{"id":"https://openalex.org/W4200527363","doi":"https://doi.org/10.3390/rs13244976","title":"Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem","display_name":"Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem","publication_year":2021,"publication_date":"2021-12-07","ids":{"openalex":"https://openalex.org/W4200527363","doi":"https://doi.org/10.3390/rs13244976"},"language":"en","primary_location":{"id":"doi:10.3390/rs13244976","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13244976","pdf_url":"https://www.mdpi.com/2072-4292/13/24/4976/pdf?version=1639043362","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/24/4976/pdf?version=1639043362","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054138866","display_name":"Muhammad Sarfraz Khan","orcid":"https://orcid.org/0000-0003-0506-5180"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Sarfraz Khan","raw_affiliation_strings":["Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112724035","display_name":"Seung Bae Jeon","orcid":null},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seung Bae Jeon","raw_affiliation_strings":["Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057693482","display_name":"Myeong\u2010Hun Jeong","orcid":"https://orcid.org/0000-0003-4850-8121"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Myeong-Hun Jeong","raw_affiliation_strings":["Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Chosun University, 309 Pilmun-daero, Gwangju 61452, Korea","institution_ids":["https://openalex.org/I152238500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057693482"],"corresponding_institution_ids":["https://openalex.org/I152238500"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.5325,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.82095314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"13","issue":"24","first_page":"4976","last_page":"4976"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10266","display_name":"Plant Water Relations and Carbon Dynamics","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/T10266","display_name":"Plant Water Relations and Carbon Dynamics","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/T11594","display_name":"Tree-ring climate responses","score":0.9889000058174133,"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/T10360","display_name":"Fluid Dynamics and Turbulent Flows","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/eddy-covariance","display_name":"Eddy covariance","score":0.7489034533500671},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5697294473648071},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5507432818412781},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5271857976913452},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.5001165866851807},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4808667302131653},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4773012399673462},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4752093255519867},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45590537786483765},{"id":"https://openalex.org/keywords/sensible-heat","display_name":"Sensible heat","score":0.44143855571746826},{"id":"https://openalex.org/keywords/latent-heat","display_name":"Latent heat","score":0.4398178458213806},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.41098666191101074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3480398654937744},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3333576023578644},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3250408172607422},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3058297336101532},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.20707735419273376},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11603963375091553}],"concepts":[{"id":"https://openalex.org/C35187779","wikidata":"https://www.wikidata.org/wiki/Q5336709","display_name":"Eddy covariance","level":3,"score":0.7489034533500671},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5697294473648071},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5507432818412781},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5271857976913452},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.5001165866851807},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4808667302131653},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4773012399673462},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4752093255519867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45590537786483765},{"id":"https://openalex.org/C59242433","wikidata":"https://www.wikidata.org/wiki/Q1480581","display_name":"Sensible heat","level":2,"score":0.44143855571746826},{"id":"https://openalex.org/C58024561","wikidata":"https://www.wikidata.org/wiki/Q207721","display_name":"Latent heat","level":2,"score":0.4398178458213806},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.41098666191101074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3480398654937744},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3333576023578644},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3250408172607422},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3058297336101532},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.20707735419273376},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11603963375091553},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C110872660","wikidata":"https://www.wikidata.org/wiki/Q37813","display_name":"Ecosystem","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13244976","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13244976","pdf_url":"https://www.mdpi.com/2072-4292/13/24/4976/pdf?version=1639043362","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8603aacecc7c49dcb9635f0cca6a8041","is_oa":true,"landing_page_url":"https://doaj.org/article/8603aacecc7c49dcb9635f0cca6a8041","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 24, p 4976 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/24/4976/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13244976","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13244976","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13244976","pdf_url":"https://www.mdpi.com/2072-4292/13/24/4976/pdf?version=1639043362","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G6739536770","display_name":null,"funder_award_id":"2021R1C1C1012785","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200527363.pdf","grobid_xml":"https://content.openalex.org/works/W4200527363.grobid-xml"},"referenced_works_count":64,"referenced_works":["https://openalex.org/W1679028378","https://openalex.org/W1972085588","https://openalex.org/W1981663377","https://openalex.org/W1985479415","https://openalex.org/W2012771827","https://openalex.org/W2019985735","https://openalex.org/W2039816013","https://openalex.org/W2064675550","https://openalex.org/W2073596977","https://openalex.org/W2073678259","https://openalex.org/W2086222328","https://openalex.org/W2093439880","https://openalex.org/W2096750778","https://openalex.org/W2100184191","https://openalex.org/W2138716039","https://openalex.org/W2147611675","https://openalex.org/W2150496079","https://openalex.org/W2151478249","https://openalex.org/W2156748700","https://openalex.org/W2251849926","https://openalex.org/W2314720829","https://openalex.org/W2592541999","https://openalex.org/W2595433512","https://openalex.org/W2624972355","https://openalex.org/W2773182740","https://openalex.org/W2778580105","https://openalex.org/W2785822431","https://openalex.org/W2789707096","https://openalex.org/W2789876780","https://openalex.org/W2911964244","https://openalex.org/W2913963116","https://openalex.org/W2973103848","https://openalex.org/W2975926778","https://openalex.org/W2989691494","https://openalex.org/W2995687014","https://openalex.org/W3010269417","https://openalex.org/W3014541478","https://openalex.org/W3022643510","https://openalex.org/W3024022789","https://openalex.org/W3027142364","https://openalex.org/W3040739689","https://openalex.org/W3106826746","https://openalex.org/W3107950019","https://openalex.org/W3109987391","https://openalex.org/W3110880854","https://openalex.org/W3115278895","https://openalex.org/W3121640303","https://openalex.org/W3132798945","https://openalex.org/W3135851475","https://openalex.org/W3137743298","https://openalex.org/W3138055694","https://openalex.org/W3138219035","https://openalex.org/W3145548377","https://openalex.org/W3158320618","https://openalex.org/W3158512546","https://openalex.org/W3186511153","https://openalex.org/W3188336259","https://openalex.org/W3193808826","https://openalex.org/W4239510810","https://openalex.org/W4256271404","https://openalex.org/W4295312788","https://openalex.org/W6698971750","https://openalex.org/W6766978945","https://openalex.org/W6787746506"],"related_works":["https://openalex.org/W2133615664","https://openalex.org/W2163606831","https://openalex.org/W2036930708","https://openalex.org/W4296741931","https://openalex.org/W4379054598","https://openalex.org/W2992337937","https://openalex.org/W2890767331","https://openalex.org/W2093943074","https://openalex.org/W2885576795","https://openalex.org/W2789330073"],"abstract_inverted_index":{"Environmental":[0],"monitoring":[1],"using":[2,38],"satellite":[3],"remote":[4],"sensing":[5],"is":[6,93,119,160,261],"challenging":[7],"because":[8],"of":[9,110,137,158,191,200,207,214,257],"data":[10],"gaps":[11],"in":[12,15,94,121,215],"eddy-covariance":[13],"(EC)-based":[14],"situ":[16,216],"flux":[17,28],"tower":[18],"observations.":[19],"In":[20,114],"this":[21],"study,":[22],"we":[23,70],"obtain":[24],"the":[25,89,108,111,116,122,156,172,183,188,193,231,242,250,255,258],"latent":[26],"heat":[27],"(LE)":[29],"from":[30,149,241],"an":[31,95],"EC":[32],"station":[33],"and":[34,48,55,65,69,74,84,139,164,178,202,254],"perform":[35],"gap":[36],"filling":[37],"two":[39,56],"deep":[40],"learning":[41,58],"methods":[42],"(two-dimensional":[43],"convolutional":[44],"neural":[45,53],"network":[46],"(CNN)":[47],"long":[49],"short-term":[50],"memory":[51],"(LSTM)":[52],"networks)":[54],"machine":[57,63],"(ML)":[59],"models":[60,260],"(support":[61],"vector":[62],"(SVM),":[64],"random":[66],"forest":[67],"(RF)),":[68],"investigate":[71],"their":[72],"accuracies":[73],"uncertainties.":[75],"The":[76,175,225],"average":[77],"model":[78,117],"performance":[79,118],"based":[80],"on":[81],"~25":[82],"input":[83],"hysteresis":[85,251],"combinations":[86],"show":[87],"that":[88,155,182,230],"mean":[90],"absolute":[91],"error":[92],"acceptable":[96],"range":[97],"(34.9":[98],"to":[99,151,212],"38.5":[100],"Wm\u22122),":[101],"which":[102,142,209],"indicates":[103,154],"a":[104,134,197,203,246],"marginal":[105],"difference":[106],"among":[107,165,171],"performances":[109,170],"four":[112,173],"models.":[113,174,224],"fact,":[115],"ranked":[120],"following":[123],"order:":[124],"SVM":[125],">":[126,128,130],"CNN":[127,186,232],"RF":[129],"LSTM.":[131],"We":[132],"conduct":[133],"robust":[135],"analysis":[136,177,228],"variance":[138],"post-hoc":[140],"tests,":[141],"yielded":[143],"statistically":[144],"insignificant":[145],"results":[146],"(p-value":[147],"ranging":[148],"0.28":[150],"0.76).":[152],"This":[153],"distribution":[157],"means":[159],"equal":[161],"within":[162],"groups":[163],"pairs,":[166],"thereby":[167,218],"implying":[168],"similar":[169,211],"time-series":[176],"Taylor":[179],"diagram":[180],"indicate":[181],"improved":[184],"two-dimensional":[185],"captures":[187],"temporal":[189],"trend":[190],"LE":[192],"best,":[194],"i.e.,":[195],"with":[196],"Pearson\u2019s":[198],"correlation":[199],">0.87":[201],"normalized":[204],"standard":[205],"deviation":[206],"~0.86,":[208],"are":[210,239],"those":[213],"datasets,":[217],"demonstrating":[219],"its":[220],"superiority":[221],"over":[222],"other":[223],"factor":[226,253],"elimination":[227],"reveals":[229],"performs":[233],"better":[234],"when":[235],"specific":[236],"meteorological":[237],"factors":[238],"removed":[240],"training":[243],"stage.":[244],"Additionally,":[245],"strong":[247],"coupling":[248],"between":[249],"time":[252],"accuracy":[256],"ML":[259],"observed.":[262]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}