{"id":"https://openalex.org/W7225319467","doi":"https://doi.org/10.1038/s41598-026-79035-7","title":"Mapping of winter wheat with UNet-Transformer fusion model based on Sentinel-2 A imagery and field observation data","display_name":"Mapping of winter wheat with UNet-Transformer fusion model based on Sentinel-2 A imagery and field observation data","publication_year":2026,"publication_date":"2026-11-05","ids":{"openalex":"https://openalex.org/W7225319467","doi":"https://doi.org/10.1038/s41598-026-79035-7"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-79035-7","doi":"https://doi.org/10.1038/s41598-026-79035-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-79035-7","pdf_url":"https://www.nature.com/articles/s41598-026-79035-7_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.nature.com/articles/s41598-026-79035-7_reference.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5144289936","display_name":"Yuanyuan Tang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210119012","display_name":"Xichang University","ror":"https://ror.org/02h3fyk31","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119012"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Tang","raw_affiliation_strings":["College of Resources and Environment, Liangshan Yi Autonomous Prefecture, Xichang University, No.1 Xuefu Road, Xichang City, 615013, Sichuan Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Resources and Environment, Liangshan Yi Autonomous Prefecture, Xichang University, No.1 Xuefu Road, Xichang City, 615013, Sichuan Province, China","institution_ids":["https://openalex.org/I4210119012"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160913505","display_name":"Linhong Yin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210103524","display_name":"Guangdong Academy of Sciences","ror":"https://ror.org/01g9hkj35","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210103524"]},{"id":"https://openalex.org/I4210147158","display_name":"Guangzhou Institute of Geography","ror":"https://ror.org/03tbxt129","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210147158"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linhong Yin","raw_affiliation_strings":["Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China","institution_ids":["https://openalex.org/I4210103524","https://openalex.org/I4210147158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080073057","display_name":"Qi YanBing","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanbing Qi","raw_affiliation_strings":["College of Natural Resources and Environment, Northwest A&F University, No.3 Taicheng Road, Yangling, 712100, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Natural Resources and Environment, Northwest A&F University, No.3 Taicheng Road, Yangling, 712100, Shaanxi Province, China","institution_ids":["https://openalex.org/I89652312"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080073057"],"corresponding_institution_ids":["https://openalex.org/I89652312"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.8457000255584717,"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":0.8457000255584717,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.10830000042915344,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.04129999876022339,"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/crop-classification","display_name":"crop classification","score":0.722000002861023},{"id":"https://openalex.org/keywords/sentinel-2-imagery","display_name":"Sentinel-2 imagery","score":0.8299999833106995},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.8970000147819519},{"id":"https://openalex.org/keywords/segnet","display_name":"SegNet","score":0.7300000190734863},{"id":"https://openalex.org/keywords/deeplabv3","display_name":"DeepLabV3+","score":0.9900000095367432},{"id":"https://openalex.org/keywords/high-resolution-crop-mapping","display_name":"high-resolution crop mapping","score":0.7699999809265137},{"id":"https://openalex.org/keywords/guanzhong-plain","display_name":"Guanzhong Plain","score":0.8759999871253967}],"concepts":[{"id":"https://openalex.org/C3018661444","wikidata":"https://www.wikidata.org/wiki/Q6977574","display_name":"Winter wheat","level":2,"score":0.6607999801635742},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.659500002861023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5763000249862671},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5705999732017517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656999945640564},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5623000264167786},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5249000191688538},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5221999883651733},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48030000925064087},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4104999899864197},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3919999897480011},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.3912000060081482},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2538999915122986},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1038/s41598-026-79035-7","doi":"https://doi.org/10.1038/s41598-026-79035-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-79035-7","pdf_url":"https://www.nature.com/articles/s41598-026-79035-7_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-79035-7","doi":"https://doi.org/10.1038/s41598-026-79035-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-79035-7","pdf_url":"https://www.nature.com/articles/s41598-026-79035-7_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9208999872207642,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320336567","display_name":"Natural Science Basic Research Program of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225319467.pdf","grobid_xml":"https://content.openalex.org/works/W7225319467.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W2056435747","https://openalex.org/W2100495367","https://openalex.org/W2786038065","https://openalex.org/W2798174855","https://openalex.org/W2903282641","https://openalex.org/W2912813928","https://openalex.org/W2919115771","https://openalex.org/W2966744467","https://openalex.org/W2978232608","https://openalex.org/W3004492228","https://openalex.org/W3017113337","https://openalex.org/W3018158283","https://openalex.org/W3037002701","https://openalex.org/W3088162569","https://openalex.org/W3171116005","https://openalex.org/W3187418919","https://openalex.org/W3216564922","https://openalex.org/W4200360302","https://openalex.org/W4225661215","https://openalex.org/W4250625077","https://openalex.org/W4297369021","https://openalex.org/W4309839560","https://openalex.org/W4386630433","https://openalex.org/W4386707311","https://openalex.org/W4390541318","https://openalex.org/W4391131603","https://openalex.org/W4391331843","https://openalex.org/W4391733641","https://openalex.org/W4392508759","https://openalex.org/W4399686814","https://openalex.org/W4400445930","https://openalex.org/W4401963252","https://openalex.org/W4402507990","https://openalex.org/W4403295012","https://openalex.org/W4403298038","https://openalex.org/W4404734944","https://openalex.org/W4407418866"],"related_works":["https://openalex.org/W4399686814","https://openalex.org/W4312443662","https://openalex.org/W4404517058","https://openalex.org/W4413143011","https://openalex.org/W7160535962","https://openalex.org/W4409883209","https://openalex.org/W4310211445","https://openalex.org/W4383551442","https://openalex.org/W7124856429","https://openalex.org/W4288040480"],"abstract_inverted_index":{"Accurate":[0],"high-resolution":[1,202],"winter":[2,31,98,130,169],"wheat":[3,32,99,131,170],"mapping":[4],"remains":[5],"challenging":[6],"due":[7],"to":[8,29,40,112,162],"crop":[9,57,203],"spectral":[10],"similarity":[11],"and":[12,44,66,85,122,138,144,158,191],"complex":[13],"agricultural":[14],"landscapes.":[15],"This":[16],"study":[17],"proposes":[18],"a":[19,196],"novel":[20],"framework":[21],"integrating":[22],"object-oriented":[23,37,188],"multiscale":[24,38,189],"segmentation":[25,39,53,190],"with":[26,51],"deep":[27,79,193],"learning":[28,80,194],"enhance":[30],"identification.":[33],"Our":[34],"method":[35],"leverages":[36],"optimize":[41],"image":[42],"annotation":[43],"feature":[45],"representation,":[46],"which":[47,95],"is":[48],"then":[49],"integrated":[50],"semantic":[52],"networks":[54],"for":[55,109,200],"precise":[56],"classification.":[58],"Using":[59],"24":[60],"scenes":[61],"of":[62,93,102,125,155,187],"Sentinel-2":[63],"A":[64],"imagery":[65],"7280":[67],"field":[68],"observation":[69],"points":[70],"in":[71,141,147],"the":[72,91,114,148,168,184],"Guanzhong":[73,149],"Plain,":[74],"we":[75],"systematically":[76],"compared":[77,161],"three":[78],"architectures":[81],"(SegNet,":[82],"DeepLab":[83],"V3+,":[84],"UNet).":[86],"The":[87,104,151],"results":[88],"initially":[89],"demonstrated":[90],"superiority":[92],"UNet,":[94,113],"achieved":[96,117],"best":[97],"identification":[100,120],"accuracies":[101],"86.04%.":[103],"corresponding":[105],"F1-scores":[106],"were":[107],"90.66%":[108],"UNet.":[110],"Compared":[111],"UNet-transformer":[115],"model":[116],"5.12%":[118],"higher":[119,124],"accuracy":[121],"1.94%":[123],"F1-score.":[126],"UNet-Transformation":[127],"based":[128],"identified":[129],"areas":[132],"are":[133],"7935.94":[134],"km\u00b2,":[135,137],"7670.15":[136],"8695.18":[139],"km\u00b2":[140],"2018,":[142],"2020,":[143],"2023":[145],"years":[146],"plain.":[150],"obtained":[152],"TA":[153],"values":[154],"94.87%,":[156],"92.44%,":[157],"98.21%":[159],"when":[160],"government":[163],"statistical":[164],"data,":[165],"indicating":[166],"that":[167,183],"map":[171],"produced":[172],"at":[173],"10":[174],"m":[175],"resolution":[176],"was":[177],"reliable":[178],"overall.":[179],"These":[180],"findings":[181],"demonstrate":[182],"proposed":[185],"integration":[186],"advanced":[192],"offers":[195],"robust":[197],"practical":[198],"solution":[199],"large-scale,":[201],"mapping.":[204]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-11-06T00:00:00"}
