{"id":"https://openalex.org/W4387829260","doi":"https://doi.org/10.1109/igarss52108.2023.10282334","title":"Synergistic Use of Sentinel-1 and Sentinel-2 Images for in-Season Crop Type Classification Using Google Earth Engine and Machine Learning","display_name":"Synergistic Use of Sentinel-1 and Sentinel-2 Images for in-Season Crop Type Classification Using Google Earth Engine and Machine Learning","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387829260","doi":"https://doi.org/10.1109/igarss52108.2023.10282334"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10282334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 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/A5103980676","display_name":"Sneha Sharma","orcid":"https://orcid.org/0000-0003-4208-7151"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sneha Sharma","raw_affiliation_strings":["The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091637998","display_name":"Dongryeol Ryu","orcid":"https://orcid.org/0000-0002-5335-6209"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dongryeol Ryu","raw_affiliation_strings":["The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111060903","display_name":"K C Sumesh","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sumesh K C","raw_affiliation_strings":["The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne,Department of Infrastructure Engineering,Victoria,Australia,3010","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081785443","display_name":"Sun\u2010Gu Lee","orcid":"https://orcid.org/0000-0002-3331-1791"},"institutions":[{"id":"https://openalex.org/I2800747041","display_name":"Korea Aerospace Research Institute","ror":"https://ror.org/037pqnq23","country_code":"KR","type":"government","lineage":["https://openalex.org/I2800747041","https://openalex.org/I2801339556","https://openalex.org/I4387152098","https://openalex.org/I4405260336"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sun-Gu Lee","raw_affiliation_strings":["National Satellite Operation and Application Center, Korea Aerospace Research Institute,Satellite Application Division,Daejeon,South Korea","Satellite Application Division, National Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Satellite Operation and Application Center, Korea Aerospace Research Institute,Satellite Application Division,Daejeon,South Korea","institution_ids":["https://openalex.org/I2800747041"]},{"raw_affiliation_string":"Satellite Application Division, National Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon, South Korea","institution_ids":["https://openalex.org/I2800747041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009791117","display_name":"Seungtaek Jeong","orcid":"https://orcid.org/0000-0003-2989-1364"},"institutions":[{"id":"https://openalex.org/I2800747041","display_name":"Korea Aerospace Research Institute","ror":"https://ror.org/037pqnq23","country_code":"KR","type":"government","lineage":["https://openalex.org/I2800747041","https://openalex.org/I2801339556","https://openalex.org/I4387152098","https://openalex.org/I4405260336"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungtaek Jeong","raw_affiliation_strings":["National Satellite Operation and Application Center, Korea Aerospace Research Institute,Satellite Application Division,Daejeon,South Korea","Satellite Application Division, National Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Satellite Operation and Application Center, Korea Aerospace Research Institute,Satellite Application Division,Daejeon,South Korea","institution_ids":["https://openalex.org/I2800747041"]},{"raw_affiliation_string":"Satellite Application Division, National Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon, South Korea","institution_ids":["https://openalex.org/I2800747041"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.4463,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.95910112,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3498","last_page":"3501"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9962999820709229,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7533072233200073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7340657711029053},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.716498613357544},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6697682738304138},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5913822054862976},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5818881392478943},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5754754543304443},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5081377029418945},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4937516152858734},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.47539958357810974},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4421749413013458},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.44034329056739807},{"id":"https://openalex.org/keywords/earth-observation","display_name":"Earth observation","score":0.4169777035713196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3340635299682617},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13152742385864258},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12565654516220093},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11980661749839783},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.08419820666313171}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7533072233200073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7340657711029053},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.716498613357544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6697682738304138},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5913822054862976},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5818881392478943},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5754754543304443},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5081377029418945},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4937516152858734},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.47539958357810974},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4421749413013458},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.44034329056739807},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.4169777035713196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3340635299682617},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13152742385864258},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12565654516220093},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11980661749839783},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.08419820666313171},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10282334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5199999809265137,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322114","display_name":"Korea Aerospace Research Institute","ror":"https://ror.org/037pqnq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1967720470","https://openalex.org/W3138000966","https://openalex.org/W3217764757","https://openalex.org/W4212921346","https://openalex.org/W4220942385","https://openalex.org/W4288987532","https://openalex.org/W4292975570"],"related_works":["https://openalex.org/W1975547468","https://openalex.org/W2804627982","https://openalex.org/W2001679188","https://openalex.org/W4205493345","https://openalex.org/W2196068029","https://openalex.org/W2016342027","https://openalex.org/W2888859519","https://openalex.org/W1538678705","https://openalex.org/W2310826128","https://openalex.org/W2974328778"],"abstract_inverted_index":{"In-season":[0],"crop":[1,19,72],"type":[2,20,73],"mapping":[3],"can":[4],"assist":[5],"in":[6],"early":[7],"yield":[8],"estimation,":[9],"however,":[10],"such":[11],"data":[12,50],"are":[13],"not":[14],"widely":[15],"available.":[16],"Currently":[17],"available":[18],"maps":[21],"mostly":[22],"rely":[23],"on":[24],"either":[25],"optical":[26,47,65,126],"imagery":[27],"or":[28],"synthetic":[29],"aperture":[30],"radar":[31],"(SAR),":[32],"but":[33],"there":[34],"is":[35],"a":[36],"growing":[37],"number":[38],"of":[39,45,57,77,108,125],"research":[40,53],"that":[41,61,100],"demonstrates":[42],"the":[43,55,101,105,117,123],"potential":[44],"synergistic":[46],"and":[48,66,90,110,128,137],"SAR":[49,67,129],"fusion.":[51],"This":[52],"investigates":[54],"performance":[56,76,103],"machine":[58,80],"learning":[59,81],"approaches":[60],"account":[62],"for":[63,104],"both":[64],"features":[68],"to":[69],"generate":[70],"in-season":[71,106,133],"maps.":[74],"Classification":[75],"three":[78],"supervised":[79],"algorithms:":[82],"Random":[83],"Forest":[84],"(RF),":[85],"Support":[86],"Vector":[87],"Machine":[88],"(SVM)":[89],"Extreme":[91],"Gradient":[92],"Boosting":[93],"(XGBoost),":[94],"were":[95],"tested.":[96],"Experimental":[97],"results":[98],"demonstrate":[99],"best":[102],"classification":[107,134],"corn":[109],"soybeans":[111],"was":[112],"obtained":[113],"four":[114],"months":[115],"after":[116],"sowing":[118],"(April":[119],"\u2013":[120],"July)":[121],"from":[122,135],"fusion":[124],"(Sentinel-2)":[127],"(Sentinel-1)":[130],"images.":[131],"The":[132],"SVM":[136],"RF":[138],"demonstrated":[139],"81.2":[140],"%":[141],"(overall":[142],"accuracy)":[143],"agreement":[144],"with":[145],"ground":[146],"truth.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}