{"id":"https://openalex.org/W2027157694","doi":"https://doi.org/10.1109/tgrs.2012.2193403","title":"Mapping Cropland Distributions Using a Hard and Soft Classification Model","display_name":"Mapping Cropland Distributions Using a Hard and Soft Classification Model","publication_year":2012,"publication_date":"2012-07-11","ids":{"openalex":"https://openalex.org/W2027157694","doi":"https://doi.org/10.1109/tgrs.2012.2193403","mag":"2027157694"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2012.2193403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2193403","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/A5078753250","display_name":"Yaozhong Pan","orcid":"https://orcid.org/0000-0002-2307-2715"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaozhong Pan","raw_affiliation_strings":["State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707170","display_name":"Tangao Hu","orcid":"https://orcid.org/0000-0003-4448-2614"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tangao Hu","raw_affiliation_strings":["Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015123283","display_name":"Xiufang Zhu","orcid":"https://orcid.org/0000-0002-6660-2034"},"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/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Xiufang Zhu","raw_affiliation_strings":["Department of Geography, University of Maryland, College Park, MD, USA","State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101851035","display_name":"Jinshui Zhang","orcid":null},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinshui Zhang","raw_affiliation_strings":["State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100382682","display_name":"Xiaodong Wang","orcid":"https://orcid.org/0000-0002-7436-5390"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Wang","raw_affiliation_strings":["State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Earth Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.6881,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94816427,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"50","issue":"11","first_page":"4301","last_page":"4312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9746000170707703,"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/pixel","display_name":"Pixel","score":0.7398525476455688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6073802709579468},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5412745475769043},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5124676823616028},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43532827496528625},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4150771498680115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4143713414669037},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3682481646537781},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32461562752723694},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.30154499411582947},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2631612718105316},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1996670961380005},{"id":"https://openalex.org/keywords/china","display_name":"China","score":0.12322327494621277}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7398525476455688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6073802709579468},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5412745475769043},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5124676823616028},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43532827496528625},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4150771498680115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4143713414669037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3682481646537781},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32461562752723694},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.30154499411582947},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2631612718105316},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1996670961380005},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.12322327494621277},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2012.2193403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2193403","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":[{"id":"https://metadata.un.org/sdg/2","score":0.4300000071525574,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1499492593","https://openalex.org/W1510526001","https://openalex.org/W1530278140","https://openalex.org/W1537844550","https://openalex.org/W1891144596","https://openalex.org/W1967400946","https://openalex.org/W1974776350","https://openalex.org/W1993417238","https://openalex.org/W1999110225","https://openalex.org/W2005156666","https://openalex.org/W2006280268","https://openalex.org/W2008085934","https://openalex.org/W2008972816","https://openalex.org/W2009265767","https://openalex.org/W2015008560","https://openalex.org/W2019899368","https://openalex.org/W2024968541","https://openalex.org/W2037459505","https://openalex.org/W2062321700","https://openalex.org/W2065800647","https://openalex.org/W2069406300","https://openalex.org/W2071902553","https://openalex.org/W2078619499","https://openalex.org/W2080210566","https://openalex.org/W2087306084","https://openalex.org/W2087556827","https://openalex.org/W2095795470","https://openalex.org/W2096483002","https://openalex.org/W2100903394","https://openalex.org/W2103568877","https://openalex.org/W2104520867","https://openalex.org/W2108493207","https://openalex.org/W2109343601","https://openalex.org/W2112341432","https://openalex.org/W2114828048","https://openalex.org/W2116699851","https://openalex.org/W2123907688","https://openalex.org/W2124706543","https://openalex.org/W2128802552","https://openalex.org/W2129305329","https://openalex.org/W2132249643","https://openalex.org/W2136635809","https://openalex.org/W2144881411","https://openalex.org/W2156909104","https://openalex.org/W2161372002","https://openalex.org/W2165161028","https://openalex.org/W2165927551","https://openalex.org/W2168809519","https://openalex.org/W2172000360","https://openalex.org/W4235880969","https://openalex.org/W4302190632","https://openalex.org/W6630424276"],"related_works":["https://openalex.org/W2088899772","https://openalex.org/W3133615129","https://openalex.org/W2087854757","https://openalex.org/W2188959887","https://openalex.org/W2373152553","https://openalex.org/W3192667092","https://openalex.org/W4389201442","https://openalex.org/W2386169820","https://openalex.org/W604251607","https://openalex.org/W2043913960"],"abstract_inverted_index":{"Accurate":[0],"and":[1,7,18,23,100,107,109,167,185,201,209],"timely":[2],"information":[3],"regarding":[4],"the":[5,30,160,163,183,190],"location":[6],"area":[8],"of":[9,93],"major":[10],"crop":[11],"types":[12],"has":[13,162],"significant":[14],"economic,":[15],"food,":[16],"policy,":[17],"environmental":[19],"implications.":[20],"Both":[21],"hard":[22,99],"soft":[24,71],"classification":[25,44,72,165],"methods":[26,186],"are":[27,57,68,75],"used":[28],"throughout":[29],"growing":[31],"season":[32],"to":[33,77,147,206],"generate":[34],"cropland":[35,171],"distribution":[36,172],"maps":[37,173],"using":[38,112,132],"multiple":[39],"remotely":[40],"sensed":[41],"data.":[42],"Hard":[43],"models":[45,73],"(HCMs)":[46],"yield":[47],"good":[48],"results":[49,157],"in":[50,62,81,85,188,214],"large":[51],"homogeneous":[52],"areas":[53,64,83],"where":[54,65],"pure":[55,88],"pixels":[56,67],"dominant,":[58],"but":[59],"they":[60,202],"fail":[61],"fragmented":[63,82],"mixed":[66],"dominant.":[69],"Conversely,":[70],"(SCMs)":[74],"thought":[76],"have":[78],"greater":[79],"accuracy":[80,166],"than":[84,174],"regions":[86],"with":[87],"pixels.":[89,155],"To":[90],"take":[91],"advantage":[92],"both":[94],"methods,":[95],"we":[96],"develop":[97],"a":[98,193],"SCM":[101],"(HSCM)":[102],"based":[103],"on":[104],"existing":[105],"HCMs":[106,179,208],"SCMs,":[108],"test":[110],"it":[111],"data":[113,122],"from":[114,123,139],"simulated":[115],"images":[116],"as":[117,119],"well":[118],"actual":[120],"satellite":[121],"southeast":[124],"Beijing,":[125],"China.":[126],"The":[127,156],"model":[128],"assessment":[129],"was":[130],"performed":[131],"three":[133],"statistical":[134],"metrics":[135],"at":[136],"scales":[137],"ranging":[138],"1":[140,146],"<formula":[141,149],"formulatype=\"inline\"":[142,150],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[143,151],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex":[144,152],"Notation=\"TeX\">$\\times$</tex></formula>":[145,153],"10":[148,154],"reveal":[158],"that":[159],"HSCM":[161,191],"highest":[164],"produces":[168],"more":[169],"reasonable":[170],"those":[175,212],"produced":[176],"by":[177],"either":[178],"or":[180],"SCMs.":[181],"Moreover,":[182],"theory":[184],"employed":[187],"developing":[189],"provide":[192],"unifying":[194],"framework":[195],"for":[196],"mapping":[197],"land":[198],"cover":[199],"types,":[200],"can":[203],"be":[204],"applied":[205],"different":[207],"SCMs":[210],"beyond":[211],"currently":[213],"use.":[215]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}