{"id":"https://openalex.org/W2901340191","doi":"https://doi.org/10.1109/igarss.2018.8517833","title":"Assessment of Predictive Ability of Starfm Based on Different Modis-Landsat Image Pair Date","display_name":"Assessment of Predictive Ability of Starfm Based on Different Modis-Landsat Image Pair Date","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2901340191","doi":"https://doi.org/10.1109/igarss.2018.8517833","mag":"2901340191"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8517833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8517833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 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/A5069103302","display_name":"Donghui Xie","orcid":"https://orcid.org/0000-0003-3923-6056"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Donghui Xie","raw_affiliation_strings":["Faculty of Geographical Science, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Geographical Science, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100362401","display_name":"Feng Gao","orcid":"https://orcid.org/0000-0002-1865-2846"},"institutions":[{"id":"https://openalex.org/I1336096307","display_name":"United States Department of Agriculture","ror":"https://ror.org/01na82s61","country_code":"US","type":"government","lineage":["https://openalex.org/I1336096307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Gao","raw_affiliation_strings":["U. S. Department of Agriculture, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA"],"affiliations":[{"raw_affiliation_string":"U. S. Department of Agriculture, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA","institution_ids":["https://openalex.org/I1336096307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022044383","display_name":"Linyuan Li","orcid":"https://orcid.org/0000-0002-8237-7076"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linyuan Li","raw_affiliation_strings":["Faculty of Geographical Science, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Geographical Science, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069103302"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.3812,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67585111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"7247","last_page":"7250"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9934999942779541,"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"}},{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.9842000007629395,"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/remote-sensing","display_name":"Remote sensing","score":0.7159285545349121},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.6909605860710144},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5638031959533691},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5530697107315063},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5286762118339539},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4638279676437378},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.42365872859954834},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31395646929740906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3049420416355133},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2608056664466858},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1860966682434082},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13585492968559265}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.7159285545349121},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.6909605860710144},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5638031959533691},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5530697107315063},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5286762118339539},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4638279676437378},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.42365872859954834},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31395646929740906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3049420416355133},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2608056664466858},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1860966682434082},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13585492968559265},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8517833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8517833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2023015896","https://openalex.org/W2040638734","https://openalex.org/W2056811372","https://openalex.org/W2077182785","https://openalex.org/W2088603520","https://openalex.org/W2110832913","https://openalex.org/W2264526004"],"related_works":["https://openalex.org/W2391745328","https://openalex.org/W4214649316","https://openalex.org/W2132659060","https://openalex.org/W2554790198","https://openalex.org/W2031992971","https://openalex.org/W2788731446","https://openalex.org/W2095903272","https://openalex.org/W4390606538","https://openalex.org/W2204403038","https://openalex.org/W3214791684"],"abstract_inverted_index":{"Accurate":[0,247],"spatiotemporal":[1],"information":[2],"about":[3],"crop":[4,13,214,259,263],"progress":[5,17],"during":[6],"the":[7,42,74,103,111,132,141,175,183,192,212],"growing":[8],"season":[9],"is":[10,71,201,245],"critical":[11],"for":[12,96],"yield":[14,264],"estimation.":[15],"Crop":[16],"monitoring":[18,261],"at":[19,44,198,251,266],"field":[20,267],"scale":[21],"requires":[22],"high":[23],"resolution":[24,253],"remote":[25,54],"sensing":[26,34,55],"data":[27,35,80,91,106,133,144,184,221,233,250],"in":[28,87,123,258],"both":[29],"time":[30],"and":[31,59,64,78,83,174,217,262],"space.":[32],"Remote":[33],"from":[36,57],"a":[37],"single":[38],"sensor":[39],"cannot":[40],"satisfy":[41],"requirement":[43],"present.":[45],"Data":[46],"fusion":[47,81,92,107,134,185,222,234],"approach":[48],"has":[49,84],"been":[50,85,148],"developed":[51],"to":[52,130,154,181],"fuse":[53],"imagery":[56],"Landsat":[58,143,252],"MODIS":[60],"instruments.":[61],"The":[62,90,187],"Spatial":[63],"Temporal":[65],"Adaptive":[66],"Reflectance":[67],"Fusion":[68],"Model":[69],"(STARFM)":[70],"one":[72],"of":[73,105,194],"most":[75],"popular":[76],"spatial":[77],"temporal":[79],"algorithms":[82],"applied":[86],"many":[88,97],"applications.":[89],"accuracy":[93,104,208],"was":[94],"evaluated":[95],"sites.":[98],"Previous":[99],"studies":[100],"found":[101],"that":[102,145,191,232],"results":[108,189,235],"depended":[109],"on":[110,140],"pair":[112,128,151,197,244],"images":[113,121,129,152,196],"used.":[114],"In":[115],"this":[116],"study,":[117],"several":[118],"Landsat-8":[119],"reflectance":[120],"(path28/row31)":[122],"2015":[124],"were":[125,137,179],"selected":[126],"as":[127,150,209],"evaluate":[131],"accuracy.":[135],"Results":[136],"assessed":[138],"based":[139],"observed":[142],"have":[146,205],"not":[147],"used":[149],"due":[153],"partial":[155],"cloud":[156],"coverage":[157],"or":[158],"image":[159,227,243],"gaps.":[160],"Several":[161],"statistic":[162],"metrics,":[163],"including":[164],"average":[165],"absolute":[166],"difference,":[167],"root":[168],"mean":[169],"square":[170],"error,":[171],"correlation":[172],"coefficient,":[173],"spectral":[176],"angle":[177],"mapper,":[178],"calculated":[180],"assess":[182],"results.":[186],"initial":[188],"show":[190],"predictability":[193],"each":[195],"different":[199,213,220],"dates":[200,204],"different.":[202],"Closer":[203],"better":[206],"prediction":[207],"expected.":[210],"Interestingly,":[211],"type":[215],"(corn":[216],"soybeans)":[218],"shows":[219],"accuracies":[223],"even":[224],"using":[225],"same":[226],"pair.":[228],"This":[229],"study":[230],"suggests":[231],"could":[236],"be":[237],"further":[238],"improved":[239],"if":[240],"an":[241],"appropriate":[242],"selected.":[246],"dense":[248],"time-series":[249],"will":[254],"enhance":[255],"our":[256],"ability":[257],"condition":[260],"estimation":[265],"scale.":[268]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}