{"id":"https://openalex.org/W3205108124","doi":"https://doi.org/10.1109/igarss47720.2021.9554874","title":"Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data","display_name":"Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205108124","doi":"https://doi.org/10.1109/igarss47720.2021.9554874","mag":"3205108124"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5043274278","display_name":"Chunquan Fan","orcid":"https://orcid.org/0000-0001-6778-1129"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunquan Fan","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671875","display_name":"Binbin He","orcid":"https://orcid.org/0000-0002-0668-6520"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binbin He","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112395785","display_name":"Kong Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Kong","raw_affiliation_strings":["Institute of Spacecraft System Engineering (ISSE), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Spacecraft System Engineering (ISSE), Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102979141","display_name":"Hao Xu","orcid":"https://orcid.org/0000-0002-4248-8310"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Xu","raw_affiliation_strings":["Institute of Spacecraft System Engineering (ISSE), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Spacecraft System Engineering (ISSE), Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381999","display_name":"Qiang Zhang","orcid":"https://orcid.org/0000-0003-3776-9799"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Zhang","raw_affiliation_strings":["Institute of Spacecraft System Engineering (ISSE), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Spacecraft System Engineering (ISSE), Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065238728","display_name":"Xingwen Quan","orcid":"https://orcid.org/0000-0001-5344-1801"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingwen Quan","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2257,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56049711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"94","issue":null,"first_page":"1222","last_page":"1225"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9998999834060669,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9994000196456909,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6700330972671509},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.6447781324386597},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5851743221282959},{"id":"https://openalex.org/keywords/water-content","display_name":"Water content","score":0.57532799243927},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5378372669219971},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.531322181224823},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.48559731245040894},{"id":"https://openalex.org/keywords/empirical-modelling","display_name":"Empirical modelling","score":0.459597647190094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2801893949508667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2692705988883972},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23395273089408875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22298353910446167},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12337478995323181},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1213790774345398},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.08999192714691162}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6700330972671509},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.6447781324386597},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5851743221282959},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.57532799243927},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5378372669219971},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.531322181224823},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.48559731245040894},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.459597647190094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2801893949508667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2692705988883972},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23395273089408875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22298353910446167},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12337478995323181},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1213790774345398},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.08999192714691162},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G7048682405","display_name":null,"funder_award_id":"U20A2090,41801272","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W37217007","https://openalex.org/W429766147","https://openalex.org/W871842247","https://openalex.org/W1964217023","https://openalex.org/W2034660849","https://openalex.org/W2038484977","https://openalex.org/W2057387551","https://openalex.org/W2077509829","https://openalex.org/W2101234009","https://openalex.org/W2136102185","https://openalex.org/W2143426320","https://openalex.org/W2167328239","https://openalex.org/W2171210136","https://openalex.org/W2212426059","https://openalex.org/W2345254164","https://openalex.org/W2487770199","https://openalex.org/W2594368475","https://openalex.org/W2782817750","https://openalex.org/W2884851559","https://openalex.org/W2911964244","https://openalex.org/W3022895944","https://openalex.org/W6601519674","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W2898051319","https://openalex.org/W4312126988","https://openalex.org/W2575795810","https://openalex.org/W2074982533","https://openalex.org/W4400591661","https://openalex.org/W2945273126","https://openalex.org/W3179185381","https://openalex.org/W3011918200","https://openalex.org/W2803341399"],"abstract_inverted_index":{"Dead":[0],"fuel":[1,20,27],"moisture":[2],"content":[3],"(DFMC)":[4],"is":[5,21,65,159],"of":[6,75,92,137],"important":[7],"significance":[8],"for":[9,107,115],"estimating":[10],"and":[11,35,45,110,131],"predicting":[12,95,146],"forest":[13,104,122],"wildfire":[14],"risk,":[15],"in":[16],"which":[17],"1-h":[18,93,147],"dead":[19],"most":[22,67],"critical":[23],"as":[24],"the":[25,66,72,78,90,108],"easiest":[26],"to":[28,70,81],"ignite.":[29],"Current":[30],"methodologies":[31],"based":[32],"on":[33,40],"empirical":[34],"physical":[36],"models":[37],"rely":[38],"heavily":[39],"meteorological":[41],"data":[42,51,69,158],"from":[43,99,155],"uneven":[44],"sparse":[46],"stations.":[47],"In":[48],"contrast,":[49],"satellite":[50],"become":[52],"a":[53,124],"better":[54],"choice":[55],"since":[56],"its":[57,82],"continuous":[58],"surface":[59],"observations.":[60],"Of":[61],"all":[62],"satellites,":[63],"Himawari-8":[64],"appropriate":[68],"meet":[71],"rapid":[73],"changes":[74],"DFMC":[76,94,148],"throughout":[77],"day":[79],"owing":[80],"high":[83],"time":[84],"resolution.":[85],"Thus,":[86],"this":[87],"study":[88],"explored":[89],"application":[91],"using":[96,152],"machine":[97,153],"learning":[98,154],"Himawari":[100,156],"-8":[101,157],"data.":[102],"Random":[103],"was":[105,113],"selected":[106],"prediction":[109],"linear":[111,138],"regression":[112,139],"used":[114],"comparison.":[116],"The":[117,142],"results":[118],"showed":[119],"that":[120,136,145],"random":[121],"has":[123],"satisfactory":[125],"performance":[126],"with":[127],"higher":[128],"R2":[129],"(0.53)":[130],"lower":[132],"RMSE":[133],"(3.15%)":[134],"than":[135],"(R2=0.21,":[140],"RMSE=5.47%).":[141],"research":[143],"suggested":[144],"at":[149],"regional":[150],"scales":[151],"promising.":[160]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}