{"id":"https://openalex.org/W4312538649","doi":"https://doi.org/10.1109/igarss46834.2022.9884171","title":"Fractional Snow Cover Mapping with High Spatiotemporal Resolution based on Landsat, Sentinel-2 And Modis Observation","display_name":"Fractional Snow Cover Mapping with High Spatiotemporal Resolution based on Landsat, Sentinel-2 And Modis Observation","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312538649","doi":"https://doi.org/10.1109/igarss46834.2022.9884171"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884171","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":"conference-paper","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/A5100439686","display_name":"Cheng Zhang","orcid":"https://orcid.org/0000-0003-1020-0850"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Zhang","raw_affiliation_strings":["Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing Normal University,State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science,Beijing,China,100875"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing Normal University,State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941","https://openalex.org/I4210137199","https://openalex.org/I4210166112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056336938","display_name":"Lingmei Jiang","orcid":"https://orcid.org/0000-0002-9847-9034"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingmei Jiang","raw_affiliation_strings":["Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing Normal University,State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science,Beijing,China,100875"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing Normal University,State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941","https://openalex.org/I4210137199","https://openalex.org/I4210166112"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3935","last_page":"3938"},"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":0.9976000189781189,"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":0.9951000213623047,"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/endmember","display_name":"Endmember","score":0.7245346307754517},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6660391688346863},{"id":"https://openalex.org/keywords/snow","display_name":"Snow","score":0.592110276222229},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5561370849609375},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5118104815483093},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.47208473086357117},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.46886593103408813},{"id":"https://openalex.org/keywords/snow-cover","display_name":"Snow cover","score":0.41702964901924133},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.38349542021751404},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2426958978176117},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.23786678910255432},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18333929777145386},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.18077176809310913},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15763479471206665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.08880147337913513},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07212108373641968}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.7245346307754517},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6660391688346863},{"id":"https://openalex.org/C197046000","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow","level":2,"score":0.592110276222229},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5561370849609375},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5118104815483093},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.47208473086357117},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.46886593103408813},{"id":"https://openalex.org/C2983043445","wikidata":"https://www.wikidata.org/wiki/Q7561","display_name":"Snow cover","level":3,"score":0.41702964901924133},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.38349542021751404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2426958978176117},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.23786678910255432},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18333929777145386},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.18077176809310913},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15763479471206665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.08880147337913513},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07212108373641968},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884171","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/13","score":0.4399999976158142,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G8788236040","display_name":null,"funder_award_id":"42171317","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1522412705","https://openalex.org/W2002604925","https://openalex.org/W2022106079","https://openalex.org/W2103794190","https://openalex.org/W2144718584","https://openalex.org/W2234018419","https://openalex.org/W2751786729","https://openalex.org/W2799417842","https://openalex.org/W2900514107","https://openalex.org/W2996984840","https://openalex.org/W3188276472"],"related_works":["https://openalex.org/W2396393741","https://openalex.org/W2388090620","https://openalex.org/W2380645478","https://openalex.org/W1987033298","https://openalex.org/W2167444906","https://openalex.org/W1965781346","https://openalex.org/W2140166023","https://openalex.org/W2567937534","https://openalex.org/W2047228190","https://openalex.org/W4308392470"],"abstract_inverted_index":{"Fractional":[0],"snow":[1],"cover":[2],"(FSC)":[3],"mapping":[4],"with":[5,63,76,90,99],"high":[6,89],"spatiotemporal":[7],"resolution":[8,66,70],"is":[9,88],"of":[10,16,84,102],"great":[11],"significance":[12],"to":[13,41],"the":[14,30,51,82,85,91,100],"study":[15],"surface":[17],"hydrological":[18],"processes,":[19],"agricultural":[20],"irrigation,":[21],"and":[22,47,67],"disaster":[23],"monitoring.":[24],"In":[25],"this":[26],"study,":[27],"we":[28],"use":[29],"spectral":[31],"mixture":[32],"analysis":[33],"based":[34],"on":[35],"automatic":[36],"endmember":[37],"extraction":[38],"(MESMA-AGE)":[39],"algorithm":[40,58],"retrieve":[42],"FSC":[43,61,86],"from":[44],"Landsat-5/7/8,":[45],"Sentinel-2,":[46],"MODIS":[48],"data":[49],"using":[50],"Google":[52],"Earth":[53],"Engine":[54],"(GEE)":[55],"platform.":[56],"The":[57,78],"can":[59],"produce":[60],"product":[62,87],"30m":[64],"spatial":[65],"4-day":[68],"temporal":[69],"at":[71],"regional":[72],"scale,":[73],"even":[74],"globally":[75],"GEE.":[77],"result":[79],"shows":[80],"that":[81],"accuracy":[83],"root":[92],"mean":[93],"square":[94],"error":[95],"(RMSE)":[96],"being":[97],"0.18":[98],"comparison":[101],"Gaofen-2":[103],"imageries.":[104]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
