Map Inversion","volume":"8","author":"Valencia","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_79","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_80","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/TGRS.2009.2014465","article-title":"An Efficient Algorithm to the Simulation of Delay\u2013Doppler Maps of Reflected Global Navigation Satellite System Signals","volume":"47","author":"Camps","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1109\/JSTARS.2016.2543301","article-title":"Reconstruction of the Radar Image From Actual DDMs Collected by TechDemoSat-1 GNSS-R Mission","volume":"9","author":"Schiavulli","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2017.2743339","article-title":"Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier\u2013Feature Assembly","volume":"14","author":"Shen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Bobylev, L.P., Zabolotskikh, E.V., Mitnik, L.M., and Johannessenn, O.M. (2008, January 11\u201314). Neural-Network based algorithm for ice concentration retrievals from satellite passive microwave data. Proceedings of the 2008 Microwave Radiometry and Remote Sensing of the Environment, Firenze, Italy.","DOI":"10.1109\/MICRAD.2008.4579499"},{"key":"ref_84","unstructured":"Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. [Ph.D. Thesis, Harvard University]."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An Algorithm for Least-Squares Estimation of Nonlinear Parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"LeCun, Y.A., Bottou, L., Orr, G.B., and M\u00fcller, K.R. (2012). Efficient BackProp, Springer.","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Yan, Q., and Huang, W. (2019, January 2\u20137). Sea Ice Concentration Estimation From TechDemoSat-1 Data Using Support Vector Regression. Proceedings of the 2019 IEEE Radar Conference (RadarConf19), Boston, MA, USA.","DOI":"10.1109\/RADAR.2019.8835575"},{"key":"ref_90","unstructured":"Cavalieri, D.J., Parkinson, C.L., Gloerson, P., and Zwally, H.J. (1996). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM\/I-SSMIS Passive Microwave Data, NASA DAAC National Snow and Ice Data Center."},{"key":"ref_91","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_92","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TGRS.2015.2475317","article-title":"Generalized Linear Observables for Ocean Wind Retrieval From Calibrated GNSS-R Delay\u2013Doppler Maps","volume":"54","author":"Garrison","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_93","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification And Regression Trees, Wadsworth & Brooks."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Mayers, D., and Ruf, C. (2018, January 22\u201327). Measuring Ice Thickness with Cygnss Altimetry. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519310"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Yan, Q., and Huang, W. (2019, January 17\u201320). Sea Ice Thickness Estimation from TechDemoSat-1 Data. Proceedings of the Oceans 2019\u2014MTS\/IEEE Marseille, Marseille, France.","DOI":"10.1109\/OCEANSE.2019.8867332"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1063\/1.325018","article-title":"The complex-dielectric constant of sea ice at frequencies in the range 0.1\u201340 GHz","volume":"49","author":"Vant","year":"1978","journal-title":"J. Appl. Phys."},{"key":"ref_97","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1986). Microwave Remote Sensing: Active and Passive, Addison-Wesley."},{"key":"ref_98","unstructured":"Tian-Kunze, X., Kaleschke, L., and Maass, N. (2019, October 31). Available online: https:\/\/icdc.cen.uni-hamburg.de\/1\/daten\/cryosphere\/l3c-smos-sit.html."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/JSTARS.2013.2293371","article-title":"GNSS-Based Model-Free Sea Surface Height Estimation in Unknown Sea State Scenarios","volume":"7","author":"Yu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1002\/2015GL066624","article-title":"First spaceborne observation of sea surface height using GPS-Reflectometry","volume":"43","author":"Clarizia","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_101","unstructured":"Helm, A. (2008). Ground-Based GPS Altimetry with the L1 OpenGPS Receiver Using Carrier Phase-Delay Observations of Reflected GPS Signals. [Ph.D. Thesis, Postdam Deutsches GFZ]."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Hobiger, T., Strandberg, J., and Haas, R. (2017, January 23\u201328). Inverse modeling of ground-based GNSS-R\u2014Results and new possibilities. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127547"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:31:03Z","timestamp":1760189463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,1]]},"references-count":102,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212565"],"URL":"https:\/\/doi.org\/10.3390\/rs11212565","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,1]]}}}