{"id":"https://openalex.org/W2548623469","doi":"https://doi.org/10.1109/igarss.2016.7730647","title":"Evaluation of Landsat 8 time series image stacks for predicitng yield and yield components of winter wheat","display_name":"Evaluation of Landsat 8 time series image stacks for predicitng yield and yield components of winter wheat","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2548623469","doi":"https://doi.org/10.1109/igarss.2016.7730647","mag":"2548623469"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7730647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5078583513","display_name":"Renzhong Song","orcid":null},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Renzhong Song","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613252","display_name":"Tao Cheng","orcid":"https://orcid.org/0000-0002-4184-0730"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Cheng","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100635496","display_name":"Xia Yao","orcid":"https://orcid.org/0000-0002-1093-1736"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Yao","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603229","display_name":"Yongchao Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongchao Tian","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100434309","display_name":"Yan Zhu","orcid":"https://orcid.org/0000-0002-1884-2404"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhu","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017022122","display_name":"Weixing Cao","orcid":"https://orcid.org/0000-0003-2622-7986"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weixing Cao","raw_affiliation_strings":["Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I119454577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5078583513"],"corresponding_institution_ids":["https://openalex.org/I119454577"],"apc_list":null,"apc_paid":null,"fwci":0.3004,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.69878237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6300","last_page":"6303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9998000264167786,"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":0.9998000264167786,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.8241317272186279},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.7822239398956299},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.6514936685562134},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.6081371307373047},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5522284507751465},{"id":"https://openalex.org/keywords/stepwise-regression","display_name":"Stepwise regression","score":0.5070748329162598},{"id":"https://openalex.org/keywords/growing-season","display_name":"Growing season","score":0.49848270416259766},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.49687817692756653},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4905669093132019},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.45512789487838745},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4526020884513855},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39852142333984375},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3808163106441498},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3351498246192932},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.2508739233016968},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.23441675305366516},{"id":"https://openalex.org/keywords/leaf-area-index","display_name":"Leaf area index","score":0.11903244256973267},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07060056924819946}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.8241317272186279},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.7822239398956299},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.6514936685562134},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.6081371307373047},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5522284507751465},{"id":"https://openalex.org/C170964787","wikidata":"https://www.wikidata.org/wiki/Q7611170","display_name":"Stepwise regression","level":2,"score":0.5070748329162598},{"id":"https://openalex.org/C137660486","wikidata":"https://www.wikidata.org/wiki/Q732240","display_name":"Growing season","level":2,"score":0.49848270416259766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49687817692756653},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4905669093132019},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.45512789487838745},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4526020884513855},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39852142333984375},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3808163106441498},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3351498246192932},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.2508739233016968},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.23441675305366516},{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.11903244256973267},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07060056924819946},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7730647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1566520081","https://openalex.org/W1607241334","https://openalex.org/W2021662310","https://openalex.org/W2056251274","https://openalex.org/W2059488281","https://openalex.org/W2070559755"],"related_works":["https://openalex.org/W3215700490","https://openalex.org/W2393341384","https://openalex.org/W1529468313","https://openalex.org/W2375721435","https://openalex.org/W2364456402","https://openalex.org/W4285406654","https://openalex.org/W2738033194","https://openalex.org/W2062105804","https://openalex.org/W4290879003","https://openalex.org/W247449116"],"abstract_inverted_index":{"The":[0,97,181],"accurate":[1],"prediction":[2,22,198],"of":[3,7,50,62,76,88,94,117,145,161,183,199],"crop":[4,20],"yield":[5,55,68,70,99,101,119,147,166,185,201],"is":[6],"great":[8],"importance":[9],"to":[10,47,54,66,194],"regional":[11],"production":[12,21],"and":[13,69,100,114,134],"food":[14],"security.":[15],"Many":[16],"empirical":[17],"models":[18],"for":[19,192],"are":[23,190],"based":[24],"on":[25],"vegetation":[26,32],"indices":[27],"(VI)":[28],"such":[29],"as":[30],"normalized":[31],"index":[33],"(NDVI)":[34],"or":[35],"simple":[36,127],"ratio":[37],"(SR)":[38],"in":[39,91],"specific":[40,184],"growing":[41,52,64,86],"period,":[42],"with":[43,72,155,202],"little":[44],"attention":[45],"paid":[46],"the":[48,60,85,142,151,164,168,197],"sensitivity":[49,61],"different":[51,63],"stages":[53],"prediction.":[56],"This":[57],"study":[58],"investigates":[59],"seasons":[65],"wheat":[67,90,146,200],"components":[71,102,186],"time":[73,203],"series":[74,204],"stacks":[75],"Landsat":[77,175,188],"8":[78],"Operational":[79],"Land":[80],"Imager":[81],"(OLI)":[82],"images":[83],"over":[84],"season":[87],"winter":[89,118],"a":[92,156],"farm":[93],"Jiangsu,":[95],"China.":[96],"final":[98],"including":[103],"panicles":[104],"per":[105,112],"m":[106],"<sup":[107,158],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[108,159],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[109,160],",":[110],"grains":[111],"panicle":[113],"1000-grains":[115,169],"weight":[116,170],"were":[120],"estimated":[121,173],"using":[122,150],"three":[123,165],"regression":[124,133,135,152],"methods":[125],"namely":[126],"linear":[128,132],"regression,":[129],"stepwise":[130],"multiple":[131],"tree.":[136],"Our":[137],"experimental":[138],"results":[139],"demonstrated":[140],"that":[141],"best":[143,172],"estimation":[144],"was":[148,171],"produced":[149],"tree":[153],"method":[154],"R":[157],"0.87.":[162],"Among":[163],"components,":[167],"from":[174,187],"data":[176,189],"acquired":[177],"at":[178],"early":[179],"stages.":[180],"estimations":[182],"useful":[191],"us":[193],"better":[195],"understand":[196],"satellite":[205],"image":[206],"data.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}