{"id":"https://openalex.org/W4387829236","doi":"https://doi.org/10.1109/igarss52108.2023.10282396","title":"Above Ground Biomass Estimation By Multi-Source Data Based On Interpretable DNN Model","display_name":"Above Ground Biomass Estimation By Multi-Source Data Based On Interpretable DNN Model","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387829236","doi":"https://doi.org/10.1109/igarss52108.2023.10282396"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282396","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_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5034676834","display_name":"Yaxuan Xing","orcid":"https://orcid.org/0000-0001-7769-3069"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaxuan Xing","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433"],"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100669010","display_name":"Feng Wang","orcid":"https://orcid.org/0000-0002-2378-9126"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Wang","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433"],"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071461704","display_name":"Feng Xu","orcid":"https://orcid.org/0000-0002-7015-1467"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Xu","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433"],"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology,Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.111,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.615625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":67,"max":78},"biblio":{"volume":null,"issue":null,"first_page":"1894","last_page":"1897"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9985,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9985,"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":0.9973,"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/T10555","display_name":"Fire effects on ecosystems","score":0.9646,"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/data-source","display_name":"Data source","score":0.48211598}],"concepts":[{"id":"https://openalex.org/C115540264","wikidata":"https://www.wikidata.org/wiki/Q2945560","display_name":"Biomass (ecology)","level":2,"score":0.6619071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.599821},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.56629986},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.54313046},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5372453},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.49484748},{"id":"https://openalex.org/C2983685735","wikidata":"https://www.wikidata.org/wiki/Q5227355","display_name":"Data source","level":2,"score":0.48211598},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.42505908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41546687},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41150182},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39866683},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3932624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39315316},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.16236886},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09448612},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07554749},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282396","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_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.62,"display_name":"Life on land"}],"grants":[{"funder":"https://openalex.org/F4320309612","funder_display_name":"Natural Science Foundation of Shanghai","award_id":null},{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":7,"referenced_works":["https://openalex.org/W3032385319","https://openalex.org/W4286206415","https://openalex.org/W4296849391","https://openalex.org/W4307815475","https://openalex.org/W4312291004","https://openalex.org/W4312367286","https://openalex.org/W4312951699"],"related_works":["https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W3207760230","https://openalex.org/W3161989282","https://openalex.org/W2536018345","https://openalex.org/W2358353312","https://openalex.org/W2296488620","https://openalex.org/W17155033","https://openalex.org/W1590307681","https://openalex.org/W1496222301"],"abstract_inverted_index":{"Above":[0],"Ground":[1],"Biomass":[2],"(AGB)":[3],"estimation":[4,110],"is":[5,68,78],"a":[6,74,112],"basis":[7],"for":[8,140],"rational":[9],"utilization":[10],"of":[11,21,38,41,50,84,94,114,116],"natural":[12],"resources":[13],"and":[14,73,124,138],"ecological":[15],"succession":[16],"process.":[17],"Recently,":[18],"multiple":[19],"sources":[20],"remote":[22],"sensing":[23],"data":[24,57],"have":[25],"been":[26],"used":[27],"to":[28,45,80],"estimate":[29],"AGB":[30,59,109],"at":[31],"high":[32],"spatial":[33],"resolution,":[34],"overcoming":[35],"the":[36,48,82,92,95,102,120,130],"limitations":[37],"each":[39],"type":[40],"data.":[42],"In":[43],"order":[44],"fully":[46],"exploit":[47],"potential":[49],"deep":[51],"learning":[52,128],"models":[53],"based":[54],"on":[55],"multi-source":[56,88],"in":[58,87],"estimation,":[60,143],"an":[61],"end-to-end":[62],"Deep":[63],"Neural":[64],"Networks":[65],"(DNN)":[66],"model":[67,105,133],"developed":[69],"using":[70],"Sentinel-1/2":[71],"data,":[72,89],"learnable":[75],"weight":[76],"matrix":[77],"designed":[79,103],"tune":[81],"contribution":[83],"different":[85],"predictors":[86],"thus":[90],"improving":[91],"performance":[93],"model.":[96],"The":[97],"experimental":[98],"results":[99],"show":[100],"that":[101],"DNN":[104,132],"can":[106],"achieve":[107],"accurate":[108],"with":[111,119],"coefficient":[113],"determination":[115],"0.7314.":[117],"Compared":[118],"widely":[121],"employed":[122],"XGBoost":[123],"Random":[125],"Forest":[126],"machine":[127],"models,":[129],"proposed":[131],"has":[134],"improved":[135],"by":[136],"6.29%":[137],"5.07%":[139],"grassland":[141],"biomass":[142],"respectively.":[144]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4387829236","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-23T03:46:01.805188","created_date":"2023-10-21"}