{"id":"https://openalex.org/W4282575352","doi":"https://doi.org/10.3390/rs14122758","title":"The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine","display_name":"The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine","publication_year":2022,"publication_date":"2022-06-08","ids":{"openalex":"https://openalex.org/W4282575352","doi":"https://doi.org/10.3390/rs14122758"},"language":"en","primary_location":{"id":"doi:10.3390/rs14122758","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122758","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/rs14122758","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055290494","display_name":"Jinxi Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinxi Yao","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China","Key Laboratory of the Northern Qinghai\u2013Tibet Plateau Geological Processes and Mineral Resources, Xining 810300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"Key Laboratory of the Northern Qinghai\u2013Tibet Plateau Geological Processes and Mineral Resources, Xining 810300, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031712321","display_name":"Ji Wu","orcid":"https://orcid.org/0000-0002-7872-9315"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Wu","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088564878","display_name":"Chengzhi Xiao","orcid":"https://orcid.org/0000-0002-8754-9711"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengzhi Xiao","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006808233","display_name":"Zhi Zhang","orcid":"https://orcid.org/0000-0001-8604-6234"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhi Zhang","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100387073","display_name":"Jianzhong Li","orcid":"https://orcid.org/0009-0008-2075-0219"},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzhong Li","raw_affiliation_strings":["Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China","institution_ids":["https://openalex.org/I2799486974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006808233"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":10.0994,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.98869088,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"14","issue":"12","first_page":"2758","last_page":"2758"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9997000098228455,"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/T10616","display_name":"Smart Agriculture and AI","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9729999899864197,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6861078143119812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6082952618598938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5605059266090393},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5352711081504822},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5165868997573853},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.46185290813446045},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4208187758922577},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41208624839782715},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3221016824245453},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12712198495864868}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6861078143119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6082952618598938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5605059266090393},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5352711081504822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5165868997573853},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.46185290813446045},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4208187758922577},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41208624839782715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3221016824245453},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12712198495864868}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14122758","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122758","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3ce45b091ea84a17a90d0f2dc8446664","is_oa":true,"landing_page_url":"https://doaj.org/article/3ce45b091ea84a17a90d0f2dc8446664","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 12, p 2758 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/12/2758/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14122758","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 12; Pages: 2758","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14122758","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122758","pdf_url":null,"source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W2035549557","https://openalex.org/W2048850076","https://openalex.org/W2058723831","https://openalex.org/W2075918924","https://openalex.org/W2095649738","https://openalex.org/W2108597246","https://openalex.org/W2133941557","https://openalex.org/W2167594433","https://openalex.org/W2522991697","https://openalex.org/W2725897987","https://openalex.org/W2770287273","https://openalex.org/W2790899344","https://openalex.org/W2791592925","https://openalex.org/W2886493749","https://openalex.org/W2890225206","https://openalex.org/W2890942070","https://openalex.org/W2897722020","https://openalex.org/W2900217217","https://openalex.org/W2901719150","https://openalex.org/W2903282641","https://openalex.org/W2907200564","https://openalex.org/W2939118835","https://openalex.org/W2943472941","https://openalex.org/W2972321769","https://openalex.org/W2987982347","https://openalex.org/W2999954079","https://openalex.org/W3045755298","https://openalex.org/W3045918052","https://openalex.org/W3082766779","https://openalex.org/W3087890773","https://openalex.org/W3100299978","https://openalex.org/W3105348024","https://openalex.org/W3119804057","https://openalex.org/W3123352549","https://openalex.org/W3127060672","https://openalex.org/W3133368940","https://openalex.org/W3137356499","https://openalex.org/W3158568413","https://openalex.org/W3164419424","https://openalex.org/W3166869167","https://openalex.org/W3184043483","https://openalex.org/W3185118158","https://openalex.org/W3193619641","https://openalex.org/W3202818215","https://openalex.org/W3210159389","https://openalex.org/W3213181250","https://openalex.org/W4206241977","https://openalex.org/W4210334192","https://openalex.org/W4223520639","https://openalex.org/W4224127181","https://openalex.org/W6807295046"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W3135126032","https://openalex.org/W1924178503","https://openalex.org/W4308716060","https://openalex.org/W4280648719","https://openalex.org/W4394984040","https://openalex.org/W4380075502"],"abstract_inverted_index":{"The":[0,14,120,170,217,265],"extraction":[1,306],"and":[2,27,34,42,75,104,117,126,136,143,147,161,187,205,220,227,255,267,286,288,304,313,327],"classification":[3,16,72,91,154,167,192,199,224,236,269,301],"of":[4,10,17,21,29,55,99,106,123,151,158,184,213,223,238,281,307,317],"crops":[5,30,108,124,166,176,321,324,328],"is":[6,20,231,316],"the":[7,25,38,50,96,110,132,137,149,152,156,175,178,182,235,279,289,300],"core":[8],"issue":[9],"agricultural":[11,52],"remote":[12],"sensing.":[13],"precise":[15],"crop":[18,191,308],"types":[19,309],"great":[22,318],"significance":[23,319],"to":[24,278],"monitoring":[26],"evaluation":[28],"planting":[31],"area,":[32],"growth,":[33],"yield.":[35],"Based":[36],"on":[37],"Google":[39,43],"Earth":[40],"Engine":[41],"Colab":[44],"cloud":[45,282],"platform,":[46],"this":[47,94,273],"study":[48,111,179],"takes":[49],"typical":[51],"oasis":[53],"area":[54,112,180],"Xiangride":[56],"Town,":[57],"Qinghai":[58],"Province,":[59],"as":[60],"an":[61],"example.":[62],"It":[63,230],"compares":[64],"traditional":[65,290],"machine":[66,291],"learning":[67,292,296],"(random":[68],"forest,":[69],"RF),":[70],"object-oriented":[71],"(object-oriented,":[73],"OO),":[74],"deep":[76,87,256,295],"neural":[77,88,257],"networks":[78],"(DNN),":[79],"which":[80],"proposes":[81],"a":[82,189],"random":[83,239],"forest":[84,240],"combined":[85,293],"with":[86,294],"network":[89,258],"(RF+DNN)":[90],"framework.":[92],"In":[93],"study,":[95],"spatial":[97,312],"characteristics":[98,122],"band":[100],"information,":[101],"vegetation":[102],"index,":[103],"polarization":[105],"main":[107],"in":[109,141,165,177,272,284],"were":[113,129,139,168,209,225],"constructed":[114],"using":[115,131,197,214],"Sentinel-1":[116],"Sentinel-2":[118],"data.":[119],"temporal":[121,314],"phenology":[125],"growth":[127,186],"state":[128],"analyzed":[130],"curve":[133],"curvature":[134],"method,":[135,200],"data":[138,285],"screened":[140],"time":[142,207],"space.":[144],"By":[145],"comparing":[146],"analyzing":[148],"accuracy":[150,219,237],"four":[153],"methods,":[155],"advantages":[157,280],"RF+DNN":[159,198],"model":[160,202],"its":[162],"application":[163],"value":[164],"illustrated.":[169],"results":[171],"showed":[172],"that":[173,212],"for":[174,320],"during":[181],"period":[183],"good":[185],"development,":[188],"better":[190,210],"result":[193],"could":[194],"be":[195],"obtained":[196],"whose":[201],"accuracy,":[203],"training,":[204],"predict":[206],"spent":[208],"than":[211,234],"DNN":[215],"alone.":[216],"overall":[218],"Kappa":[221,244,252,262],"coefficient":[222],"0.98":[226],"0.97,":[228],"respectively.":[229],"also":[232],"higher":[233],"(OA":[241,249,259],"=":[242,245,250,253,260,263],"0.87,":[243],"0.82),":[246],"object":[247],"oriented":[248],"0.78,":[251],"0.70)":[254],"0.93,":[261],"0.90).":[264],"scalable":[266],"simple":[268],"method":[270],"proposed":[271],"paper":[274],"gives":[275],"full":[276],"play":[277],"platform":[283],"operation,":[287],"can":[297],"effectively":[298],"improve":[299],"accuracy.":[302],"Timely":[303],"accurate":[305],"at":[310],"different":[311],"scales":[315],"pattern":[322],"change,":[323],"yield":[325],"estimation,":[326],"safety":[329],"warning.":[330]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
