{"id":"https://openalex.org/W4313291230","doi":"https://doi.org/10.1109/tgrs.2022.3233102","title":"Significant Wave Height Retrieval Based on Multivariable Regression Models Developed With CYGNSS Data","display_name":"Significant Wave Height Retrieval Based on Multivariable Regression Models Developed With CYGNSS Data","publication_year":2022,"publication_date":"2022-12-29","ids":{"openalex":"https://openalex.org/W4313291230","doi":"https://doi.org/10.1109/tgrs.2022.3233102"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3233102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3233102","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029034128","display_name":"Changyang Wang","orcid":"https://orcid.org/0000-0002-0757-5240"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changyang Wang","raw_affiliation_strings":["School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080131701","display_name":"Kegen Yu","orcid":"https://orcid.org/0000-0001-7710-3073"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kegen Yu","raw_affiliation_strings":["School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079094554","display_name":"Kefei Zhang","orcid":"https://orcid.org/0000-0001-9376-1148"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kefei Zhang","raw_affiliation_strings":["School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032913893","display_name":"Jinwei Bu","orcid":"https://orcid.org/0000-0001-9412-3121"},"institutions":[{"id":"https://openalex.org/I10660446","display_name":"Kunming University of Science and Technology","ror":"https://ror.org/00xyeez13","country_code":"CN","type":"education","lineage":["https://openalex.org/I10660446"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinwei Bu","raw_affiliation_strings":["Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, China","institution_ids":["https://openalex.org/I10660446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068874676","display_name":"Fangyu Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangyu Qu","raw_affiliation_strings":["College of Computer Science, Nankai University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5029034128"],"corresponding_institution_ids":["https://openalex.org/I25757504"],"apc_list":null,"apc_paid":null,"fwci":3.8945,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.93619339,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11061","display_name":"Ocean Waves and Remote Sensing","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11061","display_name":"Ocean Waves and Remote Sensing","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/significant-wave-height","display_name":"Significant wave height","score":0.7274903655052185},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6244809627532959},{"id":"https://openalex.org/keywords/buoy","display_name":"Buoy","score":0.572489857673645},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5251795053482056},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5202842354774475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5122348666191101},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.45529603958129883},{"id":"https://openalex.org/keywords/gnss-applications","display_name":"GNSS applications","score":0.42608392238616943},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41378942131996155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3030678629875183},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2939435839653015},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.2900758683681488},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24426984786987305},{"id":"https://openalex.org/keywords/wind-wave","display_name":"Wind wave","score":0.18712875247001648},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.10133358836174011},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09932196140289307},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09700512886047363},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08794507384300232}],"concepts":[{"id":"https://openalex.org/C85910571","wikidata":"https://www.wikidata.org/wiki/Q2674049","display_name":"Significant wave height","level":3,"score":0.7274903655052185},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6244809627532959},{"id":"https://openalex.org/C2779847632","wikidata":"https://www.wikidata.org/wiki/Q30026","display_name":"Buoy","level":2,"score":0.572489857673645},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5251795053482056},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5202842354774475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5122348666191101},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.45529603958129883},{"id":"https://openalex.org/C14279187","wikidata":"https://www.wikidata.org/wiki/Q5514012","display_name":"GNSS applications","level":3,"score":0.42608392238616943},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41378942131996155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3030678629875183},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2939435839653015},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.2900758683681488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24426984786987305},{"id":"https://openalex.org/C165082838","wikidata":"https://www.wikidata.org/wiki/Q165848","display_name":"Wind wave","level":2,"score":0.18712875247001648},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.10133358836174011},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09932196140289307},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09700512886047363},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08794507384300232},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C199104240","wikidata":"https://www.wikidata.org/wiki/Q118291","display_name":"Marine engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3233102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3233102","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[{"id":"https://openalex.org/G2183790458","display_name":null,"funder_award_id":"42174022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1975226595","https://openalex.org/W1978778560","https://openalex.org/W1995937139","https://openalex.org/W2028070629","https://openalex.org/W2029279877","https://openalex.org/W2037326543","https://openalex.org/W2055522016","https://openalex.org/W2069560926","https://openalex.org/W2097757623","https://openalex.org/W2104968029","https://openalex.org/W2105484143","https://openalex.org/W2112740000","https://openalex.org/W2139212933","https://openalex.org/W2140950054","https://openalex.org/W2152761983","https://openalex.org/W2161548576","https://openalex.org/W2329472099","https://openalex.org/W2528581612","https://openalex.org/W2555194062","https://openalex.org/W2625413745","https://openalex.org/W2903721734","https://openalex.org/W2921801758","https://openalex.org/W2995265070","https://openalex.org/W2998553334","https://openalex.org/W3009265480","https://openalex.org/W3023788354","https://openalex.org/W3037206505","https://openalex.org/W3083369567","https://openalex.org/W3083620541","https://openalex.org/W3101933275","https://openalex.org/W3134299082","https://openalex.org/W3141920046","https://openalex.org/W3156567400","https://openalex.org/W3157231682","https://openalex.org/W3189951784","https://openalex.org/W3200109667","https://openalex.org/W3215124533","https://openalex.org/W4211034383","https://openalex.org/W4212883601","https://openalex.org/W4214687379","https://openalex.org/W4286433387","https://openalex.org/W4290995043"],"related_works":["https://openalex.org/W1532301546","https://openalex.org/W4311758679","https://openalex.org/W2333273150","https://openalex.org/W2091909192","https://openalex.org/W4311747009","https://openalex.org/W2116304271","https://openalex.org/W1552632998","https://openalex.org/W4210465774","https://openalex.org/W2575795810","https://openalex.org/W4400591661"],"abstract_inverted_index":{"This":[0],"study":[1],"utilizes":[2],"L1B":[3],"level":[4],"data":[5,162],"from":[6,14],"reflected":[7],"global":[8],"navigation":[9],"satellite":[10],"system":[11],"(GNSS)":[12],"signals":[13],"the":[15,35,40,45,53,57,77,91,94,97,101,128,133,137,140,150,166,179,190,195,210,216,219,223],"Cyclone":[16],"GNSS":[17],"(CYGNSS)":[18],"mission":[19],"to":[20,75,89,235],"estimate":[21],"sea":[22,224],"surface":[23,225],"significant":[24],"wave":[25],"height":[26],"(SWH).":[27],"The":[28],"normalized":[29],"bistatic":[30],"radar":[31],"cross":[32],"Section":[33],"(NBRCS),":[34],"leading":[36],"edge":[37],"slope":[38],"(LES),":[39],"signal-to-noise":[41],"ratio":[42],"(SNR),":[43],"and":[44,66,96,120,149,168,176,222],"delay-Doppler":[46],"map":[47],"average":[48],"(DDMA)":[49],"are":[50,69,87,173,228,233,247],"used":[51],"as":[52,72,237],"key":[54],"variables":[55,74,98],"for":[56,240],"SWH":[58,78,95,243],"retrieval.":[59],"Eight":[60],"other":[61],"parameters,":[62],"including":[63],"instrument":[64],"gain":[65],"scatter":[67],"area,":[68],"also":[70,193,229],"utilized":[71],"auxiliary":[73],"enhance":[76],"retrieval":[79,244],"performance.":[80],"A":[81],"variety":[82],"of":[83,146,154,161,170,212,218],"multivariable":[84],"regression":[85,130],"models":[86,131],"investigated":[88],"clarify":[90],"relationship":[92],"between":[93],"by":[99],"using":[100],"following":[102],"five":[103,129],"methods:":[104],"stepwise":[105],"linear":[106],"regression,":[107],"Gaussian":[108],"support":[109],"vector":[110],"machine,":[111,119],"artificial":[112],"neural":[113],"network,":[114],"sparrow":[115],"search":[116],"algorithm\u2013extreme":[117],"learning":[118],"bagging":[121],"tree":[122],"(BT).":[123],"Results":[124],"show":[125],"that,":[126],"among":[127],"developed,":[132],"BT":[134,171,191],"model":[135,172,192],"performs":[136,203],"best":[138,196],"with":[139],"root":[141],"mean":[142],"square":[143],"error":[144],"(RMSE)":[145],"0.48":[147],"m":[148,175],"correlation":[151],"coefficient":[152],"(CC)":[153],"0.82":[155],"when":[156],"testing":[157,187],"one":[158],"million":[159],"sets":[160],"randomly":[163],"selected,":[164],"while":[165],"RMSE":[167],"CC":[169],"0.44":[174],"0.73":[177],"in":[178,205],"4500":[180],"National":[181],"Data":[182],"Buoy":[183],"Center":[184],"(NDBC)":[185],"buoy":[186],"dataset.":[188],"Meanwhile,":[189],"has":[194],"generalization":[197],"ability,":[198],"which":[199],"means":[200],"that":[201,246],"it":[202],"well":[204],"practical":[206],"applications.":[207],"In":[208],"addition,":[209],"impacts":[211],"different":[213],"input":[214],"variables,":[215],"size":[217],"training":[220],"dataset,":[221],"wind":[226],"speed":[227],"investigated.":[230],"These":[231],"findings":[232],"anticipated":[234],"serve":[236],"helpful":[238],"guides":[239],"creating":[241],"future":[242],"algorithms":[245],"more":[248],"advanced.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}