{"id":"https://openalex.org/W2982431739","doi":"https://doi.org/10.3390/rs11212520","title":"Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data","display_name":"Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data","publication_year":2019,"publication_date":"2019-10-28","ids":{"openalex":"https://openalex.org/W2982431739","doi":"https://doi.org/10.3390/rs11212520","mag":"2982431739"},"language":"en","primary_location":{"id":"doi:10.3390/rs11212520","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11212520","pdf_url":"https://www.mdpi.com/2072-4292/11/21/2520/pdf","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/2072-4292/11/21/2520/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010975608","display_name":"Hua Wu","orcid":"https://orcid.org/0000-0002-5982-8422"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210141657","display_name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application","ror":"https://ror.org/045yewh40","country_code":"CN","type":"facility","lineage":["https://openalex.org/I152031979","https://openalex.org/I4210141657"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"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":"Hua Wu","raw_affiliation_strings":["Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China","State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China","institution_ids":["https://openalex.org/I4210141657"]},{"raw_affiliation_string":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067130524","display_name":"Wangmin Ying","orcid":"https://orcid.org/0000-0002-6707-3007"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"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":false,"raw_author_name":"Wangmin Ying","raw_affiliation_strings":["State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010975608"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210141657","https://openalex.org/I4210160793","https://openalex.org/I4210165038"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.4875,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.79911874,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":"21","first_page":"2520","last_page":"2520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.9994999766349792,"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":0.9987000226974487,"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/T10347","display_name":"Atmospheric aerosols and clouds","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.697685718536377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6150873899459839},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5158197283744812},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5023157596588135},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.473147988319397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46608254313468933},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46576523780822754},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.44038230180740356},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.42443761229515076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15768441557884216},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09869697690010071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697685718536377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6150873899459839},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5158197283744812},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5023157596588135},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.473147988319397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46608254313468933},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46576523780822754},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.44038230180740356},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.42443761229515076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15768441557884216},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09869697690010071},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11212520","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11212520","pdf_url":"https://www.mdpi.com/2072-4292/11/21/2520/pdf","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:81c6da7e925a4534b9ec165f93106a2a","is_oa":true,"landing_page_url":"https://doaj.org/article/81c6da7e925a4534b9ec165f93106a2a","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 11, Iss 21, p 2520 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/21/2520/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11212520","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; Volume 11; Issue 21; Pages: 2520","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11212520","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11212520","pdf_url":"https://www.mdpi.com/2072-4292/11/21/2520/pdf","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":[{"score":0.49000000953674316,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1554407859","display_name":null,"funder_award_id":"No.41871267","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3022034049","display_name":null,"funder_award_id":"2003030","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4737026357","display_name":null,"funder_award_id":"XDA20030302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6037185330","display_name":null,"funder_award_id":"41871267","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6319199028","display_name":null,"funder_award_id":"XDA20030302","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G6800035810","display_name":null,"funder_award_id":"41871267","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982431739.pdf","grobid_xml":"https://content.openalex.org/works/W2982431739.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W1693131706","https://openalex.org/W1809549812","https://openalex.org/W1876667624","https://openalex.org/W1964217023","https://openalex.org/W1967467351","https://openalex.org/W1974832360","https://openalex.org/W1990653740","https://openalex.org/W1991969593","https://openalex.org/W1999056277","https://openalex.org/W2007040778","https://openalex.org/W2019669012","https://openalex.org/W2022625409","https://openalex.org/W2032782884","https://openalex.org/W2036670520","https://openalex.org/W2041756006","https://openalex.org/W2043648244","https://openalex.org/W2044609898","https://openalex.org/W2045510981","https://openalex.org/W2048175578","https://openalex.org/W2059523177","https://openalex.org/W2061399699","https://openalex.org/W2072774269","https://openalex.org/W2079454091","https://openalex.org/W2089300710","https://openalex.org/W2093589181","https://openalex.org/W2096012292","https://openalex.org/W2099042763","https://openalex.org/W2113453022","https://openalex.org/W2116853641","https://openalex.org/W2144630956","https://openalex.org/W2169968179","https://openalex.org/W2198568516","https://openalex.org/W2294784510","https://openalex.org/W2296185332","https://openalex.org/W2304221415","https://openalex.org/W2339926260","https://openalex.org/W2537296354","https://openalex.org/W2588164752","https://openalex.org/W2597128531","https://openalex.org/W2607336919","https://openalex.org/W2785846558","https://openalex.org/W2788336062","https://openalex.org/W2791827521","https://openalex.org/W2800635075","https://openalex.org/W2891975230","https://openalex.org/W2901899013","https://openalex.org/W2914802281","https://openalex.org/W2914908865","https://openalex.org/W2919424886","https://openalex.org/W2933766109","https://openalex.org/W6639506288","https://openalex.org/W6681585587","https://openalex.org/W6761536917"],"related_works":["https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3193043704","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052"],"abstract_inverted_index":{"Net":[0],"surface":[1,148,212,225,284],"shortwave":[2],"radiation":[3],"(NSSR)":[4],"is":[5,87,180,319],"one":[6],"of":[7,34,43,76,84,140,166,187,195,207,217,260,265,272,280,299],"the":[8,32,41,56,61,67,74,82,164,185,215,231,238,261,270,278,338,354],"most":[9],"important":[10],"fundamental":[11],"parameters":[12],"in":[13,31,66,81,155,210,222,313,353],"various":[14,170,211],"land":[15,53,171],"processes.":[16],"Benefiting":[17],"from":[18,94,244],"its":[19,249],"efficient":[20],"nonlinear":[21],"fitting":[22],"ability,":[23],"machine":[24,46,68,77,106,219,342],"learning":[25,47,78,107,220,343],"algorithms":[26,48,79,344],"have":[27,39,308],"a":[28,91,204,223,297,309,320,336],"great":[29],"potential":[30],"retrieval":[33,83,267],"NSSR.":[35],"However,":[36,230],"few":[37],"studies":[38],"explored":[40],"level":[42],"accuracy":[44,312],"that":[45,269,296],"can":[49,275],"reach":[50],"for":[51],"different":[52,135],"covers":[54],"on":[55],"worldwide":[57],"scale":[58],"and":[59,98,117,150,184,214,253,283,286,302,350],"what":[60],"optimal":[62,130,239],"independent":[63,136,263],"variables":[64,264],"are":[65,190,289],"learning-based":[69],"NSSR":[70,128,168,208,243,266,281,314,325,339],"model.":[71],"To":[72],"guide":[73],"use":[75],"correctly":[80],"NSSR,":[85],"it":[86],"necessary":[88],"to":[89,125,162,241,248,322,327],"give":[90],"comprehensive":[92],"analysis":[93,259],"algorithm":[95],"complexity,":[96],"accuracy,":[97],"other":[99,328],"aspects.":[100],"In":[101,335],"this":[102],"study,":[103],"three":[104,218],"classic":[105],"algorithms,":[108],"including":[109,138],"Random":[110],"Forest":[111],"(RF),":[112],"Artificial":[113],"Neural":[114],"Network":[115],"(ANN),":[116],"Support":[118],"Vector":[119],"Regression":[120],"(SVR),":[121],"were":[122,160,198],"built":[123],"well":[124],"estimate":[126],"instantaneous":[127],"with":[129,332,341],"hyperparameters":[131],"by":[132],"elaborately":[133],"selecting":[134],"variables,":[137],"top":[139],"atmosphere":[141,151,287],"(TOA)":[142],"channel":[143],"spectral":[144],"reflectance,":[145],"geographic":[146,273,300],"parameters,":[147],"information,":[149],"conditions.":[152],"Global":[153],"FLUXNET":[154],"situ":[156],"measurements":[157],"throughout":[158],"2014":[159],"used":[161],"validate":[163],"accuracies":[165],"retrieved":[167],"over":[169],"cover":[172],"types.":[173],"The":[174,257],"root":[175],"mean":[176],"square":[177],"error":[178,188,197,279],"(RMSE)":[179],"below":[181],"55":[182],"W/m2,":[183],"distributions":[186],"histogram":[189],"also":[191,228,294],"similar.":[192],"Approximately":[193],"50%":[194],"absolute":[196],"within":[199],"25":[200],"W/m2.":[201],"There":[202],"was":[203,227,293],"performance":[205,216],"difference":[206],"estimations":[209],"types,":[213],"methods":[221],"specific":[224],"type":[226],"different.":[229],"RF":[232],"method":[233,352],"may":[234],"be":[235,346],"considered":[236],"as":[237],"methodology":[240],"retrieve":[242],"MODIS":[245],"data,":[246],"owing":[247],"relatively":[250],"better":[251],"precision":[252],"concise":[254],"hyperparameter-tuned":[255],"process.":[256],"importance":[258],"proposed":[262],"shows":[268],"introduction":[271],"information":[274,285,288,301],"effectively":[276],"reduce":[277],"retrieval,":[282,315],"not":[290],"necessary.":[291],"It":[292],"found":[295],"combination":[298],"blue":[303],"band":[304],"TOA":[305],"reflectance":[306],"already":[307],"pretty":[310],"good":[311],"which":[316],"implies":[317],"there":[318],"possibility":[321],"transfer":[323],"our":[324],"model":[326,340],"satellite":[329],"sensors,":[330],"especially":[331],"insufficient":[333],"channels.":[334],"word,":[337],"would":[345],"an":[347],"efficient,":[348],"concise,":[349],"general":[351],"future.":[355]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
