{"id":"https://openalex.org/W4389443153","doi":"https://doi.org/10.3390/rs15245653","title":"Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data","display_name":"Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data","publication_year":2023,"publication_date":"2023-12-07","ids":{"openalex":"https://openalex.org/W4389443153","doi":"https://doi.org/10.3390/rs15245653"},"language":"en","primary_location":{"id":"doi:10.3390/rs15245653","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245653","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5653/pdf?version=1701998541","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/2072-4292/15/24/5653/pdf?version=1701998541","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhao Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Chen","raw_affiliation_strings":["Engineering Research Center for Forestry\u2013Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China","School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center for Forestry\u2013Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041003852","display_name":"Zhibin Sun","orcid":"https://orcid.org/0000-0002-8054-490X"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhibin Sun","raw_affiliation_strings":["Engineering Research Center for Forestry\u2013Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China","School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center for Forestry\u2013Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062119142","display_name":"Huaiqing Zhang","orcid":"https://orcid.org/0000-0003-3874-5326"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaiqing Zhang","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062119142","display_name":"Huaiqing Zhang","orcid":"https://orcid.org/0000-0003-3874-5326"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huaiqing Zhang","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090550416","display_name":"Huacong Zhang","orcid":"https://orcid.org/0009-0006-1047-3196"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huacong Zhang","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090550416","display_name":"Huacong Zhang","orcid":"https://orcid.org/0009-0006-1047-3196"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huacong Zhang","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011955479","display_name":"Hanqing Qiu","orcid":"https://orcid.org/0000-0003-4800-1865"},"institutions":[{"id":"https://openalex.org/I4210114891","display_name":"Institute of Forest Resource Information Techniques","ror":"https://ror.org/01h5d6x15","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114891","https://openalex.org/I4210128615","https://openalex.org/I4210134523"]},{"id":"https://openalex.org/I4210128615","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210128615","https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanqing Qiu","raw_affiliation_strings":["Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"],"affiliations":[{"raw_affiliation_string":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China","institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5062119142","https://openalex.org/A5090550416"],"corresponding_institution_ids":["https://openalex.org/I4210114891","https://openalex.org/I4210128615"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.4056,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.88340166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"15","issue":"24","first_page":"5653","last_page":"5653"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9997000098228455,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9970999956130981,"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/remote-sensing","display_name":"Remote sensing","score":0.6932472586631775},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6496461033821106},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.6371814012527466},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.617883563041687},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.58189857006073},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5438228249549866},{"id":"https://openalex.org/keywords/biomass","display_name":"Biomass (ecology)","score":0.5255497694015503},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4284363389015198},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4141482710838318},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.4131309688091278},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3825373947620392},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2246854603290558},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22050586342811584},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.13313505053520203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13245514035224915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08870282769203186}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6932472586631775},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6496461033821106},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.6371814012527466},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.617883563041687},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.58189857006073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5438228249549866},{"id":"https://openalex.org/C115540264","wikidata":"https://www.wikidata.org/wiki/Q2945560","display_name":"Biomass (ecology)","level":2,"score":0.5255497694015503},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4284363389015198},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4141482710838318},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.4131309688091278},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3825373947620392},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2246854603290558},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22050586342811584},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.13313505053520203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13245514035224915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08870282769203186},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs15245653","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245653","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5653/pdf?version=1701998541","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:bf5058856e4448df88bda6e22adf1933","is_oa":true,"landing_page_url":"https://doaj.org/article/bf5058856e4448df88bda6e22adf1933","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 24, p 5653 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs15245653","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245653","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5653/pdf?version=1701998541","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":0.7599999904632568,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2935639811","display_name":null,"funder_award_id":"2022YFE0128100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6258415954","display_name":null,"funder_award_id":"Chinese","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6596033285","display_name":null,"funder_award_id":"32271877","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"},{"id":"https://openalex.org/F4320323156","display_name":"Chinese Academy of Forestry","ror":"https://ror.org/0360dkv71"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389443153.pdf"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W190437827","https://openalex.org/W1177863458","https://openalex.org/W1964357740","https://openalex.org/W1964759162","https://openalex.org/W1974048947","https://openalex.org/W1999634002","https://openalex.org/W2007342648","https://openalex.org/W2020520344","https://openalex.org/W2040099039","https://openalex.org/W2051913432","https://openalex.org/W2069858355","https://openalex.org/W2079196379","https://openalex.org/W2087346592","https://openalex.org/W2101888226","https://openalex.org/W2108972318","https://openalex.org/W2113249705","https://openalex.org/W2113584252","https://openalex.org/W2118795150","https://openalex.org/W2126773028","https://openalex.org/W2158982427","https://openalex.org/W2161424977","https://openalex.org/W2162267805","https://openalex.org/W2166298943","https://openalex.org/W2247062920","https://openalex.org/W2252301886","https://openalex.org/W2304476603","https://openalex.org/W2414762761","https://openalex.org/W2416310637","https://openalex.org/W2487770199","https://openalex.org/W2756157229","https://openalex.org/W2765453548","https://openalex.org/W2804171184","https://openalex.org/W2889545660","https://openalex.org/W2911322491","https://openalex.org/W2911964244","https://openalex.org/W2928790886","https://openalex.org/W2966381923","https://openalex.org/W2985495555","https://openalex.org/W2992769856","https://openalex.org/W3003390797","https://openalex.org/W3028312615","https://openalex.org/W3035803741","https://openalex.org/W3084637795","https://openalex.org/W3093473493","https://openalex.org/W3111631896","https://openalex.org/W3156622514","https://openalex.org/W3163134422","https://openalex.org/W3210911897","https://openalex.org/W4200197408","https://openalex.org/W4214639322","https://openalex.org/W4286206415","https://openalex.org/W4293206866","https://openalex.org/W4294018497","https://openalex.org/W4296849391","https://openalex.org/W4308122130","https://openalex.org/W4309456089","https://openalex.org/W4310790983","https://openalex.org/W4312750672","https://openalex.org/W4313293079","https://openalex.org/W4316652579","https://openalex.org/W4376106476","https://openalex.org/W4384522285","https://openalex.org/W4385522612","https://openalex.org/W4385636574","https://openalex.org/W4386041438","https://openalex.org/W4386838010","https://openalex.org/W4387138527","https://openalex.org/W6680532697","https://openalex.org/W6787499719","https://openalex.org/W7071885155"],"related_works":["https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W1868434454","https://openalex.org/W4366985237","https://openalex.org/W2810569973","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645"],"abstract_inverted_index":{"Accurate":[0],"forest":[1,9,20,144,162,193,317],"biomass":[2,58,307],"estimation":[3,59,305],"serves":[4],"as":[5,51],"the":[6,44,52,117,122,154,178,203,207,217,242,286,301],"foundation":[7],"of":[8,19,43,210,303],"management":[10],"and":[11,23,33,39,75,95,142,161,172,212,232,268],"holds":[12],"critical":[13],"significance":[14],"for":[15,315],"a":[16,41,57,111,312],"comprehensive":[17],"understanding":[18],"carbon":[21],"storage":[22],"balance.":[24],"This":[25,309],"study":[26,53],"aimed":[27],"to":[28,55,116,152,196],"integrate":[29],"Landsat":[30,84],"8":[31,85,167],"OLI":[32],"Sentinel-1A":[34],"SAR":[35,169],"satellite":[36],"image":[37],"data":[38,160],"selected":[40],"portion":[42],"Shanxia":[45],"Experimental":[46],"Forest":[47],"in":[48,306],"Jiangxi":[49],"Province":[50],"area":[54],"establish":[56,153],"model":[60,108,189,205,244,290],"by":[61,292],"screening":[62],"influencing":[63],"factors.":[64],"Firstly,":[65],"we":[66,88],"extracted":[67],"spectral":[68],"information,":[69],"vegetation":[70],"indices,":[71],"principal":[72],"component":[73],"features,":[74],"texture":[76],"features":[77,215],"within":[78],"3":[79],"\u00d7":[80],"3-pixel":[81],"neighborhoods":[82],"from":[83],"OLI.":[86],"Moreover,":[87],"incorporated":[89],"Sentinel-1\u2019s":[90],"VV":[91],"(vertical":[92,97],"transmit\u2013vertical":[93],"receive)":[94,99],"VH":[96],"transmit\u2013horizontal":[98],"polarizations.":[100],"We":[101],"proposed":[102],"an":[103],"ensemble":[104],"AGB":[105,304],"(aboveground":[106],"biomass)":[107],"based":[109],"on":[110],"neural":[112,118,129,288],"network.":[113],"In":[114,284],"addition":[115],"network":[119,130,289],"model,":[120,132],"namely":[121],"tent":[123,293],"mapping":[124,294],"atom":[125,295],"search":[126,296],"optimized":[127],"BP":[128,188,287],"(Tent_ASO_BP)":[131],"partial":[133],"least":[134],"squares":[135],"regression":[136,146],"(PLSR),":[137],"support":[138],"vector":[139],"machine":[140],"(SVR),":[141],"random":[143],"(RF)":[145],"prediction":[147,180],"techniques":[148],"were":[149,175,282],"also":[150],"employed":[151],"relationship":[155],"between":[156],"multisource":[157],"remote":[158],"sensing":[159],"biomass.":[163,194],"Optical":[164],"variables":[165,170],"(Landsat":[166],"OLI),":[168],"(Sentinel-1A),":[171],"their":[173],"combinations":[174],"input":[176,214],"into":[177],"four":[179],"models.":[181],"The":[182],"results":[183],"indicate":[184],"that":[185],"Tent_":[186],"ASO_":[187],"can":[190],"better":[191],"estimate":[192],"Compared":[195],"pure":[197],"optical":[198,211],"or":[199],"single":[200],"microwave":[201,213],"data,":[202],"Tent_ASO_BP":[204],"with":[206],"optimal":[208],"combination":[209],"achieved":[216],"highest":[218],"accuracy.":[219],"Its":[220],"R2":[221],"was":[222,229,237],"0.74,":[223],"root":[224],"mean":[225,233],"square":[226],"error":[227,235],"(RMSE)":[228],"11.54":[230],"Mg/ha,":[231,251,263,276],"absolute":[234],"(MAE)":[236],"9.06":[238],"Mg/ha.":[239],"Following":[240],"this,":[241],"RF":[243],"(R2":[245,257,270],"=":[246,249,253,258,261,265,271,274,278],"0.54,":[247],"RMSE":[248,260,273],"21.33":[250],"MAE":[252,264,277],"17.35":[254],"Mg/ha),":[255,267],"SVR":[256],"0.52,":[259],"17.66":[262],"15.11":[266],"PLSR":[269],"0.50,":[272],"16.52":[275],"12.15":[279],"Mg/ha)":[280],"models":[281],"employed.":[283],"conclusion,":[285],"improved":[291],"optimization":[297],"algorithm":[298],"significantly":[299],"enhanced":[300],"accuracy":[302],"studies.":[308],"will":[310],"provide":[311],"new":[313],"avenue":[314],"large-scale":[316],"resource":[318],"surveys.":[319]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":5}],"updated_date":"2026-04-17T05:58:53.018234","created_date":"2025-10-10T00:00:00"}
