{"id":"https://openalex.org/W3096665883","doi":"https://doi.org/10.3390/rs12213609","title":"Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods","display_name":"Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods","publication_year":2020,"publication_date":"2020-11-03","ids":{"openalex":"https://openalex.org/W3096665883","doi":"https://doi.org/10.3390/rs12213609","mag":"3096665883"},"language":"en","primary_location":{"id":"doi:10.3390/rs12213609","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213609","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3609/pdf?version=1604477658","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/2072-4292/12/21/3609/pdf?version=1604477658","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019248350","display_name":"Xinchuan Li","orcid":"https://orcid.org/0000-0001-7006-2782"},"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/I4210134482","display_name":"Nanjing Institute of Geography and Limnology","ror":"https://ror.org/03k6r8t20","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210134482"]},{"id":"https://openalex.org/I4210147117","display_name":"Huaiyin Normal University","ror":"https://ror.org/03xvggv44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210147117"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinchuan Li","raw_affiliation_strings":["Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China","School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China","institution_ids":["https://openalex.org/I4210134482","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","institution_ids":["https://openalex.org/I4210147117"]},{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China","institution_ids":["https://openalex.org/I4210147117"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645911","display_name":"Juhua Luo","orcid":"https://orcid.org/0000-0002-4615-6006"},"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/I4210134482","display_name":"Nanjing Institute of Geography and Limnology","ror":"https://ror.org/03k6r8t20","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210134482"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Juhua Luo","raw_affiliation_strings":["Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China","institution_ids":["https://openalex.org/I4210134482","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090269831","display_name":"Xiuliang Jin","orcid":"https://orcid.org/0000-0003-2720-6247"},"institutions":[{"id":"https://openalex.org/I4210138501","display_name":"Chinese Academy of Agricultural Sciences","ror":"https://ror.org/0313jb750","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210158190","display_name":"Institute of Crop Sciences","ror":"https://ror.org/051abs833","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987","https://openalex.org/I4210158190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuliang Jin","raw_affiliation_strings":["Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"],"raw_orcid":"https://orcid.org/0000-0003-2720-6247","affiliations":[{"raw_affiliation_string":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China","institution_ids":["https://openalex.org/I4210158190","https://openalex.org/I4210138501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100932185","display_name":"Qiaoning He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147117","display_name":"Huaiyin Normal University","ror":"https://ror.org/03xvggv44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210147117"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaoning He","raw_affiliation_strings":["School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","institution_ids":["https://openalex.org/I4210147117"]},{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China","institution_ids":["https://openalex.org/I4210147117"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100770856","display_name":"Yun Niu","orcid":"https://orcid.org/0000-0002-6841-3659"},"institutions":[{"id":"https://openalex.org/I4210147117","display_name":"Huaiyin Normal University","ror":"https://ror.org/03xvggv44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210147117"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Niu","raw_affiliation_strings":["School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China","institution_ids":["https://openalex.org/I4210147117"]},{"raw_affiliation_string":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China","institution_ids":["https://openalex.org/I4210147117"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100645911"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210134482"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.6563,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":{"value":0.81962336,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"12","issue":"21","first_page":"3609","last_page":"3609"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9993000030517578,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9993000030517578,"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/T10889","display_name":"Soil erosion and sediment transport","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10716","display_name":"Soil and Unsaturated Flow","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/topographic-wetness-index","display_name":"Topographic Wetness Index","score":0.8438370227813721},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6972994208335876},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5206791162490845},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5147528648376465},{"id":"https://openalex.org/keywords/soil-science","display_name":"Soil science","score":0.5098058581352234},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.502605676651001},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4849086403846741},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4786623418331146},{"id":"https://openalex.org/keywords/digital-soil-mapping","display_name":"Digital soil mapping","score":0.4727902114391327},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4482734203338623},{"id":"https://openalex.org/keywords/soil-map","display_name":"Soil map","score":0.44663211703300476},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.44566407799720764},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.42436400055885315},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.39357370138168335},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3677466809749603},{"id":"https://openalex.org/keywords/digital-elevation-model","display_name":"Digital elevation model","score":0.3638903498649597},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3423718214035034},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3080117702484131},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2649611830711365},{"id":"https://openalex.org/keywords/soil-water","display_name":"Soil water","score":0.16260391473770142},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12584006786346436}],"concepts":[{"id":"https://openalex.org/C2776898743","wikidata":"https://www.wikidata.org/wiki/Q18353408","display_name":"Topographic Wetness Index","level":3,"score":0.8438370227813721},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6972994208335876},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5206791162490845},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5147528648376465},{"id":"https://openalex.org/C159390177","wikidata":"https://www.wikidata.org/wiki/Q9161265","display_name":"Soil science","level":1,"score":0.5098058581352234},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.502605676651001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4849086403846741},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4786623418331146},{"id":"https://openalex.org/C104471815","wikidata":"https://www.wikidata.org/wiki/Q5276164","display_name":"Digital soil mapping","level":4,"score":0.4727902114391327},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4482734203338623},{"id":"https://openalex.org/C71864017","wikidata":"https://www.wikidata.org/wiki/Q889561","display_name":"Soil map","level":3,"score":0.44663211703300476},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.44566407799720764},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.42436400055885315},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.39357370138168335},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3677466809749603},{"id":"https://openalex.org/C181843262","wikidata":"https://www.wikidata.org/wiki/Q640492","display_name":"Digital elevation model","level":2,"score":0.3638903498649597},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3423718214035034},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3080117702484131},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2649611830711365},{"id":"https://openalex.org/C159750122","wikidata":"https://www.wikidata.org/wiki/Q96621023","display_name":"Soil water","level":2,"score":0.16260391473770142},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12584006786346436},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs12213609","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213609","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3609/pdf?version=1604477658","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:96cd909fbf994a5292d6afbff6841aa4","is_oa":true,"landing_page_url":"https://doaj.org/article/96cd909fbf994a5292d6afbff6841aa4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"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 12, Iss 21, p 3609 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/12/21/3609/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs12213609","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 12; Issue 21; Pages: 3609","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs12213609","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213609","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3609/pdf?version=1604477658","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":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.7200000286102295}],"awards":[{"id":"https://openalex.org/G4254779748","display_name":null,"funder_award_id":"2018M642349","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G4721244966","display_name":null,"funder_award_id":"41801075","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/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3096665883.pdf","grobid_xml":"https://content.openalex.org/works/W3096665883.grobid-xml"},"referenced_works_count":76,"referenced_works":["https://openalex.org/W1058055990","https://openalex.org/W1605688901","https://openalex.org/W1678356000","https://openalex.org/W1977373856","https://openalex.org/W1992626825","https://openalex.org/W2010081281","https://openalex.org/W2012118327","https://openalex.org/W2014047303","https://openalex.org/W2016641430","https://openalex.org/W2019179722","https://openalex.org/W2028040032","https://openalex.org/W2034729546","https://openalex.org/W2054325787","https://openalex.org/W2059379980","https://openalex.org/W2063676146","https://openalex.org/W2068988202","https://openalex.org/W2070493638","https://openalex.org/W2078209294","https://openalex.org/W2081027696","https://openalex.org/W2085049715","https://openalex.org/W2097360283","https://openalex.org/W2100805904","https://openalex.org/W2105673687","https://openalex.org/W2112411699","https://openalex.org/W2119119065","https://openalex.org/W2161270333","https://openalex.org/W2171642129","https://openalex.org/W2274267064","https://openalex.org/W2295598076","https://openalex.org/W2424724326","https://openalex.org/W2488674625","https://openalex.org/W2527323007","https://openalex.org/W2559213523","https://openalex.org/W2560136348","https://openalex.org/W2578285607","https://openalex.org/W2597412011","https://openalex.org/W2611504252","https://openalex.org/W2740207456","https://openalex.org/W2752549100","https://openalex.org/W2766843720","https://openalex.org/W2769696834","https://openalex.org/W2773188111","https://openalex.org/W2773348893","https://openalex.org/W2778546593","https://openalex.org/W2789758093","https://openalex.org/W2790860706","https://openalex.org/W2794387191","https://openalex.org/W2900600890","https://openalex.org/W2905155550","https://openalex.org/W2908031888","https://openalex.org/W2911713721","https://openalex.org/W2911964244","https://openalex.org/W2912437039","https://openalex.org/W2923988252","https://openalex.org/W2924698935","https://openalex.org/W2947342498","https://openalex.org/W2952516441","https://openalex.org/W2953121833","https://openalex.org/W2953150186","https://openalex.org/W2990294132","https://openalex.org/W2993084136","https://openalex.org/W2995150843","https://openalex.org/W2997494414","https://openalex.org/W2998049178","https://openalex.org/W2998503064","https://openalex.org/W3000134814","https://openalex.org/W3005132041","https://openalex.org/W3005528129","https://openalex.org/W3015083507","https://openalex.org/W3041874307","https://openalex.org/W3043155501","https://openalex.org/W3102103361","https://openalex.org/W4294541781","https://openalex.org/W6655359811","https://openalex.org/W6677494780","https://openalex.org/W6869839083"],"related_works":["https://openalex.org/W2549937139","https://openalex.org/W2919487343","https://openalex.org/W1969748798","https://openalex.org/W4220757256","https://openalex.org/W1971773769","https://openalex.org/W2048504890","https://openalex.org/W2026926115","https://openalex.org/W2519024274","https://openalex.org/W2054155874","https://openalex.org/W1986420329"],"abstract_inverted_index":{"Spatially":[0],"continuous":[1],"soil":[2,31,41,49,81,116,170,193,240,253,261],"thickness":[3,50,117,171,194],"data":[4],"at":[5],"large":[6],"scales":[7],"are":[8,14,57],"usually":[9],"not":[10],"readily":[11],"available":[12],"and":[13,17,35,51,67,100,113,135,139,147,154,181,207],"often":[15],"difficult":[16],"expensive":[18],"to":[19,33,74,110,149],"acquire.":[20],"Various":[21],"machine":[22,54,86],"learning":[23,55,87],"algorithms":[24,56,88],"have":[25],"become":[26],"very":[27,166],"popular":[28],"in":[29,60,120,192],"digital":[30],"mapping":[32,252],"predict":[34,112],"map":[36,119],"the":[37,44,76,126,151,187,200,204,211,219,230,237],"spatial":[38],"distribution":[39,118],"of":[40,48,169,228,236,244],"properties.":[42,262],"Identifying":[43],"controlling":[45],"environmental":[46,70,190],"variables":[47,71,78,191],"selecting":[52],"suitable":[53],"vitally":[58],"important":[59,167],"modeling.":[61,172,195],"In":[62,124,226],"this":[63],"study,":[64],"11":[65],"quantitative":[66],"four":[68],"qualitative":[69],"were":[72,105,145,186],"selected":[73],"explore":[75],"main":[77],"that":[79,161,199],"affect":[80],"thickness.":[82,241],"Four":[83],"commonly":[84],"used":[85],"(multiple":[89],"linear":[90],"regression":[91,95,142],"(MLR),":[92],"support":[93],"vector":[94],"(SVR),":[96],"random":[97],"forest":[98],"(RF),":[99],"extreme":[101],"gradient":[102],"boosting":[103],"(XGBoost)":[104],"evaluated":[106],"as":[107],"individual":[108],"models":[109,130,214],"separately":[111],"obtain":[114],"a":[115,165],"Henan":[121],"Province,":[122],"China.":[123],"addition,":[125],"two":[127,212],"stacking":[128,213,232],"ensemble":[129],"using":[131,224],"least":[132],"absolute":[133],"shrinkage":[134],"selection":[136,163],"operator":[137],"(LASSO)":[138],"generalized":[140],"boosted":[141],"model":[143,202],"(GBM)":[144],"tested":[146],"applied":[148],"build":[150],"most":[152,188],"reliable":[153],"accurate":[155],"estimation":[156],"model.":[157],"The":[158,242],"results":[159,197,243],"showed":[160,198],"variable":[162],"was":[164],"part":[168],"Topographic":[173],"wetness":[174],"index":[175,184],"(TWI),":[176],"slope,":[177],"elevation,":[178],"land":[179],"use":[180,258],"enhanced":[182],"vegetation":[183],"(EVI)":[185],"influential":[189],"Comparative":[196],"XGBoost":[201],"outperformed":[203],"MLR,":[205],"RF":[206],"SVR":[208],"models.":[209],"Importantly,":[210],"achieved":[215],"higher":[216],"performance":[217],"than":[218],"single":[220],"model,":[221],"especially":[222],"when":[223],"GBM.":[225],"terms":[227],"accuracy,":[229],"proposed":[231],"method":[233],"explained":[234],"64.0%":[235],"variation":[238],"for":[239,251,257],"our":[245],"study":[246],"provide":[247],"useful":[248],"alternative":[249],"approaches":[250],"thickness,":[254],"with":[255,259],"potential":[256],"other":[260]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}