{"id":"https://openalex.org/W2100630813","doi":"https://doi.org/10.1109/igarss.2009.5417391","title":"Regional yield prediction of winter wheat based on retrieval of Leaf area index by remote sensing technology","display_name":"Regional yield prediction of winter wheat based on retrieval of Leaf area index by remote sensing technology","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2100630813","doi":"https://doi.org/10.1109/igarss.2009.5417391","mag":"2100630813"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2009.5417391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2009.5417391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5112108215","display_name":"Jianqiang Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210138501","display_name":"Chinese Academy of Agricultural Sciences","ror":"https://ror.org/0313jb750","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianqiang Ren","raw_affiliation_strings":["Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210108914","https://openalex.org/I4210138501"]},{"raw_affiliation_string":"Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008756","display_name":"Zhongxin Chen","orcid":"https://orcid.org/0009-0004-2148-9367"},"institutions":[{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210138501","display_name":"Chinese Academy of Agricultural Sciences","ror":"https://ror.org/0313jb750","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongxin Chen","raw_affiliation_strings":["Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210108914","https://openalex.org/I4210138501"]},{"raw_affiliation_string":"Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036109302","display_name":"Xiaomei Yang","orcid":"https://orcid.org/0000-0003-1643-8480"},"institutions":[{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomei Yang","raw_affiliation_strings":["The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210160793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022918104","display_name":"Xingren Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingren Liu","raw_affiliation_strings":["Synthesis Research Center of Chinese Ecosystem Research Network & Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Synthesis Research Center of Chinese Ecosystem Research Network & Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210160793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103668758","display_name":"Qingbo Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138501","display_name":"Chinese Academy of Agricultural Sciences","ror":"https://ror.org/0313jb750","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingbo Zhou","raw_affiliation_strings":["Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210108914","https://openalex.org/I4210138501"]},{"raw_affiliation_string":"Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112108215"],"corresponding_institution_ids":["https://openalex.org/I4210108914","https://openalex.org/I4210138501"],"apc_list":null,"apc_paid":null,"fwci":0.3155,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.66871715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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":0.9998999834060669,"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":0.9998999834060669,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9987999796867371,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.7723065614700317},{"id":"https://openalex.org/keywords/leaf-area-index","display_name":"Leaf area index","score":0.6947754621505737},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6840472221374512},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5775107145309448},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.5746437311172485},{"id":"https://openalex.org/keywords/winter-wheat","display_name":"Winter wheat","score":0.5637609958648682},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.47336336970329285},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4462767243385315},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4246675968170166},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2695598900318146},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.24435511231422424},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.20708739757537842},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1385786235332489}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.7723065614700317},{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.6947754621505737},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6840472221374512},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5775107145309448},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.5746437311172485},{"id":"https://openalex.org/C3018661444","wikidata":"https://www.wikidata.org/wiki/Q6977574","display_name":"Winter wheat","level":2,"score":0.5637609958648682},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.47336336970329285},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4462767243385315},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4246675968170166},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2695598900318146},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.24435511231422424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.20708739757537842},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1385786235332489},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2009.5417391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2009.5417391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W48839202","https://openalex.org/W1498729378","https://openalex.org/W1967720470","https://openalex.org/W1971429204","https://openalex.org/W1971883950","https://openalex.org/W1992226275","https://openalex.org/W2018636632","https://openalex.org/W2041371820","https://openalex.org/W2044574395","https://openalex.org/W2058709694","https://openalex.org/W2143494625","https://openalex.org/W2150140969"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W4385533602","https://openalex.org/W3189212133","https://openalex.org/W4382239404","https://openalex.org/W2526455285","https://openalex.org/W4382519838","https://openalex.org/W3142012701","https://openalex.org/W4289942978"],"abstract_inverted_index":{"In":[0,30],"this":[1],"paper,":[2],"the":[3,9,34,52,65,89,110,122],"authors":[4,90,123],"had":[5],"a":[6,163],"research":[7],"on":[8,125],"winter":[10,98,132,170],"wheat":[11,99,133,171],"yield":[12,42,96,107,156,172],"estimation":[13,108],"using":[14],"retrieved":[15,126],"LAI":[16,76,80,94,127],"from":[17,128],"remote":[18],"sensing":[19],"in":[20,24,27],"typical":[21],"11":[22],"counties":[23],"Huanghuaihai":[25],"Plain":[26],"North":[28],"China.":[29],"order":[31],"to":[32,50,55,72,77,130],"improve":[33],"quality":[35],"of":[36,41,58,97,106,113,177],"data":[37],"and":[38,61,95,115,144],"reduce":[39,56],"error":[40,141],"estimation,":[43],"Savitzky-Golay":[44],"filter":[45],"(S-G":[46],"filter)":[47],"was":[48,70,118,142,147],"used":[49,71],"smooth":[51],"NDVI":[53],"series":[54],"influences":[57],"cloud":[59],"contamination":[60],"abnormal":[62],"data.":[63,160],"At":[64],"same":[66],"time,":[67],"Gaussian":[68],"model":[69,117],"simulate":[73],"daily":[74],"crop":[75],"get":[78,169],"average":[79,87,139],"at":[81,100],"each":[82],"growth":[83,102],"stage.":[84,103],"Using":[85],"these":[86],"LAI,":[88],"established":[91],"relationships":[92],"between":[93],"main":[101],"After":[104],"optimization":[105],"model,":[109],"best":[111,116],"period":[112],"time":[114],"selected":[119],"out.":[120],"Finally,":[121],"depended":[124],"MODIS-NDVI":[129],"estimate":[131],"yield.":[134],"The":[135],"results":[136],"showed":[137],"that":[138,145,165],"relative":[140],"1.21%":[143],"RMSE":[146],"257.33":[148],"kg":[149],"ha":[150],"<sup":[151],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[152],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u22121</sup>":[153],"comparing":[154],"predicted":[155],"with":[157],"ground":[158],"truth":[159],"We":[161],"draw":[162],"conclusion":[164],"we":[166],"could":[167],"accurately":[168],"about":[173],"20\u201330":[174],"days":[175],"ahead":[176],"harvest":[178],"time.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
