{"id":"https://openalex.org/W3217139176","doi":"https://doi.org/10.1109/lgrs.2021.3129998","title":"Snow Depth Estimation Based on Parameter Combinations Selection and Machine Learning Algorithm Using C-Band SAR Data in Northeast China","display_name":"Snow Depth Estimation Based on Parameter Combinations Selection and Machine Learning Algorithm Using C-Band SAR Data in Northeast China","publication_year":2021,"publication_date":"2021-11-23","ids":{"openalex":"https://openalex.org/W3217139176","doi":"https://doi.org/10.1109/lgrs.2021.3129998","mag":"3217139176"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3129998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3129998","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5101485782","display_name":"Xiaoxin Zhu","orcid":"https://orcid.org/0000-0003-1697-9340"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxin Zhu","raw_affiliation_strings":["College of Electronic Science and Engineering, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0003-1697-9340","affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036910081","display_name":"Lingjia Gu","orcid":"https://orcid.org/0000-0002-4909-2263"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingjia Gu","raw_affiliation_strings":["College of Electronic Science and Engineering, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0002-4909-2263","affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725268","display_name":"Xiaofeng Li","orcid":"https://orcid.org/0000-0002-7302-8042"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210101301","display_name":"Northeast Institute of Geography and Agroecology","ror":"https://ror.org/01a9z1q73","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210101301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofeng Li","raw_affiliation_strings":["Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210101301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101911266","display_name":"Tao Jiang","orcid":"https://orcid.org/0000-0003-3833-4498"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210101301","display_name":"Northeast Institute of Geography and Agroecology","ror":"https://ror.org/01a9z1q73","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210101301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Jiang","raw_affiliation_strings":["Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210101301"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3692,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.53669674,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10644","display_name":"Cryospheric studies and observations","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"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/T10644","display_name":"Cryospheric studies and observations","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"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/T11333","display_name":"Climate change and permafrost","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"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/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7110164165496826},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6501673460006714},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6301354169845581},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6070008873939514},{"id":"https://openalex.org/keywords/snow","display_name":"Snow","score":0.5600343346595764},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5233713388442993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49972033500671387},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4944772720336914},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4660815894603729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45241355895996094},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4500220715999603},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42372044920921326},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4169304370880127},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.354669451713562},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3008972406387329},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.16513875126838684},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12276437878608704}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7110164165496826},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6501673460006714},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6301354169845581},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6070008873939514},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.5600343346595764},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5233713388442993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49972033500671387},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4944772720336914},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4660815894603729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45241355895996094},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4500220715999603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42372044920921326},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4169304370880127},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.354669451713562},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3008972406387329},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.16513875126838684},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12276437878608704},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3129998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3129998","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4530931002","display_name":null,"funder_award_id":"2021C044-7","funder_id":"https://openalex.org/F4320316081","funder_display_name":"Jilin Province Development and Reform Commission"},{"id":"https://openalex.org/G623612507","display_name":"\u4e1c\u5317\u519c\u7530\u533a\u79ef\u96ea\u6f14\u5316\u8fc7\u7a0b\u53ca\u5176\u5fae\u6ce2\u8f90\u5c04\u7279\u6027\u7814\u7a76","funder_award_id":"41871248","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6785792501","display_name":"\u57fa\u4e8e\u4e09\u7ef4\u7a7a\u95f4\u6df7\u5408\u50cf\u5143\u5206\u89e3\u6a21\u578b\u7684\u4e1c\u5317\u68ee\u6797\u533a\u79ef\u96ea\u53c2\u6570\u7684\u9ad8\u7cbe\u5ea6\u53cd\u6f14\u65b9\u6cd5\u7814\u7a76","funder_award_id":"41871225","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7935250166","display_name":"\u57fa\u4e8e\u591a\u9891\u6bb5\u7535\u78c1\u6ce2\u4f20\u64ad\u7279\u6027\u7684\u68ee\u6797\u67af\u679d\u843d\u53f6\u5c42\u53c2\u91cf\u53cd\u6f14\u65b9\u6cd5\u7814\u7a76","funder_award_id":"41771400","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320316081","display_name":"Jilin Province Development and Reform Commission","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1964357740","https://openalex.org/W1987157099","https://openalex.org/W2037460024","https://openalex.org/W2054931556","https://openalex.org/W2101664201","https://openalex.org/W2105358797","https://openalex.org/W2155698542","https://openalex.org/W2169087452","https://openalex.org/W2178662488","https://openalex.org/W2295598076","https://openalex.org/W2494454509","https://openalex.org/W2518520674","https://openalex.org/W2560103205","https://openalex.org/W2979460632","https://openalex.org/W2996364565","https://openalex.org/W3116767385"],"related_works":["https://openalex.org/W357196361","https://openalex.org/W2027314909","https://openalex.org/W1036938216","https://openalex.org/W3109425891","https://openalex.org/W2113714434","https://openalex.org/W3096637473","https://openalex.org/W2377792686","https://openalex.org/W4200439127","https://openalex.org/W829658220","https://openalex.org/W2946560178"],"abstract_inverted_index":{"Radar":[0],"images":[1,22],"with":[2,91,211,239],"high":[3],"spatial":[4,94],"resolution":[5],"are":[6],"not":[7,46],"affected":[8],"by":[9],"illumination":[10],"or":[11],"meteorological":[12,101],"conditions,":[13],"which":[14,44],"effectively":[15,271],"compensate":[16],"for":[17,83,268],"the":[18,61,74,80,116,125,130,140,171,176,188,192,201,206,217,245,265,273],"shortcomings":[19],"of":[20,64,142,178,220,248,260,275],"optical":[21],"and":[23,103,114,129,158,174,196,225,233,250,254,270],"passive":[24],"microwave":[25,29],"images.":[26],"Thus,":[27],"active":[28],"remote":[30],"sensing":[31],"technology":[32],"has":[33],"advantages":[34],"in":[35,107,205,214,242],"snow":[36,62],"depth":[37],"(SD)":[38],"research.":[39,59],"Machine":[40],"learning":[41,132],"algorithms":[42],"(MLAs),":[43],"do":[45],"need":[47],"to":[48,57,72,112,138,166],"consider":[49],"complex":[50],"physical":[51],"models,":[52],"have":[53],"increasingly":[54],"been":[55],"applied":[56,165],"SD":[58,81,97,117,146,168,179,203,276],"Considering":[60],"conditions":[63],"different":[65,143,182],"underlying":[66],"surfaces,":[67],"it":[68],"is":[69],"very":[70],"important":[71],"select":[73,264],"appropriate":[75],"parameter":[76,121],"combinations":[77],"(PC)":[78],"reflecting":[79],"information":[82],"MLAs.":[84,183],"In":[85],"this":[86,261],"study,":[87],"C-band":[88,279],"SAR":[89,280],"data":[90,99,106],"20":[92],"m":[93],"resolution,":[95],"ground-based":[96],"observation":[98],"from":[100],"stations,":[102],"field":[104],"measurement":[105],"Northeast":[108],"China":[109],"were":[110,136,164,231,252],"used":[111],"construct":[113],"validate":[115],"estimation":[118,180,277],"method.":[119],"Two":[120],"selection":[122],"methods":[123],"including":[124],"correlation":[126,193],"coefficient":[127,194],"method":[128,135,195],"machine":[131],"(ML)":[133],"fusion":[134],"proposed":[137],"discuss":[139],"influence":[141],"PC":[144,189,267],"on":[145,170],"estimation.":[147],"Then,":[148],"XGBoost,":[149],"random":[150],"forest":[151,243],"(RF),":[152],"linear":[153],"support":[154,160],"vector":[155,161],"regression":[156,162],"(LSVR),":[157],"kernel":[159],"(KSVR)":[163],"estimate":[167],"based":[169],"selected":[172,190],"PC,":[173],"evaluate":[175],"accuracies":[177],"using":[181,191,278],"The":[184,258],"results":[185,204],"demonstrated":[186],"that":[187],"XGBoost":[197,212,240],"algorithm":[198],"could":[199],"achieve":[200],"best":[202],"study":[207],"area.":[208],"Combining":[209,237],"RPC-C":[210],"algorithm,":[213,241],"cropland":[215],"areas,":[216,244],"average":[218,246],"values":[219,247],"mean":[221,227],"absolute":[222],"error":[223,229],"(MAE)":[224],"root":[226],"squared":[228],"(RMSE)":[230],"1.75":[232],"2.58":[234],"cm,":[235,256],"respectively.":[236,257],"RPC-F":[238],"MAE":[249],"RMSE":[251],"3.12":[253],"5.07":[255],"research":[259],"letter":[262],"can":[263],"optimal":[266],"MLAs":[269],"improve":[272],"accuracy":[274],"data.":[281]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}