{"id":"https://openalex.org/W2546467621","doi":"https://doi.org/10.1109/igarss.2016.7730651","title":"Using historical NDVI time series to classify crops at 30m spatial resolution: A case in Southeast Kansas","display_name":"Using historical NDVI time series to classify crops at 30m spatial resolution: A case in Southeast Kansas","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2546467621","doi":"https://doi.org/10.1109/igarss.2016.7730651","mag":"2546467621"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7730651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730651","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/A5015672094","display_name":"Pengyu Hao","orcid":"https://orcid.org/0000-0003-3711-6157"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengyu Hao","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100382402","display_name":"Li Wang","orcid":"https://orcid.org/0000-0002-2929-4255"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]},{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China","University of Maryland, College Park, MD, United States"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Maryland, College Park, MD, United States","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108648343","display_name":"Yulin Zhan","orcid":"https://orcid.org/0000-0001-5771-4168"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulin Zhan","raw_affiliation_strings":["The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110589832","display_name":"Zheng Niu","orcid":"https://orcid.org/0000-0001-5959-9351"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Niu","raw_affiliation_strings":["The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102141616","display_name":"Mingquan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingquan Wu","raw_affiliation_strings":["The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015672094"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.9012,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78815307,"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":"6316","last_page":"6319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9995999932289124,"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":0.9995999932289124,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9911999702453613,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.8683960437774658},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.6814817190170288},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6177171468734741},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.52217698097229},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5089478492736816},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.48813948035240173},{"id":"https://openalex.org/keywords/reference-data","display_name":"Reference data","score":0.46587345004081726},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.44317442178726196},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.34230202436447144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.311778724193573},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.28888773918151855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.26923611760139465},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23547613620758057},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19973522424697876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19422268867492676},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.07738006114959717},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07366865873336792}],"concepts":[{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.8683960437774658},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.6814817190170288},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6177171468734741},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.52217698097229},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5089478492736816},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.48813948035240173},{"id":"https://openalex.org/C60478076","wikidata":"https://www.wikidata.org/wiki/Q3036835","display_name":"Reference data","level":2,"score":0.46587345004081726},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.44317442178726196},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.34230202436447144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.311778724193573},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.28888773918151855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.26923611760139465},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23547613620758057},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19973522424697876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19422268867492676},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.07738006114959717},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07366865873336792},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7730651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730651","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":[{"score":0.6600000262260437,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2014555541","https://openalex.org/W2029342456","https://openalex.org/W2067215608","https://openalex.org/W2093378913","https://openalex.org/W2138973222","https://openalex.org/W2167594433","https://openalex.org/W2179952599","https://openalex.org/W2315501819","https://openalex.org/W2741004176","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W1980260791","https://openalex.org/W4385533602","https://openalex.org/W3189212133","https://openalex.org/W4382239404","https://openalex.org/W4382519838","https://openalex.org/W2803445926","https://openalex.org/W186073475"],"abstract_inverted_index":{"Most":[0],"crop":[1,40,105,184],"classification":[2,185],"work":[3],"use":[4,28],"the":[5,11,15,29,60,78,89,93,120,124,128,134,144,149,172],"ground":[6,16,65,187],"reference":[7,17,66,188],"data":[8,18,67,82,95,189],"to":[9,27,38,85,103,157],"training":[10],"classifier;":[12],"but":[13],"sometimes,":[14],"cannot":[19,190],"be":[20,179,191],"obtained.":[21],"In":[22,139],"this":[23,115,153,176],"paper,":[24],"we":[25,58],"tried":[26],"NDVI":[30,61,74,81,87],"time":[31,62,75],"series":[32,63],"obtained":[33,151],"during":[34],"2006":[35,69],"and":[36,70,133],"2013":[37,71],"classify":[39,104],"types":[41,106],"in":[42,53,123,152,175],"2014":[43],"at":[44,107],"30":[45,108],"m":[46,109],"spatial":[47],"resolution.":[48,110],"The":[49,111],"experiment":[50],"was":[51,101,131,137,155],"conducted":[52],"Southeast":[54],"Kansas,":[55],"USA.":[56],"Firstly,":[57],"extracted":[59],"using":[64,88],"between":[68,92],"from":[72],"MODIS":[73,86],"series.":[76],"Then,":[77],"composed":[79],"Landsat":[80],"were":[83],"transformed":[84],"linear":[90],"correlation":[91],"two":[94,141],"sets.":[96],"Next,":[97],"Random":[98],"Forest":[99],"(RF)":[100],"employed":[102],"result":[112,150],"showed":[113,147],"that":[114,148,158],"procedure":[116],"could":[117,178],"accurately":[118],"identify":[119],"major":[121],"crops":[122],"study":[125,145,154,177],"area":[126,146],"as":[127],"overall":[129],"accuracy":[130],"92.22%":[132],"Kappa":[135],"coefficient":[136],"0.8758.":[138],"addition,":[140],"subsets":[142],"of":[143,159],"similar":[156],"Crop":[160],"Data":[161],"Layer":[162],"(CDL)":[163],"provided":[164],"by":[165],"National":[166],"Agricultural":[167],"Statistics":[168],"Service":[169],"(NASS).":[170],"Thus,":[171],"method":[173],"proposed":[174],"an":[180],"alternative":[181],"way":[182],"for":[183],"when":[186],"acquired.":[192]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}