{"id":"https://openalex.org/W4415747934","doi":"https://doi.org/10.1109/lgrs.2025.3627552","title":"Temporal-Aware Spatial Interaction Transformer for Crop Yield Prediction Based on Multisensor Satellite Image Time Series","display_name":"Temporal-Aware Spatial Interaction Transformer for Crop Yield Prediction Based on Multisensor Satellite Image Time Series","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415747934","doi":"https://doi.org/10.1109/lgrs.2025.3627552"},"language":null,"primary_location":{"id":"doi:10.1109/lgrs.2025.3627552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3627552","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5065068394","display_name":"Tengfei Gong","orcid":"https://orcid.org/0000-0002-8465-0144"},"institutions":[{"id":"https://openalex.org/I4210149102","display_name":"Sanya University","ror":"https://ror.org/04fa2qd52","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149102"]},{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tengfei Gong","raw_affiliation_strings":["Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China"],"affiliations":[{"raw_affiliation_string":"Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China","institution_ids":["https://openalex.org/I4210149102","https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112881591","display_name":"Xinchao Zhu","orcid":"https://orcid.org/0000-0003-4538-5787"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]},{"id":"https://openalex.org/I4210149102","display_name":"Sanya University","ror":"https://ror.org/04fa2qd52","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149102"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinchao Zhu","raw_affiliation_strings":["Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China"],"affiliations":[{"raw_affiliation_string":"Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China","institution_ids":["https://openalex.org/I4210149102","https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003775716","display_name":"Yaxiong Chen","orcid":"https://orcid.org/0000-0002-2903-6723"},"institutions":[{"id":"https://openalex.org/I4210149102","display_name":"Sanya University","ror":"https://ror.org/04fa2qd52","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149102"]},{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaxiong Chen","raw_affiliation_strings":["Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China"],"affiliations":[{"raw_affiliation_string":"Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China","institution_ids":["https://openalex.org/I4210149102","https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011707621","display_name":"Shengwu Xiong","orcid":"https://orcid.org/0000-0002-4006-7029"},"institutions":[{"id":"https://openalex.org/I4210100789","display_name":"Wuhan College","ror":"https://ror.org/01dashf18","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210100789"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengwu Xiong","raw_affiliation_strings":["Interdisciplinary Artificial Intelligence Research Institute, Wuhan College, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Interdisciplinary Artificial Intelligence Research Institute, Wuhan College, Wuhan, China","institution_ids":["https://openalex.org/I4210100789"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065068394"],"corresponding_institution_ids":["https://openalex.org/I196699116","https://openalex.org/I4210149102"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32292892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.7383000254631042,"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":0.7383000254631042,"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":0.1678999960422516,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.009100000374019146,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5192999839782715},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.47839999198913574},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4700999855995178},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4422999918460846},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.3837999999523163},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3743000030517578},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.367900013923645},{"id":"https://openalex.org/keywords/spatial-variability","display_name":"Spatial variability","score":0.3634999990463257},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.34459999203681946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5527999997138977},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.47839999198913574},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.45660001039505005},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C94747663","wikidata":"https://www.wikidata.org/wiki/Q7574086","display_name":"Spatial variability","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3386000096797943},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31619998812675476},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C158709400","wikidata":"https://www.wikidata.org/wiki/Q3578586","display_name":"Spatial ecology","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2985301230","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite image","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3627552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3627552","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2991004173","https://openalex.org/W3111700100","https://openalex.org/W3131500599","https://openalex.org/W3185151088","https://openalex.org/W3213239025","https://openalex.org/W4220917101","https://openalex.org/W4385245566","https://openalex.org/W4386065702","https://openalex.org/W4387801818","https://openalex.org/W4390874672","https://openalex.org/W4394593132","https://openalex.org/W4399554035","https://openalex.org/W4405301470"],"related_works":[],"abstract_inverted_index":{"Crop":[0],"yield":[1,51,98],"prediction":[2,99],"is":[3,69,82],"crucial":[4],"for":[5,27,96],"agricultural":[6],"decision":[7],"making.":[8],"Satellite":[9],"Image":[10],"Time":[11],"Series":[12],"(SITS)":[13],"data,":[14,188],"which":[15,106],"provide":[16],"continuous":[17],"temporal":[18,126],"observations":[19],"of":[20,37,48,61,76,144,196,199,201,207],"vegetation":[21],"changes,":[22],"have":[23,32],"become":[24],"a":[25,111,173,190],"standard":[26],"accurate":[28],"prediction.":[29],"Recent":[30],"studies":[31],"shown":[33],"that":[34,118,140,165],"the":[35,46,74,90,142,151,159,166],"integration":[36],"multimodal":[38,62],"data":[39],"from":[40,128],"different":[41,67,129,148],"satellite":[42,102,130],"sensors":[43],"significantly":[44],"enhances":[45],"performance":[47],"SITS-based":[49],"crop":[50,80,97,120],"predictions.":[52],"However,":[53],"existing":[54,212],"methods":[55],"often":[56],"rely":[57],"on":[58,79,158],"simplistic":[59],"combinations":[60],"data.":[63],"Temporal":[64,112],"inconsistency":[65],"between":[66,147,153],"modals":[68],"not":[70],"considered.":[71],"In":[72],"addition,":[73],"influence":[75],"spatial":[77,145],"interaction":[78,152],"growth":[81,121],"ignored.":[83],"To":[84],"address":[85],"this":[86],"issue,":[87],"we":[88],"propose":[89],"TASI-Transformer":[91],"(Temporal-Aware":[92],"Spatial":[93,134],"Interaction":[94,137],"Transformer)":[95],"using":[100,185],"multisensor":[101],"image":[103],"time":[104,131],"series,":[105],"incorporates":[107,119],"two":[108],"innovative":[109],"modules:":[110],"Enhanced":[113,135],"Position":[114],"Encoding":[115],"(TEPE)":[116],"module":[117,139],"dates":[122],"to":[123],"extract":[124],"unique":[125],"information":[127],"series.":[132],"A":[133],"Multimodal":[136],"(SEMI)":[138],"learns":[141],"impact":[143],"relationships":[146],"regions":[149],"and":[150,161,189,203],"multiple":[154],"modals.":[155],"Experimental":[156],"results":[157],"SICKLE":[160,184],"CROPNET":[162],"datasets":[163],"demonstrate":[164],"proposed":[167],"method":[168],"achieves":[169],"state-of-the-art":[170],"performance,":[171],"with":[172],"Mean":[174,192],"Absolute":[175],"Percentage":[176],"Error":[177,194],"(MAPE)":[178],"as":[179,181],"low":[180],"26.99%":[182],"in":[183,209],"actual":[186],"season":[187],"Root":[191],"Squared":[193],"(RMSE)":[195],"6.85,":[197],"Coefficient":[198],"Determination(R\u00b2)":[200],"0.51,":[202],"Pearson":[204],"Correlation":[205],"(CORR)":[206],"0.71":[208],"CROPNET,":[210],"outperforming":[211],"methods.":[213]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-31T00:00:00"}
