{"id":"https://openalex.org/W4312801239","doi":"https://doi.org/10.1109/igarss46834.2022.9884370","title":"Learning Snow Layer Thickness Through Physics Defined Labels","display_name":"Learning Snow Layer Thickness Through Physics Defined Labels","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312801239","doi":"https://doi.org/10.1109/igarss46834.2022.9884370"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884370","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5071422014","display_name":"Debvrat Varshney","orcid":"https://orcid.org/0000-0001-8898-1736"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Debvrat Varshney","raw_affiliation_strings":["University of Maryland Baltimore County,Computer Vision and Remote Sensing Laboratory,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland Baltimore County,Computer Vision and Remote Sensing Laboratory,USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036470882","display_name":"Oluwanisola Ibikunle","orcid":"https://orcid.org/0000-0002-9392-7360"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]},{"id":"https://openalex.org/I4210144928","display_name":"Center for Remote Sensing and Integrated Systems","ror":"https://ror.org/04t2m2598","country_code":"US","type":"facility","lineage":["https://openalex.org/I146416000","https://openalex.org/I4210144928"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oluwanisola Ibikunle","raw_affiliation_strings":["University of Kansas,Center of Remote Sensing of Ice Sheets (CReSIS),USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kansas,Center of Remote Sensing of Ice Sheets (CReSIS),USA","institution_ids":["https://openalex.org/I4210144928","https://openalex.org/I146416000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071801848","display_name":"John Paden","orcid":"https://orcid.org/0000-0003-0775-6284"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]},{"id":"https://openalex.org/I4210144928","display_name":"Center for Remote Sensing and Integrated Systems","ror":"https://ror.org/04t2m2598","country_code":"US","type":"facility","lineage":["https://openalex.org/I146416000","https://openalex.org/I4210144928"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Paden","raw_affiliation_strings":["University of Kansas,Center of Remote Sensing of Ice Sheets (CReSIS),USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kansas,Center of Remote Sensing of Ice Sheets (CReSIS),USA","institution_ids":["https://openalex.org/I4210144928","https://openalex.org/I146416000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010792548","display_name":"Maryam Rahnemoonfar","orcid":"https://orcid.org/0000-0001-9358-2836"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryam Rahnemoonfar","raw_affiliation_strings":["University of Maryland Baltimore County,Computer Vision and Remote Sensing Laboratory,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland Baltimore County,Computer Vision and Remote Sensing Laboratory,USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.4915,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.97413793,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1233","last_page":"1236"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10644","display_name":"Cryospheric studies and observations","score":0.9998999834060669,"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":0.9998999834060669,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9979000091552734,"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/T10535","display_name":"Landslides and related hazards","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/snow","display_name":"Snow","score":0.8965317606925964},{"id":"https://openalex.org/keywords/polar","display_name":"Polar","score":0.6193478107452393},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6000045537948608},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5467161536216736},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5445303320884705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5166692733764648},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4923964738845825},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48693859577178955},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4690822660923004},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4331774115562439},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4318801760673523},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3766865134239197},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3743640184402466},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3494653105735779},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2643769085407257},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.2511860728263855},{"id":"https://openalex.org/keywords/nanotechnology","display_name":"Nanotechnology","score":0.13850492238998413},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12344059348106384},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09478169679641724}],"concepts":[{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.8965317606925964},{"id":"https://openalex.org/C29705727","wikidata":"https://www.wikidata.org/wiki/Q294562","display_name":"Polar","level":2,"score":0.6193478107452393},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6000045537948608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5467161536216736},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5445303320884705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5166692733764648},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4923964738845825},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48693859577178955},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4690822660923004},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4331774115562439},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4318801760673523},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3766865134239197},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3743640184402466},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3494653105735779},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2643769085407257},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.2511860728263855},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.13850492238998413},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12344059348106384},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09478169679641724},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884370","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.8500000238418579,"display_name":"Life below water"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320307762","display_name":"International Business Machines Corporation","ror":"https://ror.org/05hh8d621"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1513512681","https://openalex.org/W2183341477","https://openalex.org/W2252220274","https://openalex.org/W2531409750","https://openalex.org/W2919115771","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2986457312","https://openalex.org/W3006741963","https://openalex.org/W3010993481","https://openalex.org/W3081559251","https://openalex.org/W3092922965","https://openalex.org/W3207947259","https://openalex.org/W4244266021","https://openalex.org/W6774822518","https://openalex.org/W6800641259"],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W3192840557","https://openalex.org/W2951211570","https://openalex.org/W4375928479","https://openalex.org/W3167935049","https://openalex.org/W3023427754","https://openalex.org/W3131673289","https://openalex.org/W4393011546","https://openalex.org/W3198847674"],"abstract_inverted_index":{"Increasing":[0],"global":[1],"temperatures":[2],"are":[3,58,65],"adversely":[4],"affecting":[5],"the":[6,24,53,80,118],"polar":[7],"ice":[8,35],"sheets":[9],"and":[10,21,60],"contributing":[11],"to":[12,51],"sea":[13],"level":[14],"rise.":[15],"The":[16,37],"situation":[17],"requires":[18],"constant":[19],"monitoring":[20,38],"analysis":[22],"of":[23,28,34,82],"change":[25],"in":[26,91],"thickness":[27,81,113],"snow":[29,83,111],"layers":[30,84],"accumulated":[31],"on":[32,117],"top":[33],"sheets.":[36],"can":[39],"be":[40],"performed":[41],"through":[42,86],"radar":[43,54],"sensors,":[44],"but":[45],"current":[46],"methods":[47],"aren't":[48],"efficient":[49],"enough":[50],"process":[52],"images":[55],"since":[56],"they":[57],"noisy,":[59],"lack":[61],"quality":[62],"annotations,":[63],"which":[64],"required":[66],"by":[67,115],"state-of-the-art":[68],"deep":[69,94],"learning":[70,79,95,102],"algorithms.":[71],"In":[72],"this":[73],"work,":[74],"we":[75,98],"show":[76,99],"that":[77,100],"first":[78],"simulated":[85],"a":[87,104],"physical":[88],"model":[89],"helps":[90],"building":[92],"robust":[93],"networks.":[96],"Specifically":[97],"transfer":[101],"from":[103],"network":[105],"trained":[106],"with":[107],"physics-defined":[108],"labels":[109],"improves":[110],"layer":[112],"estimates":[114],"6-29%":[116],"test":[119],"set.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
