{"id":"https://openalex.org/W4312567254","doi":"https://doi.org/10.1109/igarss46834.2022.9883755","title":"Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series","display_name":"Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312567254","doi":"https://doi.org/10.1109/igarss46834.2022.9883755"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883755","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5025319637","display_name":"James Brinkhoff","orcid":"https://orcid.org/0000-0002-0721-2458"},"institutions":[{"id":"https://openalex.org/I90745801","display_name":"University of New England","ror":"https://ror.org/04r659a56","country_code":"AU","type":"education","lineage":["https://openalex.org/I90745801"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"James Brinkhoff","raw_affiliation_strings":["Applied Agricultural Remote Sensing Centre, University of New England,Armidale,NSW,Australia","Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Agricultural Remote Sensing Centre, University of New England,Armidale,NSW,Australia","institution_ids":["https://openalex.org/I90745801"]},{"raw_affiliation_string":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW, Australia","institution_ids":["https://openalex.org/I90745801"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5025319637"],"corresponding_institution_ids":["https://openalex.org/I90745801"],"apc_list":null,"apc_paid":null,"fwci":3.0294,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.91034644,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5854","last_page":"5857"},"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":0.991100013256073,"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/T10616","display_name":"Smart Agriculture and AI","score":0.9891999959945679,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/growing-season","display_name":"Growing season","score":0.6249909400939941},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5479079484939575},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45635583996772766},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.35563480854034424},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.347586452960968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3356572389602661},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.3255856931209564},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3249224126338959},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3182384967803955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31677699089050293},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.15046530961990356},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11004608869552612},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.10313400626182556}],"concepts":[{"id":"https://openalex.org/C137660486","wikidata":"https://www.wikidata.org/wiki/Q732240","display_name":"Growing season","level":2,"score":0.6249909400939941},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5479079484939575},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45635583996772766},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35563480854034424},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.347586452960968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3356572389602661},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3255856931209564},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3249224126338959},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3182384967803955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31677699089050293},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.15046530961990356},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11004608869552612},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.10313400626182556},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883755","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:rune.une.edu.au:1959.11/54683","is_oa":false,"landing_page_url":"https://hdl.handle.net/1959.11/54683","pdf_url":null,"source":{"id":"https://openalex.org/S7407055448","display_name":"RUNE (Research UNE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Publication"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.41999998688697815,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1974760592","https://openalex.org/W1995725830","https://openalex.org/W2030508143","https://openalex.org/W2468363661","https://openalex.org/W2725897987","https://openalex.org/W2963391824","https://openalex.org/W3000309135","https://openalex.org/W3177435843","https://openalex.org/W3215923092"],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2168523118","https://openalex.org/W2073639911","https://openalex.org/W2043988397"],"abstract_inverted_index":{"Regional":[0],"maps":[1,26,43,85,108],"of":[2,27,79,84,90],"rice":[3,30,58],"fields":[4,31],"provided":[5],"early":[6,94],"in":[7,32,97],"each":[8,105],"growing":[9,99],"season":[10],"facilitate":[11],"production":[12],"estimates,":[13],"planning":[14],"around":[15],"harvest":[16,49,63],"logistics,":[17],"marketing":[18],"and":[19,47,137],"targeted":[20],"agronomic":[21],"recommendations.":[22],"This":[23],"work":[24],"develops":[25],"all":[28],"irrigated":[29],"New":[33],"South":[34],"Wales,":[35],"Australia.":[36],"Classification":[37],"models":[38],"were":[39,53,86,130],"trained":[40],"on":[41],"reference":[42,57,128],"from":[44,60,76,93],"the":[45,61,98,127,134],"2019":[46],"2020":[48],"seasons.":[50],"Model":[51],"predictions":[52],"tested":[54],"against":[55,113,126],"a":[56],"map":[59,129],"2021":[62,119],"season,":[64,100],"covering":[65],"60,000":[66],"km2.":[67],"The":[68,107],"random":[69],"forest":[70],"algorithm":[71],"was":[72],"used,":[73],"with":[74,101],"features":[75],"aggregated":[77],"time-series":[78],"Sentinel-2":[80],"imagery.":[81],"A":[82],"sequence":[83],"generated":[87],"at":[88,104,116],"intervals":[89],"15":[91],"days,":[92],"to":[95],"late":[96],"accuracy":[102,112],"assessed":[103],"time.":[106],"achieved":[109],"95%":[110],"overall":[111],"point":[114],"samples":[115],"16":[117,136],"January":[118,139],"($\\approx":[120],"80$days":[121],"after":[122],"sowing).":[123],"Pixel-based":[124],"F1-scores":[125],"above":[131],"80%":[132],"for":[133],"1,":[135],"31":[138],"classified":[140],"maps.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-10-10T00:00:00"}